\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 179 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 252 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
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In an effort to reduce IT capital and operational expenditures (OpEx), organizations of all sizes are using clouds to provide the resources required to run their applications. Clouds vary significantly in their specific technologies and implementation, but often provide infrastructure, platform, and software resources as services [2, 3]. Vendor lock-in problem is a highly referenced topic followed by security in the technical and business IT world [4, 5, 6, 7, 8]. Research shows that several companies are already migrating to cloud-based services, but challenges of vendor lock-in is prohibiting a widespread adoption rate across enterprises of different sizes and industry sectors [9, 10, 11]. Simply put, businesses are wary of being tied to particular cloud computing vendors due to lack of competing, compatible product [12]. Due to the ever growing interest in cloud computing services, there is an explicit and constant effort to evaluate the current trends in vendor lock-in and security for such technology. Progress of current research effort in demystifying the complexity of vendor lock-in problem in cloud computing technology is contingent on having a rigorous organization of its knowledge domain and a comprehensive understanding of all the relevant elements and components of this technology and their relationship. Currently, there is a lack of sufficient standardization of cloud computing services [4, 5, 6, 13], and each cloud service vendor uses different technologies, SLAs, protocols, APIs, security protocols, and formats. This further makes interoperability, portability, and security as elusive tasks to accomplish when working with multiple (hybrid cloud) services. Moreover, with the amount of cloud computing services and vendors increasing quickly, the need for a detailed taxonomy framework rises [14]. Such taxonomy should provide a common terminology and baseline information for easy communication and understandability of vendor lock-in risks when comparing the offers to find the right cloud service/vendor of choice. Therefore, the vast amount of cloud computing services and the lack of universal definitions and standards agreement to avoid lock-in lead to the question whether cloud computing services and related challenges can be classified in a taxonomy based on their characteristics to easily compare them. While the work in [15] proposes taxonomy for a quick classification of cloud services making it easier to compare them, however, it does not specifically addresses the challenges of vendor lock-in affecting widespread cloud adoption and migration.
To this end, the main goal of this chapter is to identify, classify, and organize the main risks of vendor lock-in associated to cloud computing migration, helping in the task of underlying the challenges that remain unanswered from the enterprise perspective. This taxonomy demonstrates a dissection of vendor lock-in problem into three main perspectives, and illustrates their classification and associated elements. It makes it very easy to comprehend the cloud computing vendor lock-in field as a whole, correlate, classify, and compare the various existing industry solution proposals, mitigation strategies, and best practices. In turn, this will provide a solid foundation for future research analysis, and a review to assist academia, and a scientific research community to expedite its contributions and insights regarding lock-in avoidance for enterprise cloud migration use cases. Over the years, taxonomy techniques have been used to create models that allow for the classification of concepts within a domain. In this chapter, we apply taxonomy techniques in the cloud computing domain to discuss the many aspects involved with cloud computing lock-in that are important from an enterprise perspective. Aiming at a better understanding of the categories of applications, data, and security controls that could trigger a lock-in situation during cloud migration, this chapter contributes by proposing a detailed taxonomy based on characteristics that are fundamental for enterprise production applications typically associated with the cloud and big data paradigm.
The remainder of this chapter is organized as follows. Section 2 discusses and reviews related work that is relevant to our work. Section 3 presents the current service models of cloud computing and their specific lock-in challenges. Section 4 briefly highlights an ontology of cloud lock-in perspectives which gives a better understanding and definitions of categories that is to be used in the design of our work. Section 5 presents the classification of perspectives used in our proposed taxonomy of cloud lock-in challenges. Section 6 concludes this chapter and recommends future research directions.
Several taxonomies for cloud computing can be found in [16, 17], but most are created from the perspective of vendors that are part of the market landscape and not from the perspective of enterprise IT, the consumers of cloud services, and software. Providing taxonomic information is essential not only for cloud service providers, but also for enterprise firms, and compliance authorities to detect, manage, and control invasive proprietary components. Taxonomy identifies and enumerates the components of cloud computing that are providing basic knowledge underpinning management and implementation of the cloud computing. There is, however, no standard taxonomy, as everyone tries to define cloud computing and its services in their own way. The taxonomy described by the Cloud Computing Use Case Discussion Group [18] is categorized into three views: service developer, service provider, and service consumers. This taxonomy does not cover the potential challenges of vendor lock-in and related security risks. Crandell [19] defines a taxonomy based on cloud service offerings divided into three layers, namely application services (e.g., Salesforce CRM and other SaaS vendors), platform services (e.g., GAE, Moso, and Heroku), and infrastructure services (e.g., Amazon Web Services, Flexiscale). This taxonomy is valuable for any company with an application that runs in a data center or with a hosted provider that does not want to reinvent the wheel or pay a premium. Laird’s [20] cloud vendor taxonomy gives the classifications and vendors with their related group. This taxonomy divides the cloud vendors into infrastructure (i.e., public cloud and private cloud), platform (e.g., business user platforms and DevOps platform), services (billing, security, fabric management, and system integrators), and applications. This taxonomy gives a visual map of the SaaS, PaaS, and cloud computing industries. At the end of the spectrum, Forrester’s cloud taxonomy [21] is categorizing cloud services by IT infrastructure vs. business value and by the level of privacy offered. This taxonomy focuses on the dimensions of privacy and business value. It does not address vendor lock-in issues but instead focuses on the modes of cloud computing (public scale-out clouds, public server cloud, virtual private scale-out clouds, virtual private server clouds, private clouds, virtual private SaaS, public SaaS, PaaS, on-premises, ASP concepts, etc.) To provide an even clearer and more explicit perspective on the complexity of cloud computing vendor lock problem, we propose taxonomy of cloud lock-in challenges building on several incremental enhancements of existing taxonomies. In this chapter, we will adjust, refine, and extend those taxonomies, making them even more suitable and flexible for understanding the complexity of cloud vendor lock-in problem.
Few studies recently, articles, have attempted to establish a similar structure for cloud computing and its components [22, 23, 24, 25]. Although they attain some valuable understanding of several cloud services and components, they tend to be more general classifications without specifics about vendor lock-in. They neither went to the level of detail in the analysis as we did (in Section 4 and 5), nor they included all the cloud layer attributes we captured in our taxonomy. Their main objective is to classify the commercial cloud offerings in order to analyze the cloud computing market opportunities. As such, they do not address the specific lock-in potentials or limitations of the several cloud layers, nor the research opportunities associated with each cloud layer. We believe our proposed cloud ontology is more comprehensive, and encompass more detailed analysis of the cloud computing knowledge domain.
Providing lock-in taxonomy of unified and holistic SaaS architecture has not been addressed yet in a way comparable to the approach proposed in this chapter. Pursuing this further, SaaS architectures have been compared and analyzed in several studies [26, 27, 28]. Mahjoub et al. [29] conducted a survey on current cloud providers and technologies in order to help users choosing the better cloud offer that compiles with their needs or building their own cloud infrastructure with the most suitable open source technologies. The characteristics, architectures, and applications of several popular cloud computing platforms are analyzed and discussed in the paper by Peng et al. [30]. Wind [31] derived criteria from the literature analysis and compares existing open-source Cloud Computing management platforms. Important high-level criteria are taken into account (i.e., security, interoperability, user interface, etc.). Notwithstanding, cloud computing taxonomies have already been defined in several works [32, 33, 34, 35, 36, 37, 38]. The National Institute of Standards and Technology (NIST) [34], Rimal et al. [37], and Intel [38] presented a general cloud computing taxonomy. In contrast to our taxonomy, they did not address vital SaaS capabilities, such as data lock-in, contract lock-in, and application lock-in. Furthermore, Forrester [39] introduced a market-oriented taxonomy of cloud computing and did not incorporate technical capabilities of SaaS platforms and applications as discussed in our work. OpenCrowd [37] presented a general cloud computing taxonomy. The taxonomy only addresses IaaS layer and identifies four components: storage, compute, services management, and cloud broker. In contrast, our taxonomy addresses 11 components (relevant to IaaS, PaaS, SaaS, XaaS) and categorizes them into ordered layers.
