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137
Systemlevel Energy Management for Periodic RealTime Tasks
, 2006
"... In this paper, we consider the systemwide energy management problem for a set of periodic realtime tasks running on a DVSenabled processor. Our solution uses a generalized power model, in which frequencydependent and frequencyindependent power components are explicitly considered. Further, vari ..."
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Cited by 54 (25 self)
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In this paper, we consider the systemwide energy management problem for a set of periodic realtime tasks running on a DVSenabled processor. Our solution uses a generalized power model, in which frequencydependent and frequencyindependent power components are explicitly considered. Further, variations in power dissipations and onchip/offchip access patterns of different tasks are encoded in the problem formulation. Using this generalized power model, we show that it is possible to obtain analytically the tasklevel energyefficient speed below which DVS starts to affect overall energy consumption negatively. Then, we formulate the systemwide energy management problem as a nonlinear optimization problem and provide a polynomialtime solution. We also provide a dynamic slack reclaiming extension which considers the effects of slowdown on the systemwide energy consumption. Our experimental evaluation shows that the optimal solution provides significant (up to 50%) gains over the previous solutions that focused on dynamic CPU power at the expense of ignoring other power components.
Approximation algorithm for the temperatureaware scheduling problem
 In Proc. Int. Conf. ComputerAided Design
, 2007
"... Abstract — The paper addresses the problem of performance optimization for a set of periodic tasks with discrete voltage/frequency states under thermal constraints. We prove that the problem is NPhard, and present a pseudopolynomial optimal algorithm and a fully polynomial time approximation tech ..."
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Cited by 43 (1 self)
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Abstract — The paper addresses the problem of performance optimization for a set of periodic tasks with discrete voltage/frequency states under thermal constraints. We prove that the problem is NPhard, and present a pseudopolynomial optimal algorithm and a fully polynomial time approximation technique (FPTAS) for the problem. The FPTAS technique is able to generate solutions in polynomial time that are guaranteed to be within a designer specified quality bound (QB) (say within 1% of the optimal). We evaluate our techniques by experimentation with multimedia and synthetic benchmarks mapped on the 70nm CMOS technology processor. The experimental results demonstrate our techniques are able to match optimal solutions when QB is set at 5%, can generate solutions that are quite close to optimal (< 5%) even when QB is set at higher values (50%), and executes in few seconds (with QB> 25%) for large task sets with 120 nodes (while the optimal solution takes several hundred seconds). We also analyze the effect of different thermal parameters, such as the initial temperature, the final temperature and the thermal resistance. I.
ReliabilityAware Energy Management for Periodic RealTime Tasks
 in Proc. of the RealTime and Embedded Technology and Applications Symposium, 225–235, 2007. Absolute reliability (k = 2) 0.98 0.96 0.94 0.92 0.90 0.88 SS REO EO
"... The prominent energy management technique, Dynamic Voltage and Frequency Scaling (DVFS), was recently shown to have direct and adverse effects on system reliability. In this work, we investigate static and dynamic reliabilityaware energy management schemes for a set of periodic realtime tasks to m ..."
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Cited by 35 (19 self)
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The prominent energy management technique, Dynamic Voltage and Frequency Scaling (DVFS), was recently shown to have direct and adverse effects on system reliability. In this work, we investigate static and dynamic reliabilityaware energy management schemes for a set of periodic realtime tasks to minimize energy consumption while preserving system reliability. Focusing on EDF scheduling, we first show that the static problem is NPhard and propose two tasklevel utilizationbased heuristics. Then, we develop a joblevel dynamic (online) scheme by building on the idea of wrappertasks, to monitor and manage dynamic slack efficiently in reliabilityaware settings. Our schemes incorporate recovery tasks/jobs into the schedule as needed for reliability preservation, while still using the remaining slack for energy savings. Simulation results show that all the proposed schemes can achieve significant energy savings while preserving the system reliability. Moreover, the energy savings of the static heuristics are close to those of the static optimal solution by a margin of 5%. 1
Reliabilityaware dynamic energy management in dependable embedded realtime systems
 In Proceedings of the 12th IEEE RealTime and Embedded Technology and Applications Symposium
, 2006
"... Recent studies show that, voltage scaling, which is an efficient energy management technique, has a direct and negative effect on system reliability because of the increased rate of transient faults (e.g., those induced by cosmic particles). In this work, we propose energy management schemes that ex ..."
