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52
Algorithms for power savings
 In SODA ’03: Proceedings of the fourteenth annual ACMSIAM symposium on Discrete algorithms
, 2003
"... This paper examines two di erent mechanisms for saving power in batteryoperated embedded systems. The rst is that the system can be placed in a sleep state if it is idle. However, a xed amount of energy is required to bring the system back into an active state in which it can resume work. The secon ..."
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Cited by 129 (6 self)
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This paper examines two di erent mechanisms for saving power in batteryoperated embedded systems. The rst is that the system can be placed in a sleep state if it is idle. However, a xed amount of energy is required to bring the system back into an active state in which it can resume work. The second way inwhichpower savings can be achieved is by varying the speed at which jobs are run. We utilize a power consumption curve P (s) whichindicates the power consumption level given a particular speed. We assume that P (s) isconvex, nondecreasing and nonnegative for s 0. The problem is to schedule arriving jobs in a way that minimizes total energy use and so that each job is completed after its release time and before its deadline. We assume that all jobs can be preempted and resumed at no cost. Although each problem has been considered separately, this is the rst theoretical analysis of systems that can use both mechanisms. We givean o ine algorithm that is within a factor of two of the optimal algorithm. We alsogivean online algorithm with a constant competitive ratio. 1
Algorithmic problems in power management
 SIGACT News
, 2005
"... We survey recent research that has appeared in the theoretical computer science literature on algorithmic ..."
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Cited by 72 (4 self)
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We survey recent research that has appeared in the theoretical computer science literature on algorithmic
Optimal PowerDown Strategies
"... We consider the problem of selecting threshold times to transition a device to lowpower sleep states during an idle period. The twostate case in which there is a single active and a single sleep state is a continuous version of the skirental problem. We consider a generalized version in which the ..."
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Cited by 41 (1 self)
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We consider the problem of selecting threshold times to transition a device to lowpower sleep states during an idle period. The twostate case in which there is a single active and a single sleep state is a continuous version of the skirental problem. We consider a generalized version in which there is more than one sleep state, each with its own power consumption rate and transition costs. We give an algorithm that, given a system, produces a deterministic strategy whose competitive ratio is arbitrarily close to optimal. We also give an algorithm to produce the optimal online strategy given a system and a probability distribution that generates the length of the idle period. We also give a simple algorithm that achieves a competitive ratio of 3 + 2 √ 2 ≈ 5.828 for any system.
A Comprehensive Approach to DRAM Power Management
"... This paper describes a comprehensive approach for using the memory controller to improve DRAM energy efficiency and manage DRAM power. We make three contributions: (1) we describe a simple powerdown policy for exploiting low power modes of modern DRAMs; (2) we show how the idea of adaptive history ..."
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Cited by 34 (0 self)
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This paper describes a comprehensive approach for using the memory controller to improve DRAM energy efficiency and manage DRAM power. We make three contributions: (1) we describe a simple powerdown policy for exploiting low power modes of modern DRAMs; (2) we show how the idea of adaptive historybased memory schedulers can be naturally extended to manage power and energy; and (3) for situations in which additional DRAM power reduction is needed, we present a throttling approach that arbitrarily reduces DRAM activity by delaying the issuance of memory commands. Using detailed microarchitectural simulators of the IBM Power5+ and a DDR2533 SDRAM, we show that our first two techniques combine to increase DRAM energy efficiency by an average of 18.2%, 21.7%, 46.1%, and 37.1 % for the Stream, NAS, SPEC2006fp, and commercial benchmarks, respectively. We also show that our throttling approach provides performance that is within 4.4 % of an idealized oracular approach. 1
Thermal modeling, analysis, and management in VLSI circuits: principles and methods
 Proceedings of the IEEE
, 2006
"... The growing packing density and power consumption of VLSI circuits have made thermal effects one of the most important concerns of VLSI designers. The increasing variability of key process parameters in nanometer CMOS technologies has resulted in larger impact of the substrate and metal line tempera ..."
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Cited by 31 (3 self)
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The growing packing density and power consumption of VLSI circuits have made thermal effects one of the most important concerns of VLSI designers. The increasing variability of key process parameters in nanometer CMOS technologies has resulted in larger impact of the substrate and metal line temperatures on the reliability and performance of the devices and interconnections. Recent data shows that more than 50 % of all IC failures are related to thermal issues. This article presents a brief discussion of key sources of power dissipation and their temperature relation in CMOS VLSI circuits, and techniques for fullchip temperature calculation with especial attention to its implications on the design of highperformance, low power VLSI circuits. The article is concluded with an overview of techniques to improve the fullchip thermal integrity by means of offchip vs. onchip and static vs. adaptive methods.
An overview of competitive and adversarial approaches to designing dynamic power management strategies
 IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS
, 2005
"... Dynamic power management (DPM) refers to the problem of judicious application of various lowpower techniques based on runtime conditions in an embedded system to minimize the total energy consumption. To be effective, often such decisions take into account the operating conditions and the systeml ..."
