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95
Dynamic speed scaling to manage energy and temperature
 In IEEE Syposium on Foundations of Computer Science
, 2004
"... We first consider online speed scaling algorithms to minimize the energy used subject to the constraint that every job finishes by its deadline. We assume that the power required to run at speed ¡ is ¢¤ £. We provide a tight bound on the competitive ratio of the previously proposed Optimal Availabl ..."
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Cited by 118 (14 self)
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We first consider online speed scaling algorithms to minimize the energy used subject to the constraint that every job finishes by its deadline. We assume that the power required to run at speed ¡ is ¢¤ £. We provide a tight bound on the competitive ratio of the previously proposed Optimal Available algorithm. This improves the best known competitive ratio by a factor � � of. We then introduce a new online algorithm, and show that this algorithm’s competitive ratio is at � £ �� � £ �¨����¥�¥����� � most. This competitive ratio is significantly better and is � ������� approximately for large �. Our result is essentially tight for large �. In particular, as � approaches infinity, we show that any algorithm must have competitive ratio �� � (up to lower order terms). We then turn to the problem of dynamic speed scaling to minimize the maximum temperature that the device ever reaches, again subject to the constraint that all jobs finish by their deadlines. We assume that the device cools according to Fourier’s law. We show how to solve this problem in polynomial time, within any error bound, using the Ellipsoid algorithm. 1.
Leakage aware dynamic voltage scaling for realtime embedded systems
 In CECS
, 2003
"... A fivefold increase in leakage current is predicted with each technology generation. While Dynamic Voltage Scaling (DVS) is known to reduce dynamic power consumption, it also causes increased leakage energy drain by lengthening the interval over which a computation is carried out. Therefore, for mi ..."
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Cited by 93 (8 self)
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A fivefold increase in leakage current is predicted with each technology generation. While Dynamic Voltage Scaling (DVS) is known to reduce dynamic power consumption, it also causes increased leakage energy drain by lengthening the interval over which a computation is carried out. Therefore, for minimization of the total energy, one needs to determine an operating point, called the critical speed. We compute processor slowdown factors based on the critical speed for energy minimization. Procrastination scheduling attempts to maximize the duration of idle intervals by keeping the processor in a sleep/shutdown state even if there are pending tasks, within the constraints imposed by performance requirements. Our simulation experiments show that the critical speed slowdown results in up to 5 % energy gains over a leakage oblivious dynamic voltage scaling. Procrastination scheduling scheme extends the sleep intervals to up to 5 times, resulting in up to an additional 18 % energy gains, while meeting all timing requirements.
Energy Consumption in Mobile Phones: A Measurement Study and Implications for Network Applications
, 2009
"... In this paper, we present a measurement study of the energy consumption characteristics of three widespread mobile networking technologies: 3G, GSM, and WiFi. We find that 3G and GSM incur a high tail energy overhead because of lingering in high power states after completing a transfer. Based on the ..."
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Cited by 84 (1 self)
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In this paper, we present a measurement study of the energy consumption characteristics of three widespread mobile networking technologies: 3G, GSM, and WiFi. We find that 3G and GSM incur a high tail energy overhead because of lingering in high power states after completing a transfer. Based on these measurements, we develop a model for the energy consumed by network activity for each technology. Using this model, we develop TailEnder, a protocol that reduces energy consumption of common mobile applications. For applications that can tolerate a small delay such as email, TailEnder schedules transfers so as to minimize the cumulative energy consumed while meeting userspecified deadlines. We show that the TailEnder scheduling algorithm is within a factor 2 × of the optimal and show that any online algorithm can at best be within a factor 1.62 × of the optimal. For applications like web search that can benefit from prefetching, TailEnder aggressively prefetches several times more data and improves userspecified response times while consuming less energy. We evaluate the benefits of TailEnder for three different case study applications—email, news feeds, and web search—based on real user logs and show significant reduction in energy consumption in each case. Experiments conducted on the mobile phone show that TailEnder can download 60 % more news feed updates and download search results for more than 50 % of web queries, compared to using the default policy.
EnergyEfficient Algorithms for . . .
, 2007
"... We study scheduling problems in batteryoperated computing devices, aiming at schedules with low total energy consumption. While most of the previous work has focused on finding feasible schedules in deadlinebased settings, in this article we are interested in schedules that guarantee good respons ..."
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Cited by 65 (2 self)
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We study scheduling problems in batteryoperated computing devices, aiming at schedules with low total energy consumption. While most of the previous work has focused on finding feasible schedules in deadlinebased settings, in this article we are interested in schedules that guarantee good response times. More specifically, our goal is to schedule a sequence of jobs on a variablespeed processor so as to minimize the total cost consisting of the energy consumption and the total flow time of all jobs. We first show that when the amount of work, for any job, may take an arbitrary value, then no online algorithm can achieve a constant competitive ratio. Therefore, most of the article is concerned with unitsize jobs. We devise a deterministic constant competitive online algorithm and show that
Getting the Best Response for Your Erg
"... We consider the speed scaling problem of minimizing the average response time of a collection of dynamically released jobs subject to a constraint A on energy used. We propose an algorithmic approach in which an energy optimal schedule is computed for a huge A, and then the energy optimal schedule ..."