Currently, there is little on offer in the way of tools, procedures or standard data formats or service(s) interfaces that could guarantee data and service portability in the cloud computing environment. This makes it extremely difficult for a customer to switch cloud providers, or to move data and services from an in-house IT environment to the cloud. In effect, this potential dependency for service provision on a single cloud provider, may lead to organizational risks should the cloud provider, for instance, go out-of-business or bankrupt. Organizations considering adopting cloud computing models are concerned about the potential for lock in and the operational challenges that a storage migration (as an example) would require. Thus, it becomes important to understand that the extent and nature of lock-in varies per cloud type:
SaaS lock-in: SaaS providers typically develop a custom application tailored to the needs of their target market. The consumer data of a SaaS product is typically stored in a custom database schema designed by the SaaS provider. However, if the provider does not offer readymade data export functionality, the customer will need to develop a program to extract their data and write it to a file ready for import to another provider. Where the customer has developed programs to interact with the provider’s API directly (e.g., for integration with other applications), these will also need to be re-written to consider the new provider’s API. SaaS suffers from data lock-in, contract lock-in, and application lock-in risks.
PaaS lock-in: occurs at both the API layer and at the component level. At the API layer, PaaS lock-in occurs as different providers offer different APIs. PaaS lock-in happens at the component (i.e., runtime) layer as standard runtime environments are often heavily customized to operate safely in a specific cloud environment. PaaS suffers from framework lock-in and data lock-in (as in SaaS), but in this case, the onus is completely on the customer to create compatible export routines and more importantly for the customers’ developers to understand and consider these differences that are pointed out.
IaaS lock-in: varies depending on the specific infrastructure services consumed. Virtual machines (VMs) that can be moved to the cloud from (heterogeneous) data centers, and between vendors’ IaaS clouds, are an asset for organizations. However, doing so requires cloud IaaS providers to support a standardized VM file format. Currently, there is a little in offer in terms of standardized file format for virtual machine images and VM management. While virtualization can remove concerns about physical hardware, distinct differences exist between common hypervisors such as ZEN, VMware, and others. For example, data lock-in is the obvious concern with IaaS storage services. IaaS storage provider offerings vary from simplistic key-/value-based data stores to policy enhanced file-based stores. Moreover, feature sets can vary significantly, hence so do storage semantics. However, application level dependence on specific policy features (e.g., access controls) may limit customer’s choice of IaaS provider.
However, since the focus of this chapter is on mitigating potential risks of vendor lock-in at SaaS layer of the cloud computing stack, therefore, the following sub-section(s) presented below: (1) narrows the discussion parameters for SaaS application migration scenarios, and (2) serves the purpose of highlighting some but certainly not all the cases where interoperability, portability, and security are important issues when migrating in the cloud computing environment.
Despite the numerous advantages of cloud computing to organizations, many challenges such as data lock-in, application lock-in, and contract lock-in remain inadequately addressed. In this section, we aim to address these issues of concern as it pertains to SaaS usage and their implications to enterprise cloud adopters. We tackle the vendor lock-in challenges that act as barriers to either adopting cloud-based SaaS services in enterprises, or migrating/switching between SaaS vendors. Thus, our line of reasoning here provides a concise yet relevant discussion and in-depth analysis of these issues with some fundamental guidelines that should be observed by organizations, entering a cloud computing service SaaS contract. While it is important to understand that the extent and the nature of vendor lock-in vary as per the cloud type, be aware, however, that our focus within this chapter is aimed at SaaS lock-in, specifically. Both PaaS lock-in and IaaS lock-in is outside the scope of this thesis.
As cloud computing adoption rate soars across enterprises (small or large), the risks of vendor lock-in is prevalent. Limited studies exist, except for [40, 41, 42, 43, 44], to analyze and highlight the complexity of vendor lock-in problem in the cloud environment. Therefore, when selecting SaaS offerings from cloud vendors, organizations need to consider and balance service criticality against the significance of avoiding potential risks of vendor lock-in. Though it is claimed that vendor lock-in is not exclusively a computing problem, since it also occurs in the classic IT setting—in this case, the customer has more control over the data and services. However, Conway and Curry in [45, 46] argues that due to the immaturity of current cloud computing environment, data, applications, and services are primarily vulnerable to the risk of lock-in. In general, with cloud computing architectures, the risk of vendor lock-in rises with the number of hardware and software components the vendor provides. Thus, the highest lock-in risks occur with SaaS services because the vendor controls all key components of the customer’s information system. SaaS lock-in affects both data and applications. Besides, cloud SaaS offerings are often based on proprietary non-standard data formats and application logic, which can make migrating data and services to another cloud SaaS vendor difficult. This potential dependency for service provision on a cloud SaaS vendor may lead to specific data and application lock-in challenges as described below.
Data lock-in challenge: in using cloud SaaS offerings, enterprise data are typically stored in a custom database schema designed by the SaaS vendor. SaaS cloud vendors generally do not provide conceptual or logical data models for their service. Most SaaS vendors offer API calls to read and export data records. However, if the provider does not offer readymade data “export” functionality, the enterprise will need to develop a program to extract their data and write it to a file ready for import to another vendor. It should be noted that database schemas, data formats, and application programming interfaces (APIs) are valuable in providing the function of interoperability of communication and processing within the SaaS cloud [41, 44]. However, the closed proprietarily coding of these key components across SaaS vendor offerings results in the need for resource (i.e., human effort, time, and cost) to be focused into developing a solution to break free from having the enterprise data locked into SaaS offerings (e.g., data models, platforms, and programming languages). While custom code may be needed for data transformation, it is also wise to check that standard data formats used by the enterprise can be supported by other cloud SaaS vendors or there is a transformation mechanism available. This further drives the requirement for consumers using the SaaS services to understand the business and associated data that needs to be managed to support the business process being automated or replaced, before making important migration decisions.
Application lock-in challenge: replacing an on-premise ICT system with its cloud SaaS counterpart benefits from the advantages of converting capital expenditure to operational cost [47, 48]. However, cloud SaaS applications are developed to run on a particular operating system. SaaS vendors typically develop these custom applications tailored to the needs of their target market. Porting them to operate on another cloud SaaS provider’s environment is a significant effort, because the application processing logic is supplied by the vendor and data may be proprietary [43]. Likewise, a company can spend a considerable amount of time and effort moving its SaaS applications (and data stored in one system) to a cloud SaaS environment due to application lock-in risks. For instance, enterprise SaaS customers with a large user-base can incur very high switching costs when migrating to another SaaS vendor as the end-user experience is impacted (e.g., re-training staffs). However, it may be easy in the case of SaaS to terminate a service from one cloud vendor and start service with another. If the terminated vendor is contractually required to provide data, migrating may be of questionable use without significant cooperation and resources provided by the vendor. For example, if the data is maintained in a proprietary database architecture (e.g., NoSQL data models), a conversion effort will be required, and, unless the appropriate cooperation is obtained, the project may prove costlier and take longer than forecast. Furthermore, where the customer has developed programs to interact with the vendor’s API directly (e.g., for integration with other applications), this will also need to be re-written to consider the new vendor’s APIs. Accordingly, as pointed out by Polikaitis [49], standardizing on cloud SaaS environment is a serious decision with long-term financial implications for an enterprise.
