Open access peer-reviewed chapter

Real-Time Systems

Written By

Ming Fan

Submitted: 26 October 2015 Reviewed: 22 March 2016 Published: 08 June 2016

DOI: 10.5772/63278

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Real-time Systems

Edited by Kuodi Jian

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Since 2004, most of chip vendors have begun to shift their major focus from single-core to multi-core architecture (W. Wolf. Signal Processing Magazine, IEEE, 26(6):50–54, 2009). One major reason of this shift is that it reaches a physical limit by scaling transistor size and increasing the clock frequency to improve the computing performance on a single-core architecture (Agarwal et al. Proceedings of the 27th International Symposium on, pages 248–259, June 2000), that is, the overall chip cannot be reached within a single clock cycle. Multi-core architecture, however, brings innovative and promising opportunities to further improve the computing performance. By providing multiple processing cores on a single chip, multi-core systems can dramatically increase the computing performance and mitigate the power and thermal issues with the same performance achievement as single-core systems. As multi-core architecture has been more and more dominant in the industrial market, there is an urgent demand for effective and efficient techniques for the design of multi-core systems.


  • Real-Time
  • Multi-Core
  • Thermal
  • Power
  • Energy
  • Periodic Scheduling

1. Introduction

Today, multi-core architecture has been widely supported by most of major chip vendors, including Intel, AMD, IBM, Nvidia, ARM, Sum microsystems, Qualcomm, Broadcom, and so on. Some of the chip manufacturers have already launched 16-core chips into the market, that is, AMD OpteronTM 6300 Series [1]. It is not surprising that in the coming future, hundreds or even thousands of cores will be integrated into a single chip [22]. When moving toward multi-core architecture, it comes with new and critical challenges in design of multi-core systems, particularly multi-core real-time systems.

As architecture becomes more and more complicated, besides the timing constraint, many other design constraints are taken into consideration in real-time system design and development. Traditional approaches focus exclusively on timing constraints [3,4,6,7]. Today, many other design constraints (e.g. power/energy, thermal, and reliability) also need to be considered seriously in real-time system design [8,9,11,12,14].

Figure 1.

Power vs. Temperature [9]: Intel Core i5-2500K (32 nm Sandy Bridge), voltage 1.26 V, frequency at 1.6 GHz and 2.4 GHz, respectively.

1.1. Power/energy analysis in multi-core real-time systems

Catalyzed by continuous transistor scaling, hundreds of billions of transistors have been integrated on a single chip [13]. One of the immediate consequence caused by the tremendous increase of transistor density is the soaring power consumption [5], which further results in severe challenges in energy and temperature[11,17]. Today, power has become a critical and challenging design objective in front of system designers.

1.2. Thermal analysis in multi-core real-time systems

The continuously increased power consumption has resulted in a soaring chip temperature. Moreover, as design paradigm shifts to deep submicron domain, high chip temperature leads to a substantial increase in leakage power consumption [13], which in turn further deteriorates the power situation due to the interdependency between temperature and leakage power. For instance, with Intel core i5-2500K (32 nm Sandy Bridge), the leakage power roughly grows up to 2× from 55°C (13 W) to 105°C (26 W), see Figure 1. Furthermore, the soaring chip temperature adversely impacts the performance, reliability, and packaging/cooling costs [17]. As a result, power and thermal issues have become critical and significant for advanced multi-core system design. In next section, we introduce some necessary backgrounds of multi-core scheduling with power and thermal awareness, respectively.

The aggressive semiconductor technology scaling has pushed the chip power density doubled every two to three years [16,20], which immediately results in an exponential increasing in heat density. High temperature can degrade the performance of systems in various ways. Therefore, there is a great need of advanced techniques for thermal/temperature aware design of multi-core systems.


2. Preliminary

2.1. Multi-core platform and task model

The multi-core platform consists of M processing cores, M ≥ 2, denoted as P, P = {P1,P2,…,PM}. Each core Pi has N running modes, each of which is characterized by a 2-tuple set (vk, fk), where vk represents the supply voltage and fk represents the frequency under mode k, 1 ≤ k ≤ N.

