Performance and Cost Analysis of the Supernova Factory on the Amazon AWS Cloud

Jackson, Keith; Muriki, Krishna; Ramakrishnan, Lavanya; Runge, K.; Thomas, R. C.

doi:10.1155/2011/498542

Cited by 20 publications

(13 citation statements)

References 10 publications

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“…At least during the duration of our tests we noticed that the larger instance types showed lesser advantage with the local disks possibly due to the increased network bandwidth available in the large instances. However applications might need to do a performance-cost analysis of local disk vs EBS for their particular application [8].…”

Section: Storage Options On Vmmentioning

confidence: 99%

I/O performance of virtualized cloud environments

Ghoshal

Canon

Ramakrishnan

2011

Proceedings of the Second International Workshop on Data Intensive Computing in the Clouds

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The scientific community is exploring the suitability of cloud infrastructure to handle High Performance Computing (HPC) applications. The goal of Magellan, a project funded through DOE ASCR, is to investigate the potential role of cloud computing to address the computing needs of the Department of Energy's Office of Science, especially for mid-range computing and data-intensive applications which are not served through existing DOE centers today. Prior work has shown that applications with significant communication or I/O tend to perform poorly in virtualized cloud environments. However, there is a limited understanding of the I/O characteristics of cloud environments. This paper will present our results in benchmarking the I/O performance over different cloud and HPC platforms to identify the major bottlenecks in existing infrastructure. We compare the I/O performance using IOR benchmarks on two cloud platforms -Amazon and the Magellan cloud testbed. We analyze the performance of different storage options available on different instance types in multiple availability zones. Finally, we do some custom benchmarking in order to analyze the variability in the I/O patterns over time and region. Our results highlight the performance of the different storage options enabling applications to make effective storage option choices.

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Section: Storage Options On Vmmentioning

confidence: 99%

I/O performance of virtualized cloud environments

Ghoshal

Canon

Ramakrishnan

2011

Proceedings of the Second International Workshop on Data Intensive Computing in the Clouds

Self Cite

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“…Related Work: There have been several efforts to characterize the performance of HPC applications on cloud environments, particularly Amazon EC2 [5,7,8,12,16,17,19]. Because most of these studies were carried out before the availability of Amazon's CC instance, they were conducted on the non-HPC instances.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of Amazon Elastic Compute Cloud for NASA high‐performance computing applications

Mehrotra

Djomehri

Heistand

et al. 2013

Concurrency and Computation

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Cloud computing environments are now widely available and are being increasingly utilized for technical computing. They are also being touted for high-performance computing (HPC) applications in science and engineering. For example, Amazon Elastic Compute Cloud (EC2) Services offers specialized Cluster Compute instance types to run HPC applications. In this paper, we compare the performance characteristics of two Amazon EC2 HPC instance types with that of National Aeronautics and Space Administration's (NASA) Pleiades supercomputer, an SGI W ICE ™ cluster. For this study, we utilized the HPC Challenge kernels and the NAS Parallel Benchmarks along with four full-scale applications from the repertoire of codes that are being used by NASA scientists and engineers. We compare the total runtime of these codes for varying number of cores. We also break out the computation and communication times for a subset of these applications to explore the effect of interconnect differences on the two systems. In general, the single node performance of the two platforms is equivalent. However, for most of the codes when scaling to larger core counts, the performance of the EC2 HPC instances generally lags that of Pleiades because of worse network performance of the former. In addition to analyzing application performance, we also briefly touch upon the overhead due to virtualization and the usability of cloud environments such as Amazon EC2.

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“…Jackson has deployed a full application that performs massive file operations and data transfer on Amazon EC2 [18]. The research mostly focuses on different storage options on Amazon.…”

Section: Related Workmentioning

confidence: 99%

“…Many papers have analyzed the cost of the cloud as an alternative resource to dedicated HPC sources [18] [19] [25]. Our work covers the storage services performance both on micro-benchmarks as well as the performance while being used by data-intensive applications.…”

Section: Related Workmentioning

confidence: 99%

Understanding the Performance and Potential of Cloud Computing for Scientific Applications

Sadooghi

Martin

et al. 2017

IEEE Trans. Cloud Comput.

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Commercial clouds bring a great opportunity to the scientific computing area. Scientific applications usually require significant resources, however not all scientists have access to sufficient high-end computing systems. Cloud computing has gained the attention of scientists as a competitive resource to run HPC applications at a potentially lower cost. But as a different infrastructure, it is unclear whether clouds are capable of running scientific applications with a reasonable performance per money spent. This work provides a comprehensive evaluation of EC2 cloud in different aspects. We first analyze the potentials of the cloud by evaluating the raw performance of different services of AWS such as compute, memory, network and I/O. Based on the findings on the raw performance, we then evaluate the performance of the scientific applications running in the cloud. Finally, we compare the performance of AWS with a private cloud, in order to find the root cause of its limitations while running scientific applications. This paper aims to assess the ability of the cloud to perform well, as well as to evaluate the cost of the cloud in terms of both raw performance and scientific applications performance. Furthermore, we evaluate other services including S3, EBS and DynamoDB among many AWS services in order to assess the abilities of those to be used by scientific applications and frameworks. We also evaluate a real scientific computing application through the Swift parallel scripting system at scale. Armed with both detailed benchmarks to gauge expected performance and a detailed monetary cost analysis, we expect this paper will be a recipe cookbook for scientists to help them decide where to deploy and run their scientific applications between public clouds, private clouds, or hybrid clouds.

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Performance and Cost Analysis of the Supernova Factory on the Amazon AWS Cloud

Cited by 20 publications

References 10 publications

I/O performance of virtualized cloud environments

I/O performance of virtualized cloud environments

Performance evaluation of Amazon Elastic Compute Cloud for NASA high‐performance computing applications

Understanding the Performance and Potential of Cloud Computing for Scientific Applications

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