The vendor lock-in challenges discussed in this section are high-category risks that organizations must tackle when considering cloud SaaS solutions. They present two potential drawbacks for cloud service consumers; first, the provider has the customer organization at a disadvantage, as it can push disagreeable terms on the customer because it has no viable exit strategy. Secondly, if the provider goes out business in the worst case, the customer may have trouble sourcing an alternative. This can take considerable time, cost, and effort to find a SaaS replacement and move the entire organization’s data. However, regarding these challenges, an exit strategy will either mitigate or exacerbate the impact of such risks. There is a need for these organizations to understand what the exit strategy looks like, even if it is unlikely that they will exit a service soon—besides, no company would want to buy into a service where they feel they had no alternative provider [49]. An exit strategy in this context refers to a way of moving to another SaaS vendor if the enterprise wishes to do so. Hence, a missing exit strategy is said to exacerbate data and application lock-in risks in SaaS offerings.
In any relationship between a cloud SaaS service vendor and cloud SaaS consumer, vulnerabilities exist that can result in vendor lock-in situations [50]. For example, a lack of standard technologies and unification of interfaces within the cloud stack creates barriers for migration. In today’s cloud computing marketplace, data, applications, and services are vulnerable to the risk of lock-in. It is the cloud service customer’s data that is the primary asset at risk from lock-in situations here. Hence, if a cloud SaaS customer’s data cannot be migrated, accessed, or retrieved due to related challenges with portability and interoperability issues at the individual levels of the cloud computing stack, business continuity is at risk. These issues, consequently, translate into two core dimensions of SaaS lock-in as precisely described below.
Horizontal lock-in: cloud service consumers face horizontal lock-in situations when vendors restrict them to freely replace a SaaS solution with a similar or competitive product offering. This situation can arise when a customer wishes to move to another SaaS solution but is hindered by obstacles or migration limitations put in place by their vendor. This consequently affects data portability, re-creation of cloud-based services to on-premise (i.e., roll-back), integration and interoperability, etc. Some of the likelihood of issues with SaaS cloud vendors or technology products which give rise to horizontal lock-in situations are: discontinuing software products without clear roadmaps for replacement, developing economically unsupportable solutions, releasing products without appropriate quality checks, vendor application highly customized to suit enterprise, etc.
Vertical lock-in: in this situation, cloud SaaS customers are restricted to the use of specific software and hardware within the overall cloud service stack because of a chosen SaaS solution. This implies also that the use of an operating system, database hardware vendor, and even any required implementation (or integration) partner during migration may be dictated by vendor. At the SaaS layer, vertical lock-in can be difficult to avoid since the choice and location of hardware at the cloud provider’s data center is out of the cloud service customer’s control. Thus, the idea will be to ensure whether the data centers are locked or not into a particular operating system environment through their choice of virtualization. Common issues and challenges fraught with vertical SaaS lock-in includes but not limited to enterprise infrastructure built around vendor proprietary standards, SaaS applications built using vendor proprietary APIs, data in SaaS cloud products resides in proprietary database with no ability to export, and the vendor owns data rights necessary to operate SaaS solution, etc.
Therefore, while the business value of cloud computing is compelling, it is clear from raised above that many organizations still face the challenge of lock-in when adopting cloud SaaS service capabilities. With regards to cloud adoption approaches in enterprises, for simplicity, in this section, we categorize cloud computing SaaS services into two broad titles, namely: (1) horizontal SaaS offerings and (2) vertical (or sector-specific) SaaS offerings. Horizontal SaaS offerings are typically applicable to organizations across a range of business sectors, i.e., they are not specific to a business but can be found in almost any kind of organization. Some common horizontal SaaS applications are in the areas of email, customer relationship management (CRM), productivity, collaboration, analytics, etc. With the proven success and maturing of horizontal SaaS offerings, sector-specific SaaS offerings are emerging to include application in the areas of logistics and supply chain management (SCM), for example. Vertical SaaS offerings refer to specialized applications that will be used to support a focused business function or core processes that are found within that industry, e.g., patient record management for hospitals, hotel management software, etc.
Being that cloud SaaS solutions are strategically engineered to have control points, it makes difficult for customers to migrate away from their technology to competing solutions [51]. Thus, it is important that customers review the SaaS lock-in discussed above, to determine cloud vendors and technologies that have the highest replacement or switching costs, and are most likely to create operational, financial, or legal issues. Organizations should also analyze SaaS offerings (i.e., vertical or horizontal) in terms of Total Cost of Ownership (TCO)/Return of Investment (ROI) against associated risks such as vendor lock-in, interoperability, portability, and security, including defining a clear strategy for both private and public implementations before adopting specific SaaS offerings.
To identify open challenges and facilitate future advancements, it is essential to synthesize and classify the research on cloud computing vendor lock-in conducted to date. In this section, we discuss causes and problems of vendor lock-in, and present a taxonomy of cloud lock-in challenges covering the hardware, operating system, virtualization, and data center levels. A model encompassing the various elements (or triggers) of vendor lock-in risks in cloud computing is presented. The analysis of this multi-dimensional model shows that each element can create different effects of lock-in on specific business processes operating in a cloud environment. With the intent to create a cloud lock-in model, both for studying proprietary lock-in challenges in the context of service migration and for supporting decision-making for enterprise cloud adoption. Authors’ aim in this section is to consider the various risks and challenges of vendor lock-in presented in Figure 1, and organize them in hierarchical categories of perspectives, thus creating a cloud computing vendor lock-in taxonomy. But before doing so, it is important to make clear that for any given information processing system (whether in cloud or non-cloud environments), there are a few user categories—or more accurately, several “roles”—that have an interest in the system. Each role is interested in the same system, but their relative views of the system are different, they see different issues, they have different requirements, and they use different vocabularies (or languages) when describing the system. In this direction, rather than attempting to deal with the full complexity of cloud lock-in problem, author mainly attempts to recognize these different interests by defining different viewpoints of the lock-in problem in question. Each of these perspectives or viewpoints is chosen to reflect one set of inter-related consumer cloud lock-in concerns.
Perspectives for categorizing vendor lock-in risks in cloud computing. Top level overview of the viewpoints of cloud lock-in taxonomy, highlighting the three main perspectives to view the broad problem of vendor lock-in—related to business, technical and legal categories.
Across the three inter-related perspectives of vendor lock-in, organizations can use the proposed taxonomy to review their existing processes for cloud adoption and migration, data governance, and purchase policies to see if these support a strategy to achieve a high-level of flexibility and control to reduce the chance of being unavoidably locked into a single cloud provider offering. The aim of this taxonomy is to give both cloud service consumers (i.e., enterprises, end-users, developers, etc.) and cloud service providers guidance in the provision and selection of cloud services, indicating how to mitigate the risk of being tied to a cloud service provider—due to the difficulty and costs of switching to use equivalent cloud service from other providers. The taxonomy of cloud vendor lock-in perspectives partitions the challenges to be addressed into three viewpoints: business, technical, and legal. Each of the viewpoints can be used as problem analysis technique as well as solution space of the relevant issues of the lock-in problem domain. The main structure of the taxonomy along with its top levels of classification is depicted in Figure 1. The illustration is not meant to be exhaustive but to give a precise yet accurate view of the broad problem of cloud lock-in from different perspectives.
