Resource allocation to conserve energy in distributed computing

Lynar, Timothy; Herbert, Ric D.; Chivers, Simon; Chivers, William

doi:10.1504/ijguc.2011.039976

Cited by 18 publications

(8 citation statements)

References 17 publications

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“…Besides the performance perspective that represents final application time, we also compute the energy and the cost metrics. Particularly, energy consumption in computing is an important issue due to the increasing energy cost (Lynar et al, 2011;Lynar et al, 2013). Thus, we investigate the energy consumption by measuring resource allocation.…”

Section: Evaluation Methodologymentioning

confidence: 99%

“…Thus, scientific computing has historically been dependent on the advances of High Performance Computing (HPC) and parallel processing. In general, supercomputers, clusters and grids have a fixed number of resources that must be maintained in terms of infrastructure configuration, scheduling (where tools such as PBS 1 , OAR 2 , OGS 3 are usually employed for resource reservation and job scheduling) and energy consumption (Weidner et al, 2016;Lynar et al, 2011;Lynar et al, 2013;Lorido-Botrán et al, 2012;Harvey et al, 2016). In addition, optimising the number of processes to execute a HPC application can be a hard procedure: (i) both short and large values will not explore the distributed system in an efficient way; (ii) a fixed value cannot fit irregular applications, where the workload varies along the execution and/or it is not predictable in advance.…”

Section: Introductionmentioning

confidence: 99%

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Towards providing middleware-level proactive resource reorganisation for elastic HPC applications in the cloud

Righi

Rodrigues

Nardin

et al. 2019

IJGUC

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Elasticity is one of the most important features of cloud computing, referring to the ability to add or remove resources according to the needs of the application or service. Particularly for High Performance Computing (HPC), elasticity can provide a better use of resources and also a reduction in the execution time of applications. Today, we observe the emergence of proactive initiatives to handle the elasticity and HPC duet, but they present at least one problem related to the need of a previous user experience, large processing time or completion of parameters. Concerning the aforesaid context, this paper presents ProElastic-a lightweight model that uses proactive elasticity to drive resource reorganisation decisions for HPC applications. Our idea is to explore both performance and adaptivity at middleware level in an effortless way at user perspective. The results showed performance gains and a competitive cost (application time  consumed resources).

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Section: Evaluation Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards providing middleware-level proactive resource reorganisation for elastic HPC applications in the cloud

Righi

Rodrigues

Nardin

et al. 2019

IJGUC

View full text Add to dashboard Cite

show abstract

“…various levels of heterogeneity, fault tolerance, strong energy consumption constraints, it is mandatory to move towards an energy-aware resource management [22], including scheduling algorithms that are able to handle various levels of heterogeneity and the diversity of available resources [73].…”

Section: Energy-aware Scheduling Algorithmsmentioning

confidence: 99%

Energy-efficient Algorithms for Ultrascale Systems

Carretero

Distefano

Petcu

et al. 2015

JSFI

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The chances to reach Exascale or Ultrascale Computing are strongly connected with the problem of the energy consumption for processing applications. For physical and economical reasons, the energy consumption has to be reduced significantly to make Ultrascale Computing possible. The research efforts towards energy-saving mechanisms of the hardware have already made energy-aware hardware systems available. However, to achieve a strong energy reduction, hardware mechanisms must be complemented with new energy-efficient software that can exploit them so that the foreseen energy savings actually result. In the software area, there also exist a multitude of research approaches towards energy saving, often concentrating either on the system software level or the application organization level, reflecting the expertise of the corresponding research group. The challenge of reducing the energy consumption dramatically to make Ultrascale Computing possible is so ambitious that a concerted action combining research efforts through all the software levels seems reasonable. In this article, we discuss the current research efforts and results related to energy efficiency in the diverse areas of software. We conclude with open problems and questions concerning energy-related techniques with an emphasis on the application or algorithmic side.

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“…Therefore, VFS-enabled systems perform better in energy saving in such environments. Using VFS at CPU level can save a lot of energy by scaling down the CPU frequency either manually [36,86] or dynamically (DVFS) [20, 30, 41-43, 55-57, 62, 80] by means of processor technologies like Intel's SpeedStep [58]. Thus the Grid meta-scheduler, along with other factors, should decide to assign jobs to resource sites at the time in which the jobs can be executed at minimum CPU frequency level to save power consumption.…”

Section: Energy -Aware Resource Allocationmentioning

confidence: 99%

“…In [38] the authors pointed out that just focusing on processor power consumption is not always sufficient. In order to minimize the power consumption of the entire Grid due to heterogeneity and a more holistic approach involving energy-aware changes to RA mechanisms is necessary [86]. In fact, although significant research exists on energy optimization within a cluster's or a datacenter's boundaries, the issue of power-aware RA in a distributed environment with multiple geographically dispersed resource sites remains largely open.…”

Section: Energy -Aware Resource Allocationmentioning

confidence: 99%

Survey on Grid Resource Allocation Mechanisms

et al. 2014

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Grid is a distributed high performance computing paradigm that offers various types of resources (like computing, storage, communication) to resource-intensive user tasks. These tasks are scheduled to allocate available Grid resources efficiently to achieve high system throughput and to satisfy A. Y. Zomaya University of Sydney, Sydney, Australia user requirements. The task scheduling problem has become more complex with the ever increasing size of Grid systems. Even though selecting an efficient resource allocation strategy for a particular task helps in obtaining a desired level of service, researchers still face difficulties in choosing a suitable technique from a plethora of existing methods in literature. In this paper, we explore and discuss existing resource allocation mechanisms for resource allocation problems employed in Grid systems. The work comprehensively surveys Gird resource allocation mechanisms for different architectures (centralized, distributed, static or dynamic). The paper also compares these resource allocation mechanisms based on their common features such as time complexity, searching mechanism, allocation strategy, optimality, operational environment and objective function they adopt for solving computing-and data-intensive applications. The comprehensive analysis of cutting-edge research in the Grid domain presented in this work provides readers with an understanding of essential concepts of resource allocation mechanisms in Grid systems and helps them identify important and outstanding issues for further investigation. It also helps readers to choose the most appropriate mechanism for a given system/application.

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Resource allocation to conserve energy in distributed computing

Cited by 18 publications

References 17 publications

Towards providing middleware-level proactive resource reorganisation for elastic HPC applications in the cloud

Towards providing middleware-level proactive resource reorganisation for elastic HPC applications in the cloud

Energy-efficient Algorithms for Ultrascale Systems

Survey on Grid Resource Allocation Mechanisms

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