Let S represent a static and periodic speed schedule, which indicates how to vary the supply voltage and working frequency for each core at different time instants. A speed schedule S is constituted by several state intervals, which is described as below:

Definition 1 [25]: Given a speed schedule S for a multi-core system, an interval [tq−1, tq] is called a state interval if each core runs only at one mode during that interval.

Recall that speed schedule S is a periodic schedule, let L denote the length of one scheduling period. According to Definition 1, a speed schedule S essentially consists of a number of non-overlapped state intervals. Let Q represent the number of non-overlapped state intervals within one scheduling period of S, then we have that [25]

  1. q=1Q[tq1,tq]=[0,L]

  2. [tq1,tq][tp1,tp]=, if qp

For a single state interval [tq−1, tq], let Kq represent the interval mode, Kq = {k1,k2,…,kM}, where ki denotes the running mode of core Pi in interval [tq−1, tq].

2.2. Power model

The overall power consumption (in Watt) of each core is composed of two parts: dynamic power Pdyn and leakage power Pleak. We assume that: (1) Pdyn is varied with respect of supply voltage but independent of temperature, (2) Pleak is sensitive to both temperature and supply voltage. Specifically, for the dynamic power, we know that it is proportional to the square of supply voltage and linearity of working frequency. We assume that the working frequency is linearly proportional to supply voltage, thus the power consumption of the i-th core (Pi) under running mode ki can be formulated as below [18].


where γki is a constant value determined by the platform and running mode, and vki is the supply voltage of core Pi determined by the running mode.

For leakage power, although there is a very complicated relationship between leakage power and temperature from circuit level perspective, Liu et al. [23] found that a linear approximation of the leakage temperature dependency is fairly accurate. Work [12] further formulated the leakage power of core Pi as below:


where αki and βkiare constants depending on the core running mode, that is, mode ki.

Consequently, the total power consumption of core Pi at time t, denoted as Pi(t), can be formulated as:


For convenience in our presentation, we rewrite the above formula by separating the elements into temperature independent/dependent parts such that


whereψi=αki*vki+γki*vki3 and ϕi=βki*vki.




Note that we use the bold text for a vector/matrix and the unbolded text for a value, for example, T represents a temperature vector while T represents a temperature value.

2.3. Thermal model

Figure 2.

Illustration for thermal phenomena on multi-core system [26].

Figure 2 illustrates the thermal circuit model for a multi-core platform consisting of four processing cores. Ci represents the thermal capacitance (in Watt/°C) of core Pi, and Rij represents the thermal resistance (in J/°C) between core Pi and Pj. Note that the thermal model adopted here is similar to the one used in related work [19,21]. Let Tamb represent the ambient temperature, then the thermal phenomena of core Pi in Figure 2 can be formulated as



{δi=TambRiigij={k=1M1Rik, if j=i1Rij, otherwiseE8

Then the thermal model in equation (7) can be rewritten as


Accordingly, for the entire system, the thermal model can be represented as


where C and g are MxM matrices


and δ is an Mx1 vector


Note that C, g, and δ are all constants that are determined by the multi-core platform only. Moreover, C is the thermal capacitance matrix with none zero values only on the diagonal, and g is a thermal conductance matrix. The thermal model adopted here is a generic model which takes the heat transfer among different cores into consideration. Thus, it can be directly applied on thermal analysis for both temperature transient state and temperature stable state.


3. Temperature analysis in multi-core real-time systems

There is an interactive effect between power and temperature, that is, high power leads to high temperature, which in turn further aggravates the power consumption. In order to calculate the energy consumption accurately and efficiently, it is necessary to develop an efficient solution to calculate the temperature first.

In this section, we first present a temperature formulation within thermal transient state for a constant speed schedule interval. Then we present an analytical solution to calculate the temperature within thermal steady state for a periodic speed schedule.