The three main perspectives of cloud vendor lock-in problem(s) are: business (or economics) perspective, technical (or technological) perspective, and legal (or political) perspective. Together they provide a complete picture of cloud computing vendor lock-in challenge. The concerns addressed in each of the perspectives are precisely presented. For instance, the business dimension is subdivided into standards, interoperability, portability, and security. The technical perspective includes constraints related to integration, compatibility, and APIs that are implementation-specific requirements or restrictions which may hinder interconnectability and/or trigger lock-in situation in the cloud. The complete organization of this scenario is presented in Section 5.3. While the first two categories correspond to enterprise architecture requirements (for enabling interoperability and portability) of products and IT services based on standard interfaces to interact seamlessly without the need for a large amount of integration efforts, the legal or political perspective is split into four sub-categories (i.e., SLA compliance, contract termination, cloud migration strategies, and metadata and data ownership) per the service life cycle and measures in which various aspects of cloud services offered and managed for a cloud service consumer can result in a lock-in situation. It is also noted that the lock-in risks in this dimension or perspective cover the complete information lifecycle (i.e., generation, use, transfer, transformation, storage, archiving, and destruction) inside the cloud providers’ perimeter and in its immediate boundaries (or interfaces) to the consumers. The expansion of this categorization is depicted in Figure 4.
A clear perspective of the main risk factors that contribute to a lock-in situation in the cloud environment and how such risk(s) should be organized to ease decision-making is the main step for having a comprehensive analysis of the status of cloud computing vendor lock-in challenges. To organize the complex and broad data related to cloud lock-in problems and to facilitate further studies in this area, based on our previous work [44], the main problems (i.e., risks or challenges) of cloud lock-in are identified and grouped into a model composing of 11 categories namely: standards, portability, interoperability, security, integration, compatibility, APIs, data, contracts, SLA compliance, and cloud migration (in/out) strategies. These elements are placed in a hierarchical order of significance to the broad lock-in problem, in general. Moreover, the elements are significant considerations to the use of cloud services, and are also indicators to how component may trigger and/or intensify the risk of lock-in involved. The hierarchical categorization approach employed in this work assists in demonstrating how each element of vendor lock-in relates to several other components in the architecture of cloud computing. At a high level, the model establishes a common language (i.e., ontology) for easy understanding and communication of the capabilities and requirements which should be standardized in a cloud environment to facilitate open collaboration and interoperability amongst cloud providers, thereby avoiding the risk of a single provider lock-in. At a low level, the model is further composed into taxonomy to support consumers cloud service selection and adoption strategy in terms of validating cloud provider’s solutions to achieve architectural integrity of business solutions of an enterprise’s cloud ecosystem.
Prior to presenting the proposed taxonomy, it is worthy to mention that the identification of elements and components of the categorization used is based on the critical review of key literatures in [40, 41, 42, 43, 44, 45], the preceding section and subsequent sections. This critical review followed the systematic approach proposed by [52]. Some of these studies include standards and proposal documents from academia and industry as well as independent quantitative and qualitative studies conducted by author. The systematic review also covered general computing, IT and information systems (IS) journals, conference proceedings, books, industrial white papers, and technical reports. The fundamental purpose was to identify broadly any possible factors and issues that might lead to or intensify potential risks of vendor lock-in. Through this extensive and critical literature review, author established and proposed a set of potential cloud lock-in risk factors using taxonomy. The taxonomy is explained using case-examples from existing services of major cloud providers with an emphasis on the distinction made between services in software application programs (SaaS), platform (PaaS), and infrastructure (IaaS), which are commonly used within traditional enterprise computing or as the fundamental basis for cloud service classification. Based on the review of existing literature studies and the results extrapolated from our systematic study, the following constraints and challenges have been identified with switching between cloud SaaS vendors: switching cost, data portability, API propagation and integration issues, interoperability and standards, security risks, contract and SLA management, and legal challenges (data location constraints, data ownership rights, cloud in/exit issues, legal jurisdiction and compliance, etc.). They have been further grouped into three main challenge (i.e., technical, business environment, and legal) areas of SaaS migration, and briefly analyzed below. The first four are technical constraints to the growth (i.e., in terms of migration to, and adoption) of cloud computing SaaS services; the next four are internal business environment obstacles to switching between cloud vendors once the SaaS solution has been and/or replaced; and the last four challenges are policy and legal issues intrinsic to cloud SaaS migration process. These challenges represent shared concerns that need to be addressed prior to SaaS adoption, or switching between cloud SaaS services and vendors.
As an example, integration and data portability, for instance, are two core lock-in risk factors mentioned and discussed in several of the referenced studies. This is because as new cloud SaaS services are deployed within an existing enterprise environment, the need to integrate them with various on-premise systems and other cloud services becomes important. Thus, the integration task and the need to ensure data portability have increased the complexity of decision-making in respect of enterprise cloud SaaS migration [41, 42, 53, 54, 55]. Therefore, as organizations struggle with the complexities of integrating cloud services with other critical systems residing on-premise, the ability to share data (i.e., portability) across these hybrid environments remains critical, and continues as more enterprise workloads and projects are committed to cloud computing SaaS services. As would be seen in the subsequent section, different elements of lock-in encountered in each category is described below to aid readers’ understandability of the overall complexity of cloud lock-in situation in more details. Each of these elements in the categorization (or classification) model below, results in subdivisions highlighting the main risk factors of vendor lock-in that have been identified.
It focuses on the needs of the consumers of a cloud product or service offering. It describes the business challenges of vendor lock-in in terms of answering what is required of a cloud provider to meet customers’ expectations to avoid over dependency on a product and the vendor. From a business perspective, avoiding vendor lock-in is requested by reasons varying from optimal service selection regarding utilization, costs or profits, to technology (hardware or software) changes. The adoption of cloud computing is still hindered by the lack of proper technology (or technology maturity), knowledge (of use), transparency, and trust issues. One of the problems spanning across these reasons is the low level of portability and interoperability of cloud applications and data storage services. The vendor lock-in challenge with respect to both low-level resource management and application level services is related also to the lack of world-wide adoption of standards or interfaces to leverage the dynamic landscape of cloud related offers. Portability and interoperability standards provide customers the ability to switch cloud providers without a lock-in to a provider. Moreover, data and applications in the cloud reside on systems’ consumers who do not own and likely have only limited control over—which can result in loss of data and application security issues. Lack of interoperable and portable standards for different security policy or control, key management or data protection between providers may open undiscovered security gaps when moving to a new provider or platform. Hence, it becomes important to consider several items, for portable and interoperable security standards, to protect sensitive data being moved to or in the cloud. In this direction, author acknowledges that not all information used within a cloud system may qualify as confidential or fall under regulations requiring protection. Therefore, the security categories proposed in this case are based on information security lifecycle for protecting data in terms of confidentiality, availability, and integrity (which can be applied not only to cloud environments, but also to any solution which requires basic interoperable and portable security integration). The complete organization is depicted in Figure 2.
Vendor lock-in taxonomy-business perspective. NB: components from the business perspective of vendor lock-in are subdivided into four categories (i.e., standards, interoperability, portability and security). These elements are significant considerations to the use of cloud services, and are also indicators to how each component may trigger and/or intensify the risk of lock-in involved.
