Cluster computing: the commodity supercomputer

Baker, Mark; Buyya, Rajkumar

doi:10.1002/(sici)1097-024x(199905)29:6<551::aid-spe248>3.0.co;2-c

Cited by 52 publications

(21 citation statements)

References 14 publications

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“…MPICH-G2 [1,2] is a Grid-enabled implementation of the MPI v1.1 standard. That is, using services from the Globus Toolkit (e.g., job startup, security), MPICH-G2 allows you to couple multiple machines, potentially of different architectures, to run MPI applications.…”

Section: Mpich-g2mentioning

confidence: 99%

Performance-based dynamic loop scheduling in heterogeneous computing environments

2010

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With the rapid advance of computing technologies, it becomes more and more common to construct high-performance computing environments with heterogeneous commodity computers. Previous loop scheduling schemes were not designed for this kind of environments. Therefore, better loop scheduling schemes are needed to further increase the performance of the emerging heterogeneous PC cluster environments. In this paper, we propose a new heuristic for the performance-based approach to partition loop iterations according to the performance weighting of cluster/grid nodes. In particular, a new parameter is proposed to consider HPCC benchmark results as part of performance estimation. A heterogeneous cluster and grid were built to verify the proposed approach, and three kinds of application program were implemented for execution on cluster testbed. Experimental results show that the proposed approach performs better than the previous schemes on heterogeneous computing environments.

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Section: Mpich-g2mentioning

confidence: 99%

Performance-based dynamic loop scheduling in heterogeneous computing environments

2010

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“…Baker And Buyya [5] conclude that the use of computer clusters have advantages over the use of a dedicated parallel supercomputer that is associated with a lower price and incremental growth factor. Comparison between the CPU and GPU in cluster show that the performance of the GPU in Anomaly Detection in Hyperspectral Images is superior to the CPU clusters [6].…”

Section: Introductionmentioning

confidence: 99%

The Optimal High Performance Computing Infrastructure for Solving High Complexity Problem

2016

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“…With the advance of parallel and distributed technologies, such as cluster computing [3,20] and Grid computing [9] that enable on-demand resource sharing across various organizations, commercial vendors such as Amazon [2], HP [10], IBM [11], and Sun Microsystems [27] are now progressing aggressively towards realizing the next era of computing model -utility computing [36].…”

Section: Introductionmentioning

confidence: 99%

Integrated Risk Analysis for a Commercial Computing Service in Utility Computing

Yeo

Buyya

2008

J Grid Computing

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Recent technological advances in Grid computing enable the virtualization and dynamic delivery of computing services on demand to realize utility computing. In utility computing, computing services will always be available to the users whenever the need arises, similar to the availability of real-world utilities, such as electrical power, gas, and water. With this new outsourcing service model, users are able to define their service needs through Service Level Agreements (SLAs) and only have to pay when they use the services. They do not have to invest on or maintain computing infrastructures themselves and are not constrained to specific computing service providers. Thus, a commercial computing service will face two new challenges: (i) what are the objectives or goals it needs to achieve in order to support the utility computing model, and (ii) how to evaluate whether these objectives are achieved or not. To address these two new challenges, this paper first identifies four essential objectives that are required to support the utility computing model: (i) manage wait time for SLA acceptance, (ii) meet SLA requests, (iii) ensure reliability of accepted SLA, and (iv) attain profitability. It then describes two evaluation methods that are simple and intuitive: (i) separate and (ii) integrated risk analysis to analyze the effectiveness of resource management policies in achieving the objectives. Evaluation results based on simulation successfully demonstrate the applicability of separate and integrated risk analysis to assess policies in terms of the objectives. These evaluation results which constitute an a posteriori risk analysis of policies can later be used to generate an a priori risk analysis of policies by identifying possible risks for future utility computing situations.

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Cluster computing: the commodity supercomputer

Cited by 52 publications

References 14 publications

Performance-based dynamic loop scheduling in heterogeneous computing environments

Performance-based dynamic loop scheduling in heterogeneous computing environments

The Optimal High Performance Computing Infrastructure for Solving High Complexity Problem

Integrated Risk Analysis for a Commercial Computing Service in Utility Computing

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