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Cited by 33 (17 self)
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Recent studies show that, voltage scaling, which is an efficient energy management technique, has a direct and negative effect on system reliability because of the increased rate of transient faults (e.g., those induced by cosmic particles). In this work, we propose energy management schemes that explicitly take system reliability into consideration. The proposed reliabilityaware energy management schemes dynamically schedule recoveries for tasks to be scaled down to recuperate the reliability loss due to energy management. Based on the amount of available slack, the application size and the fault rate changes, we analyze when it is profitable to reclaim the slack for energy savings without sacrificing system reliability. Checkpoint technique is further explored to efficiently use the slack. Analytical and simulation results show that, the proposed schemes can achieve comparable energy savings as ordinary energy management schemes while preserving system reliability. The ordinary energy management schemes that ignore the effects of voltage scaling on fault rate changes could lead to drastically decreased system reliability.
Energy Management for RealTime Embedded Systems with Reliability Requirements
, 2006
"... With the continued scaling of CMOS technologies and reduced design margins, the reliability concerns induced by transient faults have become prominent. Moreover, the popular energy management technique dynamic voltage and frequency scaling (DVFS) has been shown to have direct and negative effects on ..."
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Cited by 32 (13 self)
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With the continued scaling of CMOS technologies and reduced design margins, the reliability concerns induced by transient faults have become prominent. Moreover, the popular energy management technique dynamic voltage and frequency scaling (DVFS) has been shown to have direct and negative effects on reliability. In this work, for a set of realtime tasks, we focus on the slack allocation problem to minimize their energy consumption while preserving the overall system reliability. Building on our previous findings for a single realtime application where a recovery task was used to preserve reliability, we identify the problem of reliabilityaware energy management for multiple tasks as NPhard and propose two polynomialtime heuristic schemes. We also investigate the effects of onchip/offchip workload decomposition on energy management, by considering a generalized power model. Simulation results show that ordinary energy management schemes could lead to drastically decreased system reliability, while the proposed reliabilityaware heuristic schemes are able to preserve the system reliability and obtain significant energy savings at the same time.
B.: LeakageAware Multiprocessor Scheduling for Low Power
 In: Proc. Int. Parallel and Distributed Processing Symp. (2006
"... It is expected that (single chip) multiprocessors will increasingly be deployed to realize highperformance embedded systems. Because in current technologies the dynamic power consumption dominates the static power dissipation, an effective technique to reduce energy consumption is to employ as many ..."
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Cited by 30 (2 self)
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It is expected that (single chip) multiprocessors will increasingly be deployed to realize highperformance embedded systems. Because in current technologies the dynamic power consumption dominates the static power dissipation, an effective technique to reduce energy consumption is to employ as many processors as possible in order to finish the tasks as early as possible, and to use the remaining time before the deadline (the slack) to apply voltage scaling. We refer to this heuristic as Schedule and Stretch (S&S). However, since the static power consumption is expected to become more significant, this approach will no longer be efficient when leakage current is taken into account. In this paper, we first show for which combinations of leakage current, supply voltage, and clock frequency the static power consumption dominates the dynamic power dissipation. These results imply that, at a certain point, it is no longer advantageous from an energy perspective to employ as many processors as possible. Thereafter, a heuristic is presented to schedule the tasks on a number of processors that minimizes the total energy consumption. Experimental results obtained using a public task graph benchmark set show that our leakageaware scheduling algorithm reduces the total energy consumption by up to 24 % for tight deadlines (1.5x the critical path length) and by up to 67% for loose deadlines (8x the critical path length) compared to S&S. 1
Systemlevel energyefficient dynamic task scheduling
 in 42nd DAC
, 2005
"... Dynamic voltage scaling (DVS) is a wellknown low power design technique that reduces the processor energy by slowing down the DVS processor and stretching the task execution time. But in a DVS system consisting of a DVS processor and multiple devices, slowing down the processor increases the device ..."