The technical dimension is subdivided into integration, compatibility, and APIs. In this case, the classification proposed are based on technical constraints placed on consumer’s ability to achieve seamless integration and compatibility with user, administrative, and programming interfaces for using and controlling a cloud service. Since the interfaces and APIs of cloud services are not standardized, different providers use different APIs for what are otherwise comparable cloud services. These APIs expose the semantics (i.e., description of cloud services by its provider) and technologies (i.e., middleware and applications used to support a cloud service) used by a provider by providing the service management functionality. This implicit lack of standards (as pointed earlier) adoption by cloud providers is in fact a breeding ground for various types of heterogeneity (e.g., hardware and platform), because each cloud provider uses different technologies, protocols, and formats. This heterogeneity is a crucial problem as it gives rise to vendor lock-in situations in cloud computing. Thus, the need for a well-defined standard interface plays an important role toward achieving compatibility and manageability inside and between clouds. Then, cloud service consumers can take advantage of seamlessly integrating different provider offerings, combining benefits of each cloud to build solutions that are coherent to their respective business goals. The complete categorization of technical perspective of lock-in is presented in Figure 3.
Vendor lock-in taxonomy—technical perspective.
The need to avoid the risk(s) of cloud vendor lock-in from a legal perspective is to limit possible constraints on data, application, and services per the locations or national laws, as well as grant customers the free will to avoid dependence on only one external provider. The categorization in this dimension includes aspects related to contract and license issues, exit process or termination of use of a cloud service, judicial requirements and law (such as multiple data locations and privilege management). The legal perspective is split into four sub-categories (i.e., SLA compliance; contract termination and exit process; cloud migration strategies; and metadata and data ownership) per the service life cycle and measures in which various aspects of cloud services offered and managed for a cloud service consumer can result in a lock-in situation. It is also noted that the lock-in risks in this scenario cover the complete information lifecycle (i.e., generation, use, transfer, transformation, storage, archiving, and destruction) inside the cloud providers’ perimeter and in its immediate boundaries (or interfaces) to the consumers. Audit and monitoring are also important aspects worth considering in the legal dimension, due to the requirements that a cloud provider should ensure to fulfill service agreements. For instance, the exit process or termination of the use of a cloud by a customer requires careful planning from an information security perspective. From a data security and storage perspective, it is important that once the customer has completed the termination process, none of the customer’s data should remain with the provider. Thus, the exit process must allow customer to retrieve their data in a suitably secure form, backups must be retained for agreed periods before being eliminated and associated event logs and reporting data must be retained until the exit process is complete. Meanwhile, customers are advised to negotiate directly with their cloud service provider to ensure appropriate exit process provisions and assurances that are included and adequately documented in their cloud SLA and contracts. The expansion of this categorization is depicted in Figure 4.
Vendor lock-in taxonomy—legal perspective.
In this section, we examined current proprietary lock-in risk factors in cloud computing services and proposed a model for classifying these to aid better enterprise cloud procurement and migration decision process. Aiming to organize this information into a useful tool for comparing, relating, and classifying already identified lock-in challenges and risks as well as future ones, we present a taxonomy proposal for cloud computing vendor lock-in. We focus on issues that are specific to cloud computing, without losing sight of important issues that also exist in other distributed IT systems. Here proposed taxonomy is capable of classifying both current and future cloud computing services for potential risks of single provider lock-in. The simple layered-tree structure used in the taxonomy development allows quick and easy analysis, by giving the user a set of core elements at each level. This clear structure makes analyzing and understanding the complexity of vendor lock-in challenges in cloud computing services more efficient than using table-based comparisons. While, table-based comparisons of cloud computing services exist [59], however, they are mainly for commercial use and the degree of detail varies greatly. Further, the taxonomy not only helps to categorize a cloud lock-in risk scenario, but it also helps potential customers and developers to point out what characteristics the service they seek or wish to develop should have (to avoid lock-in).
In summary, the contribution of this chapter is twofold. First, we identified the main risk factors of vendor lock-in that must be considered when transforming IT services to cloud-based solutions, or migrating to a cloud computing environment. These lock-in challenges were derived based on an extensive literature review and previous works on enterprise migration to public/private and/or hybrid cloud environment. We believe that the identified lock-in challenges are pertinent issues that require urgent solutions in the cloud environment, in order to pave the way for providing more standard and secured cloud services. Second, in order to classify and categorize the core elements of cloud computing lock-in, using a systematic classification scheme, taxonomy of cloud lock-in challenges is proposed. Moreover, this taxonomy extends our previous works presented in, respectively, providing an enhanced review of the cloud computing lock-in challenges previously presented, as well as a deeper analysis of the related work by discussing the main SaaS lock-in risk dimensions. Furthermore, we discuss the PaaS and IaaS lock-in aspects related to cloud computing, a fundamental yet still underserved field of research.
This chapter provides a comprehensive analysis of cloud computing vendor lock-in problem, and proposed taxonomy of cloud lock-in perspectives. The three main perspectives of cloud vendor lock-in problem(s) are: business (or economics) perspective, technical (or technological) perspective, and legal (or political) perspective. Together they provide a complete picture of cloud computing vendor lock-in challenge. The concerns addressed in each of the perspective have been precisely and concisely discussed in this chapter. The hierarchical categorization approach used in taxonomy assists in demonstrating how each element of the vendor lock-in problem relates to several other components in the architecture of a cloud computing system. At a high level, the model establishes a common language (i.e., ontology) for easy understanding and communication of the capabilities and requirements which should be standardized in a cloud environment to facilitate open collaboration and interoperability amongst cloud providers—thereby avoiding the risk of a single provider lock-in for cloud consumers. At a low level, the model is further composed into taxonomy to support consumers cloud service selection and adoption strategy in terms of validating cloud provider’s solutions to achieve architectural integrity of business solutions of an enterprises’ cloud ecosystem. In contrast to existing works in the field, our study extends the current scope of cloud computing migration beyond one specific challenge area, instead it addresses the complex vendor lock-in problem from three main perspectives or categories—thereby contributing substantially to the growing body of knowledge on cloud computing.
In our future work, we present a high-level (combined) view of the proposed taxonomy. It will show the transformation between the different vendor lock-in perspectives. This high-level taxonomy of vendor lock-in risks identifies the key cloud computing interoperability, portability, API interface categories, as well as other relevant and intricate components of cloud systems that should be portable and interoperable. For example, standardization of the interfaces between these components is the first step to achieving interoperability and portability—as it prevents being locked into any cloud or provider. In the expanded taxonomy diagram, a layer represents a set of functional and non-functional requirements that provide similar capabilities or serve a similar purpose to support a vendor neutral (and technology-independent) sourcing strategy for cloud applications and services. We also survey various key works in the area and map them to our taxonomy to guide future design and development efforts.
