Efficient Power Management of Heterogeneous Soft Real-Time Clusters

Wang, Leping; Lü, Ying

doi:10.1109/rtss.2008.31

Cited by 56 publications

(27 citation statements)

References 15 publications

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“…Furthermore, our algorithm incurs low overhead and achieves optimal power consumption. We have published our preliminary work in a conference paper [21]. This journal paper significantly extends that work [21] -we tailor our power management algorithm and make it also applicable to clusters, where servers have significant switch on/off overheads and their CPUs only support discrete frequencies.…”

Section: Related Workmentioning

confidence: 90%

An Efficient Threshold-Based Power Management Mechanism for Heterogeneous Soft Real-Time Clusters

Wang

Lü

2010

IEEE Trans. Ind. Inf.

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Section: Related Workmentioning

confidence: 90%

An Efficient Threshold-Based Power Management Mechanism for Heterogeneous Soft Real-Time Clusters

Wang

Lü

2010

IEEE Trans. Ind. Inf.

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“…Wang and Lu proposed a threshold-based approach to reduce energy consumption for heterogeneous clusters [9]. First, an offline analysis is performed to generate thresholds.…”

Section: Related Workmentioning

confidence: 99%

“…Some DVFS approaches target real-time systems, deploying scheduling methods that rely on the knowledge of the computational demand of individual tasks in order to allow response-time analysis, for example, Wang and Lu [9], Bambagini and Marinoni [11], or Bini et al [14].…”

Section: Soft Vs Hard Real Timementioning

confidence: 99%

Dynamic power management for reactive stream processing on the SCC tiled architecture

et al. 2016

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Dynamic voltage and frequency scaling (DVFS) is a means to adjust the computing capacity and power consumption of computing systems to the application demands. DVFS is generally useful to provide a compromise between computing demands and power consumption, especially in the areas of resource-constrained computing systems. Many modern processors support some form of DVFS. In this article, we focus on the development of an execution framework that provides lightweight DVFS support for reactive stream processing systems (RSPS). RSPs are a common form of embedded control systems, operating in direct response to inputs from their environment. At the execution framework, we focus on support for many-core scheduling for parallel execution of concurrent programs. We provide a DVFS strategy for RSPs that is simple and lightweight, to be used for dynamic adaptation of the power consumption at runtime. The simplicity of the DVFS strategy became possible by the sole focus on the application domain of RSPs. The presented DVFS strategy does not require specific assumptions about the message arrival rate or the underlying scheduling method. While DVFS is a very active field, in contrast to most existing research, our approach works also for platforms like many-core processors, where the power settings typically cannot be controlled individually for each computational unit. We also support dynamic scheduling with variable workload. While many research results are provided with simulators, in our approach, we present a parallel execution framework with experiments conducted on real hardware, using the single-chip cloud computer many-core processor. The results of our experimental evaluation confirm that our simple DVFS strategy provides potential for significant energy saving on RSPs.

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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.…”

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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Efficient Power Management of Heterogeneous Soft Real-Time Clusters

Abstract: Abstract

Cited by 56 publications

References 15 publications

An Efficient Threshold-Based Power Management Mechanism for Heterogeneous Soft Real-Time Clusters

An Efficient Threshold-Based Power Management Mechanism for Heterogeneous Soft Real-Time Clusters

Dynamic power management for reactive stream processing on the SCC tiled architecture

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

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