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Cited by 26 (2 self)
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Dynamic voltage scaling (DVS) is a wellknown low power design technique that reduces the processor energy by slowing down the DVS processor and stretching the task execution time. But in a DVS system consisting of a DVS processor and multiple devices, slowing down the processor increases the device energy consumption and thereby the systemlevel energy consumption. In this paper, we present dynamic task scheduling algorithms for periodic tasks that minimize the systemlevel energy (CPU energy + device standby energy). The algorithms use a combination of (i) optimal speed setting, which is the speed that minimizes the system energy for a specific task, and (ii) limited preemption which reduces the numbers of possible preemptions. For the case when the CPU power and device power are comparable, these algorithms achieve up to 43 % energy savings compared to [1], but only up to 12 % over the nonDVS scheduling. If the device power is large compared to the CPU power, we show that DVS should not be employed.
Procrastination determination for periodic realtime tasks in leakageaware dynamic voltage scaling systems
 In ICCAD
, 2007
"... Many computing systems have adopted the dynamic voltage scaling (DVS) technique to reduce energy consumption by slowing down operation speed. However, the longer a job executes, the more energy in leakage current the processor consumes for the job. To reduce the power/energy consumption from the le ..."
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Cited by 20 (5 self)
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Many computing systems have adopted the dynamic voltage scaling (DVS) technique to reduce energy consumption by slowing down operation speed. However, the longer a job executes, the more energy in leakage current the processor consumes for the job. To reduce the power/energy consumption from the leakage current, a processor can enter the dormant mode. Existing research results for leakageaware DVS scheduling perform procrastination of realtime jobs greedily so that the idle time can be aggregated as long as possible to turn off the processor. This paper proposes algorithms for the procrastination determination of periodic realtime tasks in uniprocessor systems. Instead of greedy procrastination, the procrastination procedures are applied only when the evaluated energy consumption is less than not procrastination. Evaluation results show that our proposed algorithms could derive energyefficient solutions and outperform existing algorithms.
Task Scheduling for Control Oriented Requirements for CyberPhysical Systems
"... The wide applications of cyberphysical systems (CPS) call for effective design strategies that optimize the performance of both computing units and physical plants. We study the task scheduling problem for a class of CPS whose behaviors are regulated by feedback control laws. We codesign the contro ..."
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Cited by 19 (3 self)
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The wide applications of cyberphysical systems (CPS) call for effective design strategies that optimize the performance of both computing units and physical plants. We study the task scheduling problem for a class of CPS whose behaviors are regulated by feedback control laws. We codesign the control law and the task scheduling algorithm for predictable performance and power consumption for both the computing and the physical systems. We use a typical example, multiple inverted pendulums controlled by one processor, to illustrate our method. 1.
Procrastination Scheduling in Fixed Priority RealTime Systems
 In Proceedings of the Language Compilers and Tools for Embedded Systems
, 2004
"... Procrastination scheduling has gained importance for energy efficiency due to the rapid increase in the leakage power consumption. Under procrastination scheduling, task executions are delayed to extend processor shutdown intervals, thereby reducing the idle energy consumption. We propose algorithms ..."
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Cited by 19 (2 self)
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Procrastination scheduling has gained importance for energy efficiency due to the rapid increase in the leakage power consumption. Under procrastination scheduling, task executions are delayed to extend processor shutdown intervals, thereby reducing the idle energy consumption. We propose algorithms to compute the maximum procrastination intervals for tasks scheduled by either the fixed priority or the dual priority scheduling policy. We show that dual priority scheduling always guarantees longer shutdown intervals than fixed priority scheduling. We further combine procrastination scheduling with dynamic voltage scaling to minimize the total static and dynamic energy consumption of the system. Our simulation experiments show that the proposed algorithms can extend the sleep intervals up to 5 times while meeting the timing requirements. The results show up to 18% energy gains over dynamic voltage scaling.