Electrokinetics (EK), as the name implies, is a technique of using electric field to cause motion. The motion can be that of a liquid or colloidal particle (microscopic solid particles suspended in a fluid). This concept of electrokinetics has four main features: Electro-osmosis (EO), electrophoresis (EP), streaming potential (StP), and sedimentation potential (SeP). In the disease diagnostics arena—an integral part of the application of microfluidics in healthcare, electro-osmosis and electrophoresis are the main electrokinetic forces that particles experience. Electro-osmosis involves the bulk motion of a liquid through a solid surface under the influence of electric field. On the other hand, electrophoresis is the motion of a solid material through a liquid under an electric field effect. Both electrophoresis and electro-osmosis require that the surface of the solid be charged. As shown in Figure 1, when the surface of the particle, placed within a uniformly distributed electric field, is positively charged (A), the particle (suspended in a characterized liquid) moves to the left but to the right (against the field direction) when the charge on the surface is reversed (B). If the particle is a neutral body (C), no electrophoretic effect will be seen. Figure 1D is a case where liquid is flowing through a charged solid surface. The solid surface (usually glass or surface-treated polymer) become deprotonated when in contact with the liquid such that counter (positive) ions from the liquid goes into the channel surface to firmly replace the detached positive ions forming what is referred to as the Stern layer. Columbic force of attraction causes the Stern layer to, in turn, attract negative ions from the liquid forming the diffuse layer (together with some unattached positive charges) beyond which the region of electro-neutrality is initiated. A plane, called the slipping plane, formed around the loosely-bound diffuse layer is very crucial to how the bulk of the liquid would move when the electric field if applied across the static upstream and downstream flow region. The potential of this slipping plane is called the Zeta potential, ζ [1]. The same concept of Zeta potential applies to a charged solid moving through a liquid. For a charged solid moving through a liquid under an electric field effect, the rate of motion of the solid,\n
The representation of electro-osmosis and electrophoresis as the two main electrokinetic phenomena in microfluidic channels: (A) positively charged particle under a uniform field region effect will move toward the negative electrode according to the fundamental attraction-repulsion laws. (B) Negatively charged particle placed under the same electric field region will migrate toward the positive electrode. (C) A no-charge (neutral) particle placed in the same field region will experience no electrophoretic motion because electrophoresis only applies to charged particles. (D) Representation of the deprotonation of the terminal (SiOH)/surface chemical group of the channel wall (treated poly(dimethyl siloxane) (PDMS) or uncoated glass), which births the electro-osmotic pumping within the channel.
On the other hand, when a liquid is flowing through a charged wall of Zeta potential,\n
In dielectrophoresis (DEP), the applied electric field must be non-uniform. The movement of the particles in non-uniform electric field does not depend primarily on the particle charge but on the ability of the particle to become polarized relative to that of the suspending medium [2].
\nTherefore, DEP force is always in effect when the particle is charged or not. This means, dielectrophoresis, therefore, safely be defined as a technique of using non-uniform electric field to induce the motion of a charged or an uncharged particle. Electric field can be rendered non-uniform in different ways. Figure 2 shows some examples of how to generate non-uniform electric fields using AC or DC source. For the AC field, a simple unequal spatial electrode-pair arrangement (Figure 2A) would generate non-uniform field (AC DEP or classical DEP). A second method is to place an insulating constriction within a simple straight channel operating under DC condition that would make the field non-uniform (DC-iDEP or iDEP). There is never any hard and fast rule regarding how the electrodes should be arranged or the constrictions be distributed but simulation could assist in formulation of the device architecture. Every researcher has a purpose in mind and that purpose drives the architecture of the channel or device without denying the underlining physics.
\nGeneration of non-uniform electric fields for dielectrophoretic applications; (A) simple unequal spatial electrode-pair arrangement connected to an AC source with the lower electrode acting as the high-field region while the upper electrode on the low-field region. The field low-high regions remain the same even when the electrode terminals are interchanged owing the frequency component of the AC source (B) insulating constriction (or obstacle) changed the uniform field between the electrodes to non-uniform field at its location.
Characterization of biological entities like cells involves the utilization of various methods including but not limited to electrical, magnetic, acoustic, and optical characterization to explore cell properties. In this section, electrical method will be discussed (with focus on dielectrophoresis) since dielectrophoretic force is related, in part, to the electrical properties of the biological cell. In the utilization of electrical method for bioparticle characterization, it is not uncommon to use impedance cytometry, dielectrophoresis, and electrorotation. Impedance cytometry works on the principle that when a particle suspended in a conductive fluid passes through a small orifice (comparable to the size of the particle) created by two electrodes, the passage of the particle through the (usually AC) electric field between the electrodes results in the generation of electric signal, which can be processed to provide valuable information about the electrical properties of the particle. In electrorotation, four electrodes are each charged with AC voltage of different phases to generate a rotating electric field, thus setting up an electrical torque. When a (spherical) particle is placed within this rotating field, it becomes polarized inducing a dipole. The dipole moment induced within this particle rotates with the electric field at certain velocity. However, the multiphase nature of the four electrodes causes the particle to lag behind the field by a factor that depends on the frequency of the rotating field. Since the particle velocity is determined by the torque in the rotating electric field, electrical properties of the particle can be extracted measuring the dependence of the torque on the field frequency. With dielectrophoresis, the case is different. When a charged or uncharged spherical particle is placed between an unequally dimensioned AC electrode-pair which is generating non-uniform electric field, (Figure 2A) the particle becomes polarized just as the medium in which the particle is suspended [3]. The particle could then move toward the region of high field (HF), low field (LF) or remain unperturbed by the field depending on the properties of the applied electric field, suspending medium and the particle itself. When the particle moves toward the HF region, the phenomenon is termed positive dielectrophoresis (pDEP) while it is called negative dielectrophoresis (nDEP) if the particle’s translational motion is toward the LF region [4]. Usually, when a particle is experiencing nDEP, for instance, it does so over a range of frequency. As the frequency changes further, the particle can translate to the pDEP regime. Before this happens, however, at a specific point of inflection where the particle comes to a halt before changing regime must have been reached. The frequency at such point of inflection is termed crossover frequency. At the crossover frequency i.e. after the application of the AC electric field parameters, the particle is only seen vibrating at a spot without any appreciable translational motion. At this point, the particle experiences no DEP force (\n
where \n
This implies that the real part of the Clausius-Mossotti (CM) factor, \n
That is, \n
Hence, \n
Setting the real part to zero with \n
Eq. (7) represents a simplified presentation of the first crossover frequency (\n
The conductivity-frequency data is then fitted with the appropriate model representing the biological materials of interest. Human red blood cells (RBCs), for instance, can be modeled as a bag of cytoplasm have an insulating plasma membrane. This type of model is popularly referred to as the single-shell model [5, 6]. Figure 3A shows the representative images of nDEP, pDEP, and crossover states for RBCs at a given AC amplitude and sweeping frequencies. Experiments resulting in these images are generally conducted by suspending the particles in an isotonic medium (5% dextrose and fed into a reservoir sealed onto a borosilicate glass with an interfacial 90°, low-separation electrode-pair connected to an arbitrary waveform generator. At a fixed amplitude output, the frequency of the AC field was varied until the crossover frequency was reached and surpassed. The conductivity of the suspending medium was then sequentially increased using phosphate-buffered saline (PBS) or other conductivity conditioners and at each increase, the corresponding crossover frequency was obtained. For other bioparticles such as the nucleated white blood cells and bacteria, the double-shell and three-shell models can be applied respectively. Detailed shell analysis for biological cells is available in diverse literatures [7, 8, 9, 10].
\nImages of the particle behavior under different field frequencies. (A) Particles well dispersed before field application. (B) Particles undergoing nDEP forming chains with increased particle-particle interaction. (C) Particles at cross over frequency; no response to the dielectrophoretic force. (D) Particles clinging to the high-field region depicting pDEP.
