Environment-conscious scheduling of HPC applications on distributed Cloud-oriented data centers

Garg, Saurabh; Yeo, Chee Shin; Anandasivam, Arun; Buyya, Rajkumar

doi:10.1016/j.jpdc.2010.04.004

Cited by 263 publications

(121 citation statements)

References 31 publications

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“…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. The studies in (Garg et al, 2011;Tesauro et al, 2007;Zhang et al, 2014a) optimized multiple aspects of data center behavior such as consumed power, service cost and the overall performance. The authors in (Chase et al, 2001) proposed a cost approach to manage shared server resources such that the service requests for these resources are represented as a function taking into account the energy consumption.…”

Section: Related Workmentioning

confidence: 99%

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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%

“…It is possible to examine and anticipate such requirements by executing part of the code. Many studies assumes that execution time with specific processing elements is a known quantity or estimated by the cloud user (Garg et al, 2011;Lee, 2012). To estimate the job execution time, four major solutions were proposed:…”

Section: Profiling and Prediction Phasementioning

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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“…HPC applications have various sizes and require multiple cores and submit to system at dynamic arrival rate [10]. An HPC application can request some VMs.…”

Section: Constraintmentioning

confidence: 99%

A GPU-Based Enhanced Genetic Algorithm for Power-Aware Task Scheduling Problem in HPC Cloud

Quang-Hung¹,

Tan²,

Phat³

et al. 2014

Information and Communication Technology

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Abstract. In this paper, we consider power-aware task scheduling (PATS) in HPC clouds. Users request virtual machines (VMs) to execute their tasks. Each task is executed on one single VM, and requires a fixed number of cores (i.e., processors), computing power (million instructions per second -MIPS) of each core, a fixed start time and non-preemption in a duration. Each physical machine has maximum capacity resources on processors (cores); each core has limited computing power. The energy consumption of each placement is measured for cost calculating purposes. The power consumption of a physical machine is in a linear relationship with its CPU utilization. We want to minimize the total energy consumption of the placements of tasks. We propose here a genetic algorithm (GA) to solve the PATS problem. The GA is developed with two versions: (1) BKGPUGA, which is an adaptively implemented using NVIDIA's Compute Unified Device Architecture (CUDA) framework; and (2) SGA, which is a serial GA version on CPU. The experimental results show the BKGPUGA program that executed on a single NVIDIA® TESLA™ M2090 GPU (512 cores) card obtains significant speedups in comparing to the SGA program executing on Intel® Xeon TM E5-2630 (2.3 GHz) on same input problem size. Both versions share the same GA's parameters (e.g. number of generations, crossover and mutation probability, etc.) and a relative small (10 -11 ) on difference of two finesses between BKGPUGA and SGA. Moreover, the proposed BKGPUGA program can handle large-scale task scheduling problems with scalable speedup under limitations of GPU device (e.g. GPU's device memory, number of GPU cores, etc.).

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“…Jobs that have cooling as PADEVE have several concerns such as consumption of air conditioning Datacenters [15], spin down the processor cooler [7] and temperature reduction generated by the processor [9]. In all evaluated areas has been a reduction in energy consumption.…”

Section: Introductionmentioning

confidence: 99%

“…There are works that analyze proposed possuim as many points as processor consumption, consumption of air conditioning, the cost of energy and the emission of CO 2 . The main idea of this analysis PADEVE is send to meta-scheduler a coefficient of carbon emissions of data centers (provided by American Environmental Agency) and through these data make the decision of scheduling [15,16].…”

Section: Introductionmentioning

confidence: 99%

Green Cloud Meta-Scheduling

et al. 2015

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This article aims to evaluate the flexibility of GreenMACC (Metascheduling Green Architecture to Provide Quality of Service in Cloud Computing). The GreenMACC has a module called LRAM (Local Resource Allocation Manager) to automate the execution of all scheduling policies implemented in the architecture. This module enables the Meta-scheduler automatically adjust for each type of service requested by the user of a private cloud. Due to this function, can be ensure the most appropriate behavior to the principles of GreenIT while worrying about the quality of service. In this paper is shown the importance of the LRAM on GreenMACC. This article is also shown how to include a new policy in GreenMACC in a way that identifies the LRAM and automatically use. Through the performance evaluation of the new policy included it could be concluded that the GreenMACC is a flexible, reliable architecture and the LRAM module enables the automation of choosing the best scheduling mechanism in a private cloud.

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Environment-conscious scheduling of HPC applications on distributed Cloud-oriented data centers

Cited by 263 publications

References 31 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

A GPU-Based Enhanced Genetic Algorithm for Power-Aware Task Scheduling Problem in HPC Cloud

Green Cloud Meta-Scheduling

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