3.1. Temperature analysis in system thermal transient state

In this subsection, we will formulate the temperature variation [26] within one state interval [tq−1, tq] in the system thermal transient state.

First, by applying power model given by equation (6) into thermal model given by equation (10), we can derive that


Then we simplify the above equation by letting G=gΦ. Thus the above equation can be rewritten as


Since C represents the capacitance matrix, according to the circuit nature, we know that matrix C contains no zero values only on its diagonal. Thus, we can see matrix C is non-singular. Therefore, the inverse of C, that is, C1 exists. Then we can further simplify equation (14) into below


where A=C1G and B=C1(Ψ+δ). The system thermal model formulated by equation (15) has a form of first order Ordinary Differential Equations (ODE). Particularly, if all coefficients are constant, then there exists a solution which can be formulated as


For a state interval [tq−1,tq], it is important to point out that all coefficients in the above are constant. Specifically, let Kq be the corresponding interval mode, and let T(tq−1) be the temperature at the starting point of that interval. Then according to equation (16), the ending temperature of that interval, that is, T(tq−1), can be directly formulated as


where Δtq=tqtq1.

3.2. Temperature analysis in system thermal steady state

In this subsection, we will formulate the temperature variation [26] within one state interval [tq−1, tq] in the system thermal steady state.

Consider a periodic speed schedule S, and let T(0) be the initial temperature at time instant 0. For an arbitrary state interval [tq−1, tq] within speed schedule S, to obtain its steady-state temperature, one intuitive way is to trace the entire schedule S by consecutively calculating the temperature from the first scheduling period until system reaches its thermal steady state.

Although this approach works from theoretical point of view, when considering the practical computational time cost (which would be extremely expensive), it turns out that this intuitive approach would be inefficient or even impractical. Thus, it would be desirable and useful to develop an efficient solution to rapidly calculate steady-state temperatures for a periodic speed schedule.

Figure 3.

A speed schedule within two scheduling periods.

Let us first consider the temperature variation at the end of each scheduling period, that is, t = nL, where n ≥ 1. Let the scheduling points of S(t) in the first period be t0,t1,…,ts, respectively. After repeating S(t), let the corresponding points in the second scheduling period be t0',t1',…,ts', respectively (see Figure 3). Note that

t0 = 0, t0' = ts = L, and ts' = 2L. According to equation (17), at time t1 and t1', we have


Subtract equation (18) from (19) on both sides, and simplify the result by applying Δt1=Δt1', t0=0 and t0'=L, we get


Follow the same trace of the above derivation, we have that


Since ts=L,ts'=2L, and let eΔtsAKseΔt2AK2eΔt1AK1=K, the last equation in (21) can be rewritten as


In the same way, we can construct that


where x = 1,2, …,n. Sum up the above n equations, we get


In the above, {Kx−1|x = 1,2,…,n} forms a matrix geometric sequence. If (IK) is invertible, then we have


Next, we consider the temperature variation for an arbitrary time instant when repeating a periodic speed schedule. Given a periodic speed schedule S(t), let tq (tq ∈[0,L]) be an arbitrary time instant within schedule S(t). Moreover, let's repeat S(t) for n times, where n ≥ 1. Let T(nL + tq) denote the temperature of T(tq) in the n-th scheduling period, by following the similar way of the above derivation, we can get that


where Kq=eΔtqAKqeΔt2AK2eΔt1AK1.

Until now, we have been able to formulate the temperature variation of an arbitrary time instant in the n-th scheduling period. Next, we will further formulate the temperature variation of an arbitrary time instant in the system steady state. Consider an arbitrary time instant, that is, tq , 0 ≤ tqL, within the first scheduling period. The brief idea of calculating the steady-state temperature corresponding to tq is to let n go to infinity in equation (27). We formally describe our method in Theorem 1.