The popularity of six sigma DMAIC (define, measure, analyze, improve, and control) approach has made more companies to embrace extensive modeling and simulation in order to save cost and time. In the production of chip-based disease diagnostic platforms, numerical computation is seen as an important process step as it affords the flexibility of exploring how various parameters affect device performance without the need for extensive experimental research. In cases where experimental activities are needed to improve numerical computations, the utilization of the design of experiment principles is usually a wise choice. In the design of experimental principles, ability to obtained related factors, which can be confounded optimally is a key component of the cost and time-saving strategy. Microfluidic devices for disease diagnostics can be operated using AC or DC sources. Discussion in this section will be based on DC operations. Details of AC-operated designs have been given elsewhere [11, 12, 13].
\nIn DC models for disease diagnostics, electro-osmosis and dielectrophoresis are usually the electrokinetic mechanism that governs the transport phenomena prior to the region of electric field non-uniformity within the channel [14]. At the region of non-uniform electric field, the dielectrophoretic force is combined with these electrokinetic forces to bring about the desired particle differentiation either through trapping (pDEP) or streaming (nDEP). A smart idea, then, is to locate the exit channels close to the region immediately following the field gradient so that sorted bioparticles can be collected appropriately. Failure to do this might result in the recombination of streamlines, which tend to restore the separated cells to their ab initio states.
\nModels of the envisaged diagnostic devices are usually drawn to scale using any suitable software. (AutoCAD, SolidWorks, etc.). These models are then interfaced with multi-physics simulation software such as COMSOL Multiphysics, FLUENT, to explore parameter dependence and their effects of targeted outcomes. It is not uncommon to draw the models using the functionalities available in the simulation software themselves. One requirement to emphasize here is the sound knowledge of the physics governing the operations of the diagnostic device. These inexhaustible physics are discussed in this section. For insulator-based (iDEP) diagnostic devices, the physics usually involve momentum transport (Newton’s Law), mass transport (Fick’s Law), energy transport (Fourier’s Law) and charge transport (Maxwell’s Laws/Ohm’s law) [15].
\nMomentum transport involves the transfer of momentum from one particle to another. This transfer results in the continuous change in fluid’s positional space leading to the concept of fluid dynamics (hydrodynamics for liquids). Depending of the focus of any project, momentum transport can be explored in 1D, 2D or 3D. One good approximation in iDEP devices is that the complexity of 3D consideration can be avoided by using 2D analysis provided the channel’s width-to-depth ratio is about 5:1. The 3D to 2D approximation is also good on the basis that turbulence is not a common occurrence in micron-sized devices. The governing equations for momentum transport are generally the Navier-Stokes and mass continuity equations [16] (Eqs. 8 and 9). Navier-Stokes equation is usually reduced to Stokes equation when the continuity equation is applied at static conditions under the assumption that the Reynolds number is very low. This makes the computer solve, numerically, for the pressure and velocity distributions within the channel. Prior to obtaining the pressure and velocity profiles, appropriation boundary conditions are utilized to completely define the system. In iDEP operations, electroosmotic wall is specified in lieu of the ‘no-slip’ wall condition. This is because, in electro-osmotic flow, the bulk motion of the fluid is driven by the wall effects - a phenomenon termed electro-osmotic pumping. Electro-osmotic pumping makes the use of external pumping mechanism unnecessary. The boundary mainly utilizes the electric field solution obtained from the second physics (electric current node in, for instance, COMSOL Multiphysics) which is usually solved together with the fluid flow equations in stationary mode. The electric current module requires that the electric potential be specified in addition to electrical insulating boundaries. The spatial distribution of the electric field strength with the channel reveals that when constrictions are placed within a uniform microchannel, the effect is non-uniformity in field strength. This non-uniformity is usually seen using, in COMSOL Multiphysics for instance, color pallet or legend. As theory suggests, dielectrophoretic force acts only at the region with field gradient (i.e. at the constriction(s)). Suffice it to say that the solved electric current equations in iDEP systems usually include Ohm’s law, electric displacement and the charge conservation.
\nAfter solving the fluid and electric current equations in stationary mode to obtain the distributions of velocity, pressure and electric field within the diagnostic device, it is expedient to visually and quantitatively verify if the sorting or trapping process results in the desired output. Two approaches are possible; (1) using Fick’s law of diffusion to classify the components of the mixture as unique tubes through the transport of diluted species module or (2) using a balance of viscous drag and dielectrophoretic force through particle tracking module. In the former, it is expected that the concentration gradient between the inlet and the outlet ports may cause diffusion and when the electro-osmotic flow is initiated for bulk fluid motion, convective flux also comes into play. This implies that the total particle flux can be expressed as the sum of diffusive, EP, DEP and convective flux:
\nand
\nwhere \n
where \n
Microdevice fabrication has traditionally been through the lithographic and etching process. In iDEP devices constructed with polymer, a combination of lithography, etching and rapid prototyping is usually utilized. While lithography prints the patterns on the substrates (glass or silicon wafers), etching creates the grooves on those patterns and rapid prototyping transfers the substrates patterns to polymer. The lithography process starts with the printing of the patterns made in the modeling and simulation stage. Usually, the patterns assisted by laser or electron-beam (and other process steps) are printed on a transparent-opaque pair plate which could further transfer the printed patterns to a resist-coated substrate in the next stage of the microfabrication process. The plate is transparent where light is desired to pass but opaque where it is undesirable. This transparent-opaque pair plate is referred to as photomask. The architecture of the photomasks determines the final patterns (convex or concave, depending on the type of photoresist used) on the substrates after photoresist development, depending on the type of photoresist on the substrate. Figure 4 shows the two forms of photomasks commonly used in iDEP device making process.
\nDifferent forms of mask used in photolithography. (A) The mask intend for positive substrate photoresist and (B) the mask for negative photoresist.
