Speed Scaling Functions for Flow Time Scheduling Based on Active Job Count

Lam, Tak-Wah; Lee, Lap-Kei; To, Isaac K.; Wong, Prudence W. H.

doi:10.1007/978-3-540-87744-8_54

Cited by 55 publications

(60 citation statements)

References 25 publications

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“…[4], [5] showed that a natural online algorithm is 2-competitive for the objective of a linear combination of energy and total (unweighted) (integer) delay. Previously, [3], [6], [7], [8], [9], [10], [11] gave online algorithms with competitive analyses in the case that the power function was of the form s α . [12] first introduced the energy-delay product as a metric, and its use has been prevalent since then.…”

Section: Related Resultsmentioning

confidence: 99%

Optimal energy trade-off schedules

Barcelo

Cole

Letsios

et al. 2013

Sustainable Computing: Informatics and Systems

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Abstract-We consider scheduling tasks that arrive over time on a speed scalable processor. At each time a schedule specifies a job to be run and the speed at which the processor is run. Processors are generally less energy efficient at higher speeds. We seek to understand the structure of schedules that optimally trade-off the energy used by the processor with a common scheduling quality of service measure, fractional weighted delay. We assume that there is some user defined parameter β specifying the user's desired additive trade-off between energy efficiency and quality of service. We prove that the optimal energy trade-off schedule is essentially unique, and has a simple structure. Thus it is easy to check the optimality of a schedule. We further prove that the optimal energy trade-off schedule changes continuously as a function of the parameter β. Thus it is possible to compute the optimal energy trade-off schedule using a natural homotopic optimization algorithm. We further show that multiplicative trade-off schedules have fewer desirable properties.

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Section: Related Resultsmentioning

confidence: 99%

Optimal energy trade-off schedules

Barcelo

Cole

Letsios

et al. 2013

Sustainable Computing: Informatics and Systems

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show abstract

“…Our study may also serve as a basis for the development of the online algorithms for problems with the immediate start condition. Notice that the online versions of the traditional models of power-aware scheduling, without immediate start, are proposed by Albers and Fujiwara (2007), Bansal et al (2010), Chan et al (2013), Lam et al (2008Lam et al ( , 2012.…”

Section: Discussionmentioning

confidence: 99%

Models and algorithms for energy-efficient scheduling with immediate start of jobs

Shioura

Shakhlevich

Strusevich

et al. 2017

J Sched

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We study a scheduling model with speed scaling for machines and the immediate start requirement for jobs. Speed scaling improves the system performance, but incurs the energy cost. The immediate start condition implies that each job should be started exactly at its release time. Such a condition is typical for modern Cloud computing systems with abundant resources. We consider two cost functions, one that represents the quality of service and the other that corresponds to the cost of running. We demonstrate that the basic scheduling model to minimize the aggregated cost function with n jobs is solvable in O(n log n) time in the singlemachine case and in O(n 2 m) time in the case of m parallel machines. We also address additional features, e.g., the cost of job rejection or the cost of initiating a machine. In the case of a single machine, we present algorithms for minimizing one of the cost functions subject to an upper bound on the value of the other, as well as for finding a Pareto-optimal solution.

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“…We adopt the potential function used by Lam et al [23] in the context of online speed scaling and tailor it to suit the mean response time analysis of two-level adaptive schedulers. Specifically, at any time t, let n t (z) denote the number of jobs whose remaining accounted allocation is at least γ 1 z under Agdeq, i.e., n t (z) =…”

Section: Performance Of Agdeqmentioning

confidence: 99%

“…The analysis of Equi and Laps employs a technique called amortized local competitive argument, which bounds the amortized performance of an algorithm at any local time through a carefully designed potential function, and it has become a useful technique for analyzing scheduling algorithms (see, e.g. [28,27,5,4,23,11]). In this paper, we extend the amortized local competitive argument and provide a simple framework to analyze the mean response time for a set of perhaps less well-known but also quite effective schedulers called two-level adaptive schedulers [1,18,32].…”

Section: Introductionmentioning

confidence: 99%

Competitive Two-Level Adaptive Scheduling Using Resource Augmentation

Sun

Cao

Hsu

2009

Job Scheduling Strategies for Parallel Processing

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Abstract. As multi-core processors proliferate, it has become more important than ever to ensure efficient execution of parallel jobs on multiprocessor systems. In this paper, we study the problem of scheduling parallel jobs with arbitrary release time on multiprocessors while minimizing the jobs' mean response time. We focus on non-clairvoyant scheduling schemes that adaptively reallocate processors based on periodic feedbacks from the individual jobs. Since it is known that no deterministic non-clairvoyant algorithm is competitive for this problem, we focus on resource augmentation analysis, and show that two adaptive algorithms, Agdeq and Abgdeq, achieve competitive performance using O(1) times faster processors than the adversary. These results are obtained through a general framework for analyzing the mean response time of any twolevel adaptive scheduler. Our simulation results verify the effectiveness of Agdeq and Abgdeq by evaluating their performances over a wide range of workloads consisting of synthetic parallel jobs with different parallelism characteristics.

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Speed Scaling Functions for Flow Time Scheduling Based on Active Job Count

Cited by 55 publications

References 25 publications

Optimal energy trade-off schedules

Optimal energy trade-off schedules

Models and algorithms for energy-efficient scheduling with immediate start of jobs

Competitive Two-Level Adaptive Scheduling Using Resource Augmentation

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