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Cited by 17 (0 self)
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Dynamic power management (DPM) refers to the problem of judicious application of various lowpower techniques based on runtime conditions in an embedded system to minimize the total energy consumption. To be effective, often such decisions take into account the operating conditions and the systemlevel design goals. DPM has been a subject of intense research in the past decade driven by the need for low power consumption in modern embedded devices. We present a comprehensive overview of two closely related approaches to designing DPM strategies, namely, competitive analysis approach and model checking approach based on adversarial modeling. Although many other approaches exist for solving the systemlevel DPM problem, these two approaches are closely related and are based on a common theme. This commonality is in the fact that the underlying model is that of a competition between the system and an adversary. The environment that puts service demands on devices is viewed as an adversary, or to be in competition with the system to make it burn more energy, and the DPM strategy is employed by the system to counter that.
Polynomial Time Algorithms for Minimum Energy Scheduling
, 908
"... The aim of power management policies is to reduce the amount of energy consumed by computer systems while maintaining satisfactory level of performance. One common method for saving energy is to simply suspend the system during the idle times. No energy is consumed in the suspend mode. However, the ..."
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Cited by 15 (2 self)
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The aim of power management policies is to reduce the amount of energy consumed by computer systems while maintaining satisfactory level of performance. One common method for saving energy is to simply suspend the system during the idle times. No energy is consumed in the suspend mode. However, the process of waking up the system itself requires a certain fixed amount of energy, and thus suspending the system is beneficial only if the idle time is long enough to compensate for this additional energy expenditure. In the specific problem studied in the paper, we have a set of jobs with release times and deadlines that need to be executed on a single processor. Preemptions are allowed. The processor requires energy L to be woken up and, when it is on, it uses one unit of energy per one unit of time. It has been an open problem whether a schedule minimizing the overall energy consumption can be computed in polynomial time. We solve this problem in positive, by providing an O(n5)time algorithm. In addition we provide an O(n4)time algorithm for computing the minimum energy schedule when all jobs have unit length. 1
Deadline Scheduling and Power Management for Speed Bounded Processors
"... Energy consumption has become an important issue in the study of processor scheduling. Energy reduction can be achieved by allowing a processor to vary the speed dynamically (dynamic speed scaling) [2–4, 7, 10] or to enter a sleep state [1, 5, 8]. In the past, these two mechanisms are often studied ..."
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Cited by 13 (1 self)
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Energy consumption has become an important issue in the study of processor scheduling. Energy reduction can be achieved by allowing a processor to vary the speed dynamically (dynamic speed scaling) [2–4, 7, 10] or to enter a sleep state [1, 5, 8]. In the past, these two mechanisms are often studied separately. It is indeed natural to consider an integrated model in which a
Routing for Energy Minimization in the Speed Scaling Model
"... Abstract—We study network optimization that considers energy minimization as an objective. Studies have shown that mechanisms such as speed scaling can significantly reduce the power consumption of telecommunication networks by matching the consumption of each network element to the amount of proces ..."
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Cited by 11 (0 self)
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Abstract—We study network optimization that considers energy minimization as an objective. Studies have shown that mechanisms such as speed scaling can significantly reduce the power consumption of telecommunication networks by matching the consumption of each network element to the amount of processing required for its carried traffic. Most existing research on speed scaling focuses on a single network element in isolation. We aim for a networkwide optimization. Specifically, we study a routing problem with the objective of provisioning guaranteed speed/bandwidth for a given demand matrix while minimizing energy consumption. Optimizing the routes critically relies on the characteristic of the energy curve f(s), which is how energy is consumed as a function of the processing speed s. If f is superadditive, we show that there is no bounded approximation in general for integral routing, i.e., each traffic demand follows a single path. This contrasts with the wellknown logarithmic approximation for subadditive functions. However, for common energy curves such as polynomials f(s) = µs α, we are able to show a constant approximation via a simple scheme of randomized rounding. The scenario is quite different when ajnonzero startup cost σ 0 if s = 0 appears in the energy curve, e.g. f(s) = σ + µs α if s> 0. For this case a constant approximation is no longer feasible. In fact, for any α> 1, we show an Ω(log 1 4 N) hardness result under a common complexity assumption. (Here N is the size of the network.) On the positive side we present O((σ/µ) 1/α) and O(K) approximations, where K is the number of demands. I.
Sleep with Guilt and Work Faster to Minimize Flow plus Energy
"... Abstract. In this paper we extend the study of flowenergy scheduling to a model that allows both sleep management and speed scaling. Our main result is a sleep management algorithm called IdleLonger, which works online for a processor with one or multiple levels of sleep states. The design of IdleL ..."
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Cited by 9 (6 self)
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Abstract. In this paper we extend the study of flowenergy scheduling to a model that allows both sleep management and speed scaling. Our main result is a sleep management algorithm called IdleLonger, which works online for a processor with one or multiple levels of sleep states. The design of IdleLonger is interesting; among others, it may force the processor to idle or even sleep even though new jobs have already arrived. IdleLonger works in both clairvoyant and nonclairvoyant settings. We show how to adapt two existing speed scaling algorithms AJC [15] (clairvoyant) and LAPS [9] (nonclairvoyant) to the new model. The adapted algorithms, when coupled with IdleLonger, are shown to be O(1)competitive clairvoyant and nonclairvoyant algorithms for minimizing flow plus energy on a processor that allows sleep management and speed scaling. The above results are based on the traditional model with no limit on processor speed. If the processor has a maximum speed, the problem becomes more difficult as the processor, once overslept, cannot rely on unlimited extra speed to catch up the delay. Nevertheless, we are able to enhance IdleLonger and AJC so that they remain O(1)competitive for flow plus energy under the bounded speed model. Nonclairvoyant scheduling in the bounded speed model is left as an open problem. 1