Details of the process steps involved in photomask printing have been given by Mack [17]. One important point to note is that mask making process is probably the most important step in microfabrication. Any error associated with dimensions during this mask-making process will propagate through the whole microfabrication process. In iDEP devices, all dimensions (especially at the constrictions regions) are critical. A change in the fillet angle, for instance, can affect the gradient of the electric field within the device and this could, in turn, change the efficiency of the device. Following the mask-making process is the cleaning of substrates onto which the mask patterns will be transferred. Cleaning substrate can be achieved through chemical (acetone, ethanol) or physicochemical (plasma cleaning) means. In some cases, cleansed substrates are treated with adhesion promoting agents such as silane, organotitanates, organozirconates and their derivatives. These coupling agents tend to act as binders between the substrates and the photoresist. Depending on whether concave or convex patterns are desired, positive or negative photoresists are spin-coated on the cleansed substrates at some pre-determined spin coating parameters to ensure uniform surface roughness and film thickness and prevent speckle or void formation. Following the spin coating process is the low-temperature baking step. This is the step that removes some of the solvent in the resist itself. Lowering the solvent content of the film is essential to preserve its integrity at room temperature since moisture content of the film could change the film properties, enhance contamination and thus damage the entire lithographic process. It is important to note that this baking step, which tend to lower the moisture content of the film, could reduce film thickness. It is therefore expedient to factor this effect into consideration when quantifying the resist volume and spin coating parameters prior to coating. A pre-baked resist-coated substrate is ready for UV exposure after the photomask has been correctly aligned with its positional space to circumvent any overlay. The dosing period of the UV depends, amongst other factors, on its intensity, the type, and percentage composition of the photoactive component of the resists. Usually, photoresists manufacturers provide the guidelines for UV exposure using certain substrate (usually silicon-based). Using a different substrate from the manufacturers’ test substrate might have its own effect on the critical dimensions of the microstructure. If positive photoresist is used (together with mask A in Figure 4), it is expected that the exposed region will be less soluble owing to the change in the chemistry of the resist after exposure to ultraviolet (UV) light. Post-exposure baking and development of the resist will create the pattern vials which act as guides for the etchants during the etching process. After UV exposure, the substrate is usually baked to create a final structure that would reduce undercut and improve selectivity during the etching process. After this post UV-exposure baking, the resist is developed to remove the part of the resist that have been weakened by the UV. Another type of resist called chemically amplified resist has also become more common. This resist is not weakened or hardened by the photo exposure. Rather, it generates some form of cationic entity that becomes amplified during the post exposure baking and thus makes development meaningful. As mentioned earlier, the structural integrity of lithographic features is partly a function of the UV dosage. Over exposure and under exposure should be avoided. After the resist development stage, it is important to conduct some form of metrology to verify the conformance of critical dimensions and check if any overlay has occurred because of substrate relocation or displacement during mask alignment. Critical Dimension-SEM is probably the best metrological undertaking. As much as possible, the use of laser profiling system should be avoided as the collimated laser tend to form unwanted patterns on the resist. Once the dimensions of the features have been verified and defects such as edge effects have been substantially avoided/minimized, the substrate is then moved to the etching phase. Etching is the removal of some parts of the substrate as guided by the resist patterns. There are two main types of resist: Dry and Wet. Wet etching usually in HF or its mixture with HNO3, i.e., HF/HNO3 mixture has been traditionally used as etchant. However, with the Moore’s law becoming obsolete and lithographic features turning smaller day by day, dry/plasma etching are becoming preferable. After etching, some metrological analysis could also be made to check the efficiency of the etching process. Once the etched substrate has been certified okay, the next stage is the transfer of the etched patterns to the polymer. In iDEP devices, several polymers can be used for the patterning, but silicone elastomer (PDMS) seems to be more widely accepted. By mixing silicone with its curing agent in the prescribed ratio (10:1), an air-ridden mixture whose air component can be removed through vacuum-degassing operation in any closed container is formed. Degassed mixture could then be poured onto the etched substrate (herein referred to as the master), cured thermally or at ambient conditions and systematically peeled off the master, diced, punched and completed with electrodes for dielectrophoretic experiments. Depending on the goal of the researcher, external devices such as syringe pump, microsensors, imaging line, etc. can be attached to the iDEP device. The completion of the device fabrication stage is the hydrophobic ceiling of the cured PDMS to glass or other PDMS material. PDMS is known to be hydrophobic owing to its terminal Si-O-CH3 bond. This terminal bond will prevent wall-surface ionization which is necessary to initiate electrodynamic flow within the channel. There are several ways to treat this hydrophobic wall in order to render it hydrophilic by changing the Si▬O▬CH3 bond to Si▬O▬H. One common way is plasma oxidation. The plasma oxidation process involves using the radio frequency (RF) generated by changing magnetic field under low-pressure and certain gas to produce plasma, which is a mixture of electrons, atoms, molecules and other components depending on the nature of the gas used. It is believed that certain component of the plasma is responsible for the surface reaction which replaced ▬OCH3 with OH terminal. After surface treatment, high surface energy of the PDMS allows its bonding to glass/other treated PDMS and also prevents the suspending medium from beading up within the microfluidic channel thus preventing air bubbles from being trapped. To ensure that the sealing is perfect (i.e. the sealing is leak proof), the sealed reservoir can be filled with deionized water and anhydrous copper sulfate spread around the sealed interface. The whole set up is then placed in a controlled environment for confirmatory test. If the white color of the sulfate turns blue at the end of the test period, then sealing is not leak-free, and the device should be discarded. It is important to do this check because leaking device would lead to an untold operation error since the leakage will induce pressure gradient which will belie the desired electro-osmotic flow within the channel.
\nSections 1–3 have detailed how bioparticles can be characterized, iDEP devices modeled/simulated and how the numerically obtained device architecture can be fabricated using lithography and transferred to polymer using prototyping. This section will focus more on testing the fabricated devices and comparing its efficiency with the numerically obtained one. The experimental process of disease detection using iDEP devices starts with preparation of the bioparticles for DEP experiment. In some cases, the bioparticles of interests are cultured while in other cases, they are obtained and tested directly from donors, stored temporarily and worked on. Several other methods exist for obtaining samples. Irrespective of the method of obtaining the bioparticle samples, one important factor to consider before experimentation is the properties of the suspending medium. It is customary in iDEP experimentations to suspend bioparticles in a medium prior to feeding them into the inlet reservoir. During the operation of the iDEP device, it is necessary to prevent Joule heating or electrolysis in the iDEP devices by using low-conducting medium. Preventing Joule heating circumvents, amongst other things, any form of recirculation within the channel especially at the post-constriction regions [7]. Knowing that cells are cultured in relatively conducting medium at pH range that depends on the nature of the bioparticles, it is necessary to pre-treat the samples and make them fit for dielectrophoretic experimentation. The pH value of the suspending medium affects the Zeta potential of the bioparticle as well as that of the iDEP channel wall. These will in turn affect the electro-osmotic and electrophoretic contributions to the flux of particles within the channel. Apart from the medium pH, its tonicity (which is related to its osmolality) is also important. A hypertonic medium causes plasmolysis while a hypotonic result in cell turgidity. According to Eq. 3, the dielectrophoretic force experience by bioparticles depends, amongst other factors, on the size of the particle. If the suspending medium is hypertonic, it results in an increased particle radius whose effect on the DEP force is to the third power. Reduction in particle radius when the particles are suspended in a hypotonic solution has a reversed effect on the DEP force. For this reason, isotonic solution may be the more appropriate medium for preserving the cell size since there is no net movement of fluid when the cells are suspended. 5% dextrose and 0.9% NaCl are considered isotonic medium and are therefore good choices for iDEP experiments. Using direct current (DC) to generate electric field for iDEP experiment requires that one leverage the membrane characteristics of the bio-particles in context. This is because DC manipulation of cells cares less about the variation in field frequency which is the main factor in AC fields (higher AC frequency tends to put cytoplasmic contributions into account). According to the complex permittivity relation, \n
Having walked through the essential steps involved in the design, fabrication and testing of iDEP disease diagnostic device, a very pertinent question remains: where is this dielectrophoretic analysis heading to? iDEP is currently the choicest dielectrophoretic methodology for bioparticle separation/disease diagnostic purposes. This method could still be classified as being in the teething stage going by the number of research work that are published, especially since 1992. Therefore, iDEP is poised do more than separation and detection of bioparticle. For instance, bioparticle are currently being characterized using electrode-based (AC) dielectrophoresis. iDEP can be used to replace this age-long method through directional etching of glass substrate. This will boost the expansion of AC-DC DEP for simultaneous particle characterization, separation and detection. On glass, groove can be etched systematically and conducting metals, such as copper, can be electrochemically deposited.
\nThis work was supported by NSF CBET-1500815.
\nThe authors declare no conflict of interest.
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\n\nThe Open Access Publishing Fee (OAPF) is payable only after your full chapter, monograph or Compacts monograph is accepted for publication.
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\n\n*These prices do not include Value-Added Tax (VAT). Residents of European Union countries need to add VAT based on the specific rate in their country of residence. Institutions and companies registered as VAT taxable entities in their own EU member state will not pay VAT as long as provision of the VAT registration number is made during the application process. This is made possible by the EU reverse charge method.
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