Theorem 1 [25]: Given a periodic speed schedule S(t), let T(L) and T(tq) be the temperatures at time instant L and tq, tq ∈ [0,L], respectively. If for each eigenvalue λi of K, we have |λi| < 1, then the steady-state temperature corresponding to tq can be formulated as



First, based on equation (26), by letting n→+∞, the steady-state temperature of the q-th scheduling point in S(t) can be represented as


When n→+∞, the matrix sequence Kn converges if and only if |λi|< 1, for each eigenvalue λi of K [14]. Under this condition, we have limnKn=0. Moreover, if |λi|< 1 holds, then (I-K) is invertible. Thus, the steady-state temperature of the q-th scheduling point in S(t) can be further formulated as


Note that as n→+∞, unless the temperature runs away and causes the system to break down, we know that the system could eventually achieve its thermal steady state. That means for each eigenvalue λi of K, the condition of |λi|< 1 should always hold. Therefore, it is reasonable and practical to make such assumption shown in Theorem 1.


4. Energy analysis in multi-core real-time systems

Besides temperature, energy consumption is another important and challenging issue in the design of multi-core real-time systems. We have now been able to formulate the temperature variation in a multi-core system in the previous section. In this section, we will discuss how to formulate the energy consumption on multi-core systems with consideration of the interdependence between leakage power and temperature.

In the rest of this section, we first present an analytical solution to calculate energy consumption of an arbitrary state interval. Then we present a solution to calculate the total energy consumption of the entire speed schedule.

4.1. Energy analysis for one scheduling state interval

Consider a state interval, that is, [tq−1, tq] with initial temperature of T(tq−1). The energy consumption of all cores within that interval can be simply formulated as


Based on our system power model, given by equation (6), we have


Theorem 2 given below is targeted to solve the above energy calculation problem.

Theorem 2 [25]: Given a state interval [tq−1, tq], let Tq−1 be the temperature at time tq−1. Then the overall system energy consumption within interval [tq−1, tq] can be formulated as


where Δtq=tqtq1, andH=Δtq(Ψ+δ)C(T(tq)T(tq1)).


In equation (31), let X=tq1tqT(t)dt, then the energy formula can be rewritten as


On the other hand, according to the system thermal model given by equation (10), we have


where G=gΦ. By integrating on both sides of the above equation with respect to time t, where t ∈ [tq−1, tq], we can get


Note that X=tq1tqT(t)dt; thus from the above, we can derive that


where H=Δtq(Ψ+δ)C(T(tq)T(tq1))

Applying equation (36) into (33), we can see that


From Theorem 2, we can see that for an arbitrary state interval [tq−1, tq], once the beginning temperature T(tq−1) is known, the total energy consumption within [tq−1, tq] can be easily and directly calculated.

Subsequently, given a periodic speed schedule S and an initial temperature T0, we are able to calculate the energy consumption within an arbitrary state interval in any scheduling period.

Corollary 1 [25]: Given a periodic speed schedule S(t) consisting of Q state intervals, let T0 be the initial temperature. Then the energy consumption within the q-th state interval in the n-th scheduling period, denoted as E(tq−1 + nL, tq + nL), can be calculated as


where Δtq=tqtq1, andHKq=Δtq(ΨKq+δ)C(T(tq+nL)T(tq1+nL)).

Corollary 1 can be easily derived from Theorem 2. With the help of Corollary 1, given any periodic speed schedule on a multi-core platform, when repeating that schedule, we can quickly calculate the energy consumption within any state interval.

Accordingly, given a periodic speed schedule, when analyzing the system thermal steady state, we can directly calculate the energy consumption of any state interval within the system steady state.

Corollary 2 [25]: Given a periodic speed schedule S(t) consisting of Q state intervals, let T0 be the initial temperature. Then the energy consumption within the q-th state interval in the system steady state, denoted as Ess(tq−1, tq), can be calculated as


where Δtq=tqtq1, andHssKq=Δtq(ΨKq+δ)C(Tss(tq)Tss(tq1)).

Corollary 2 is directly derived from Corollary 1 by replacing the transient temperatures with steady-state temperatures.


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Written By

Ming Fan

Submitted: 26 October 2015 Reviewed: 22 March 2016 Published: 08 June 2016