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Cited by 50 (9 self)
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We consider the speed scaling problem of minimizing the average response time of a collection of dynamically released jobs subject to a constraint A on energy used. We propose an algorithmic approach in which an energy optimal schedule is computed for a huge A, and then the energy optimal schedule is maintained as A decreases. We show that this approach yields an efficient algorithm for equiwork jobs. We note that the energy optimal schedule has the surprising feature that the job speeds are not monotone functions of the available energy. We then explain why this algorithmic approach is problematic for arbitrary work jobs. Finally, we explain how to use the algorithm for equiwork jobs to obtain an algorithm for arbitrary work jobs that is O(1)approximate with respect to average response time, given an additional factor of (1 + ffl)energy.
A Survey of Techniques for Energy Efficient OnChip Communication
 Communication,” Proceedings of Design Automation Conference
, 2003
"... Interconnects have been shown to be a dominant source of energy consumption in modern day SystemonChip (SoC) designs. With a large (and growing) number of electronic systems being designed with battery considerations in mind, minimizing the energy consumed in onchip interconnects becomes crucial. ..."
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Cited by 38 (0 self)
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Interconnects have been shown to be a dominant source of energy consumption in modern day SystemonChip (SoC) designs. With a large (and growing) number of electronic systems being designed with battery considerations in mind, minimizing the energy consumed in onchip interconnects becomes crucial. Further, the use of nanometer technologies is making it increasingly important to consider reliability issues during the design of SoC communication architectures. Continued supply voltage scaling has led to decreased noise margins, making interconnects more susceptible to noise sources such as crosstalk, power supply noise, radiation induced defects, etc. The resulting transient faults cause the interconnect to behave as an unreliable transport medium for data signals. Therefore, fault tolerant communication mechanisms, such as Automatic Repeat Request (ARQ), Forward Error Correction (FEC), etc., which have been widely used in the networking community, are likely to percolate to the SoC domain.
Speed Scaling Functions for Flow Time Scheduling based on Active Job Count
"... Abstract. We study online scheduling to minimize flow time plus energy usage in the dynamic speed scaling model. We devise new speed scaling functions that depend on the number of active jobs, replacing the existing speed scaling functions in the literature that depend on the remaining work of activ ..."
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Cited by 31 (12 self)
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Abstract. We study online scheduling to minimize flow time plus energy usage in the dynamic speed scaling model. We devise new speed scaling functions that depend on the number of active jobs, replacing the existing speed scaling functions in the literature that depend on the remaining work of active jobs. The new speed functions are more stable and also more efficient. They can support better job selection strategies to improve the competitive ratios of existing algorithms [5,8], and, more importantly, to remove the requirement of extra speed. These functions further distinguish themselves from others as they can readily be used in the nonclairvoyant model (where the size of a job is only known when the job finishes). As a first step, we study the scheduling of batched jobs (i.e., jobs with the same release time) in the nonclairvoyant model and present the first competitive algorithm for minimizing flow time plus energy (as well as for weighted flow time plus energy); the performance is close to optimal. 1
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 24 (13 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
Speed scaling on parallel processors
 In Proc. 19th Annual Symp. on Parallelism in Algorithms and Architectures (SPAA’07
, 2007
"... In this paper we investigate algorithmic instruments leading to low power consumption in computing devices. While previous work on energyefficient algorithms has mostly focused on single processor environments, in this paper we investigate multiprocessor settings. We study the basic problem of sch ..."
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Cited by 24 (2 self)
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In this paper we investigate algorithmic instruments leading to low power consumption in computing devices. While previous work on energyefficient algorithms has mostly focused on single processor environments, in this paper we investigate multiprocessor settings. We study the basic problem of scheduling a set of jobs, each specified by a release time, a deadline and a processing volume, on variable speed processors so as to minimize the total energy consumption. We first settle the complexity of speed scaling with unit size jobs. More specifically, we devise a polynomial time algorithm for agreeable deadlines and prove NPhardness results for arbitrary release dates and deadlines. For the latter setting we also develop a polynomial time algorithm achieving a constant factor approximation guarantee that is independent of the number of processors. Additionally, we study speed scaling of jobs with arbitrary processing requirements and, again, develop constant factor approximation algorithms. We finally transform our offline algorithms into constant competitive online strategies.
Energy efficient online deadline scheduling
 In Proc. SODA
, 2007
"... Abstract. This paper extends the study of online algorithms for energyefficient deadline scheduling to the overloaded setting. Specifically, we consider a processor that can vary its speed between 0 and a maximum speed T to minimize its energy usage (of which the rate is roughly a cubic function of ..."
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Cited by 21 (12 self)
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Abstract. This paper extends the study of online algorithms for energyefficient deadline scheduling to the overloaded setting. Specifically, we consider a processor that can vary its speed between 0 and a maximum speed T to minimize its energy usage (of which the rate is roughly a cubic function of the speed). As the speed is upper bounded, the system may be overloaded with jobs and no scheduling algorithms can meet the deadlines of all jobs. An optimal schedule is expected to maximize the throughput, and furthermore, its energy usage should be the smallest among all schedules that achieve the maximum throughput. In designing a scheduling algorithm, one has to face the dilemma of selecting more jobs and being conservative in energy usage. Even if we ignore energy usage, the best possible online algorithm is 4competitive on throughput [12]. On the other hand, existing work on energyefficient scheduling focuses on minimizing the energy to complete all jobs on a processor with unbounded speed, giving several O(1)competitive algorithms with respect to the energy usage [2,20]. This paper presents the first online algorithm for the more realistic setting where processor speed is bounded and the system may be overloaded; the algorithm is O(1)competitive on both throughput and energy usage. If the maximum speed of the online scheduler is relaxed slightly to (1+ǫ)T for some ǫ> 0, we can improve the competitive ratio on throughput to arbitrarily close to one, while maintaining O(1)competitive on energy usage. 1