Power-aware resource allocation in high-end systems via online simulation

Lawson, Barry; Smirni, Evgenia

doi:10.1145/1088149.1088179

Cited by 38 publications

(24 citation statements)

References 22 publications

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“…The authors in (Horvath et al, 2007;Wang and Lu, 2008;Wang et al, 2009;Li et al, 2011) used DVFS techniques to save energy. Some works like the one presented in (Lawson and Smirni, 2005) dynamically adjust the number of CPUs in a cluster to operate in ''sleep'' mode when the utilization is low, others like in (Lang and Patel, 2010;Heo et al, 2011;Chakravarty and Sinha, 2013) switch the PMs from on to off. Bradley et al (2003;Guenter et al, 2011;Bobroff et al, 2007), the authors statistically analyze the workload data and examine how to minimize the power consumption using workload history.…”

Section: Related Workmentioning

confidence: 99%

Towards Solving the Problem of Virtual Machine Placement in Cloud Computing: A Job Classification Approach

Al-Dulaimy¹,

Zekri²,

Itani³

et al. 2016

Journal of Computer Science

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Cloud Computing is a paradigm that delivers services by providing an access to wide range of shared resources which are hosted in cloud data centers. One of the recent challenges in this paradigm is to enhance the energy efficiency in these data centers. In this study, a model that identifies common patterns for the jobs submitted to the cloud is proposed. This model is able to predict the type of the job submitted and accordingly, the set of users' jobs is classified into four subsets. Each subset contains jobs that have similar requirements. In addition to the jobs' common pattern and requirements, the users' history is considered in the jobs' type prediction model. The goal of job classification is to find a way to propose useful strategy that helps to improve power efficiency. Based on the process of jobs' classification, the best fit virtual machine is allocated to each job. Then, the virtual machines are placed on the physical machines according to a novel strategy, called Mixed Type Placement strategy. The core idea of the proposed strategy is to place virtual machines of the jobs of different types in the same physical machine whenever possible. The placement process is based on Multi Choice Knapsack Problem which is a generalization of the classical Knapsack Problem. This is because different types of jobs do not intensively use the same compute or storage resources in the physical machine. This strategy minimizes the number of active physical machines which, in turn, leads to major reduction in the total energy consumption in the data center. The total execution time and the cost of executing the jobs submitted are considered in the placement process. To evaluate the performance of the proposed strategy, the CloudSim simulator is used with a real workload trace to simulate the cloud computing environment. The results show that the proposed strategy outperform both Genetic Algorithm and Round Robin from energy efficiency perspective.

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Section: Related Workmentioning

confidence: 99%

Towards Solving the Problem of Virtual Machine Placement in Cloud Computing: A Job Classification Approach

Al-Dulaimy¹,

Zekri²,

Itani³

et al. 2016

Journal of Computer Science

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“…Distributed methods for RA such as market mechanism, compensation and coalition formation, were summarized in the survey by Wu et al [140]. Distributed solutions were also the focus of Laure et al [75,76] with a particular interest on data or data-centric Grids. Krauter et al [69] surveyed and categorized existing resource management systems (RMS) and identified open challenges.…”

Section: Brief Comparison With the Existing Surveys On Grid Resource mentioning

confidence: 99%

“…Some of the Grid features include different global administrative control over resources, existence of multiple Grid schedulers together with local schedulers, different performance characteristics of resources with respect to CPU especially, and storage system access [75,76]. Current research on novel dynamic RM and job scheduling [81] add more scalability, robustness and fault tolerance to the system by balancing high workload due to the dynamic realtime availability of high computational power of various Grid resources.…”

Section: Resource Managementmentioning

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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“…Lawson et al aim to decrease supercomputing center power dissipation powering down some nodes ( [17]). The EASY backfilling is used as the job scheduling policy.…”

Section: Related Workmentioning

confidence: 99%

BSLD threshold driven power management policy for HPC centers

Etinski

Corbalan

Labarta

et al. 2010

2010 IEEE International Symposium on Parallel &Amp; Distributed Processing, Workshops and PHD Forum (IPDPSW)

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In this paper, we propose a power-aware parallel job scheduler assuming DVFS enabled clusters. A CPU frequency assignment algorithm is integrated into the well established EASY backfilling job scheduling policy. Running a job at lower frequency results in a reduction in power dissipation and accordingly in energy consumption. However, lower frequencies introduce a penalty in performance. Our frequency assignment algorithm has two adjustable parameters in order to enable fine grain energy-performance trade-off control. Furthermore, we have done an analysis of HPC system dimension. This paper investigates whether having more DVFS enabled processors for same load can lead to better energy efficiency and performance. Five workload traces from systems in production use with up to 9 216 processors are simulated to evaluate the proposed algorithm and the dimensioning problem. Our approach decreases CPU energy by 7%-18% on average depending on allowed job performance penalty. Using the power-aware job scheduling for 20% larger system, CPU energy needed to execute same load can be decreased by almost 30% while having same or better job performance.

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Power-aware resource allocation in high-end systems via online simulation

Cited by 38 publications

References 22 publications

Towards Solving the Problem of Virtual Machine Placement in Cloud Computing: A Job Classification Approach

Towards Solving the Problem of Virtual Machine Placement in Cloud Computing: A Job Classification Approach

Survey on Grid Resource Allocation Mechanisms

BSLD threshold driven power management policy for HPC centers

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