A Performance Analysis of EC2 Cloud Computing Services for Scientific Computing

Ostermann, Simon; Iosup, Alexandru; Yigitbasi, Nezih; Prodan, Radu; Fahringer, Thomas; Epema, Dick

doi:10.1007/978-3-642-12636-9_9

Cited by 346 publications

(303 citation statements)

References 17 publications

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“…Therefore, traps can be minimized for imbalanced environments and assistance can be given to users in more difficult decisions when the environments have similar performance by using an advisory tool as the one proposed in this paper. (2) in an imbalanced environment, the cutoff function must take into account the relative speeds of both the cloud and local environments. We empirically found out that this relationship follows an exponential decay, i.e.…”

Section: Resultsmentioning

confidence: 99%

“…The computed distances are then passed to the kernel function, defined in Equation (2). Its purpose is to transform distances in a way that, as they approach zero, their transformed values approach a maximum constant value.…”

Section: Estimator Designmentioning

confidence: 99%

“…The actual prediction for a query point q is obtained by computing a weighted average of labels of the k points closest to q, where the weights are given by the kernel function from Equation (2). The complete estimation function is shown in Equation (3), where l( p ) is the label value of point p, and the points p and r are the points from the set of k nearest neighbors of q.…”

Section: Estimator Designmentioning

confidence: 99%

“…Current work on HPC cloud, also known as HPCaaS (HPC as a Service) [1], has mainly focused on understanding the cost-benefits of using cloud over on-premise clusters [2][3][4][5][6][7][8]. In addition, the aim has been to evaluate the performance gap between cloud and on-premise resources [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Job placement advisor based on turnaround predictions for HPC hybrid clouds

Cunha¹,

Rodrigues²,

Tizzei³

et al. 2017

Future Generation Computer Systems

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Abstract-Several companies and research institutes are moving their CPU-intensive applications to hybrid High Performance Computing (HPC) cloud environments. Such a shift depends on the creation of software systems that help users decide where a job should be placed considering execution time and queue wait time to access on-premise clusters. Relying blindly on turnaround prediction techniques will affect negatively response times inside HPC cloud environments. This paper introduces a tool to make job placement decisions in HPC hybrid cloud environments taking into account the inaccuracy of execution and waiting time predictions. We used job traces from real supercomputing centers to run our experiments, and compared the performance between environments using real speedup curves. We also extended a stateof-the-art machine learning based predictor to work with data from the cluster scheduler. Our main findings are: (i) depending on workload characteristics, there is a turning point where predictions should be disregarded in favor of a more conservative decision to minimize job turnaround times and (ii) scheduler data plays a key role in improving predictions generated with machine learning using job trace data-our experiments showed around 20% prediction accuracy improvements.

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Section: Resultsmentioning

confidence: 99%

Section: Estimator Designmentioning

confidence: 99%

Section: Estimator Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Job placement advisor based on turnaround predictions for HPC hybrid clouds

Cunha¹,

Rodrigues²,

Tizzei³

et al. 2017

Future Generation Computer Systems

View full text Add to dashboard Cite

show abstract

“…Virtualization can improve overall system security and reliability by isolating multiple software stacks on their own VMs [16]. The clouds provide an alternative for clusters, grids, and supercomputers to the scientist [17]. Cloud computing refers to both the applications delivered as services over the Internet and the hardware and systems software in the data centers that provide those services.…”

Section: Related Workmentioning

confidence: 99%

Sharing of Medical Information on Cloud Platform-A Review

Gupta¹,

Mann²

2014

IOSRJCE

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General‐purpose computation on GPUs for high performance cloud computing

Expósito

Taboada

Ramos

et al. 2012

Concurrency and Computation

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SUMMARY Cloud computing is offering new approaches for High Performance Computing (HPC) as it provides dynamically scalable resources as a service over the Internet. In addition, General‐Purpose computation on Graphical Processing Units (GPGPU) has gained much attention from scientific computing in multiple domains, thus becoming an important programming model in HPC. Compute Unified Device Architecture (CUDA) has been established as a popular programming model for GPGPUs, removing the need for using the graphics APIs for computing applications. Open Computing Language (OpenCL) is an emerging alternative not only for GPGPU but also for any parallel architecture. GPU clusters, usually programmed with a hybrid parallel paradigm mixing Message Passing Interface (MPI) with CUDA/OpenCL, are currently gaining high popularity. Therefore, cloud providers are deploying clusters with multiple GPUs per node and high‐speed network interconnects in order to make them a feasible option for HPC as a Service (HPCaaS). This paper evaluates GPGPU for high performance cloud computing on a public cloud computing infrastructure, Amazon EC2 Cluster GPU Instances (CGI), equipped with NVIDIA Tesla GPUs and a 10 Gigabit Ethernet network. The analysis of the results, obtained using up to 64 GPUs and 256‐processor cores, has shown that GPGPU is a viable option for high performance cloud computing despite the significant impact that virtualized environments still have on network overhead, which still hampers the adoption of GPGPU communication‐intensive applications. Copyright © 2012 John Wiley & Sons, Ltd.

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A Performance Analysis of EC2 Cloud Computing Services for Scientific Computing

Cited by 346 publications

References 17 publications

Job placement advisor based on turnaround predictions for HPC hybrid clouds

Job placement advisor based on turnaround predictions for HPC hybrid clouds

Sharing of Medical Information on Cloud Platform-A Review

General‐purpose computation on GPUs for high performance cloud computing

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