Reducing Power with Performance Constraints for Parallel Sparse Applications

Chen, G.; Malkowski, Konrad; Kandemir, Mahmut; Raghavan, Padma

doi:10.1109/ipdps.2005.378

Cited by 54 publications

(41 citation statements)

References 21 publications

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“…Thus, DVS saves energy by switching between processor's voltages/frequencies to execute errands during slack times. Although DVS was in the beginning planned for task scheduling on single processors and not long been extended and used in parallel and distributed computing systems as well [6,7].…”

Section: Dynamic Voltage Frequency Scaling (Dvs)mentioning

confidence: 99%

“…RDVS is a simplified version of the algorithm introduced by Kimura et al in [7] for power-scalable high performance clusters at the bottom of DVS. It reduces the power utilization of processors by selecting the negligible obtainable processor incidence competent of concluding a job in a known time casing.…”

Section: Reference Dynamic Voltage-frequency Scaling (Rdvfs)mentioning

confidence: 99%

“…We have knowledgeable our algorithm with in cooperation random and real-world application job graphs and compared with the consequences in preceding research in [7] and [11]. The RDVS algorithm decreases task implementation power by choosing the most excellent processor's speed with respect to the task's unoccupied time [7]. The solution idea of MFS-DVS is to carry out tasks by means of a linear grouping of obtainable frequencies so that their slack times are fully filled / enclosed MFS-DVS can be defined.…”

Section: Multiple Frequency Selection For Dynamic Voltage-frequency Smentioning

confidence: 99%

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A Metaphorical Research on Diversified Approaches of DVS Enabled Energy - Aware Cloud Data Centers under the Cloudsim Platform

C.Saranya¹

2015

IJIRCCE

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Energy has turn into one of the majority critical issues. Energy-efficient designs encompass engage in recreation significance roles for hardware as fine as software implementations. Energy-efficiency can be achieved by adopting the dynamic voltage scaling (DVS) scheme. DVS has been a solution scheme in exploiting to reduce energy dissipation by lowering the supply voltage and operating occurrence. In this dissertation we anticipated diversified approaches on DVS enabled Energy aware Cloud Data centers. DVS has implemented on every approaches with different mechanisms and it provides energy saving and different advantaged features of the system. The scrutiny of research would give the instruction concerning what has been done previously in the same area, what is the in attendance tendency and what are the other related areas, Comparison of algorithms imposed with DVS features under the Cloudsim. The analysis has resultant that the DVS has reduced the power and improve the system utilization.

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Section: Dynamic Voltage Frequency Scaling (Dvs)mentioning

confidence: 99%

Section: Reference Dynamic Voltage-frequency Scaling (Rdvfs)mentioning

confidence: 99%

Section: Multiple Frequency Selection For Dynamic Voltage-frequency Smentioning

confidence: 99%

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A Metaphorical Research on Diversified Approaches of DVS Enabled Energy - Aware Cloud Data Centers under the Cloudsim Platform

C.Saranya¹

2015

IJIRCCE

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“…Thus, it is possible to reduce energy consumption by leveling down the processor supply voltage during non-critical execution of tasks [15].…”

Section: B Energy Reduction Via Dvfs Techniquementioning

confidence: 99%

Towards Energy Aware Scheduling for Precedence Constrained Parallel Tasks in a Cluster with DVFS

Wang

Laszewski

Dayal

et al. 2010

2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing

187

108

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Abstract-Reducing energy consumption for high end computing can bring various benefits such as, reduce operating costs, increase system reliability, and environment respect. This paper aims to develop scheduling heuristics and to present application experience for reducing power consumption of parallel tasks in a cluster with the Dynamic Voltage Frequency Scaling (DVFS) technique. In this paper, formal models are presented for precedence-constrained parallel tasks, DVFS enabled clusters, and energy consumption. This paper studies the slack time for non-critical jobs, extends their execution time and reduces the energy consumption without increasing the task's execution time as a whole. Additionally, Green Service Level Agreement is also considered in this paper. By increasing task execution time within an affordable limit, this paper develops scheduling heuristics to reduce energy consumption of a tasks execution and discusses the relationship between energy consumption and task execution time. Models and scheduling heuristics are examined with a simulation study. Test results justify the design and implementation of proposed energy aware scheduling heuristics in the paper.

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“…Then it is possible to reduce the processor supply voltage during non-critical execution of tasks [18].…”

Section: A Power-aware Cluster Computingmentioning

confidence: 99%

Power-aware scheduling of virtual machines in DVFS-enabled clusters

Laszewski

Wang

Younge

et al. 2009

2009 IEEE International Conference on Cluster Computing and Workshops

226

136

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Abstract-With the advent of Cloud computing, large-scale virtualized compute and data centers are becoming common in the computing industry. These distributed systems leverage commodity server hardware in mass quantity, similar in theory to many of the fastest Supercomputers in existence today. However these systems can consume a cities worth of power just to run idle, and require equally massive cooling systems to keep the servers within normal operating temperatures. This produces CO2 emissions and significantly contributes to the growing environmental issue of Global Warming. Green computing, a new trend for high-end computing, attempts to alleviate this problem by delivering both high performance and reduced power consumption, effectively maximizing total system efficiency. This paper focuses on scheduling virtual machines in a compute cluster to reduce power consumption via the technique of Dynamic Voltage Frequency Scaling (DVFS). Specifically, we present the design and implementation of an efficient scheduling algorithm to allocate virtual machines in a DVFS-enabled cluster by dynamically scaling the supplied voltages. The algorithm is studied via simulation and implementation in a multi-core cluster. Test results and performance discussion justify the design and implementation of the scheduling algorithm.

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Reducing Power with Performance Constraints for Parallel Sparse Applications

Cited by 54 publications

References 21 publications

A Metaphorical Research on Diversified Approaches of DVS Enabled Energy - Aware Cloud Data Centers under the Cloudsim Platform

A Metaphorical Research on Diversified Approaches of DVS Enabled Energy - Aware Cloud Data Centers under the Cloudsim Platform

Towards Energy Aware Scheduling for Precedence Constrained Parallel Tasks in a Cluster with DVFS

Power-aware scheduling of virtual machines in DVFS-enabled clusters

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