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Journal ArticleDOI

Amazon Elastic Compute Cloud (EC2) versus In-House HPC Platform: A Cost Analysis

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TLDR
This article proposes a theoretical price-performance model based on the study of the actual Cloud instances proposed by one of the major Cloud IaaS actors: Amazon Elastic Compute Cloud (EC2), and proposes a hourly price comparison between an in-house cluster and the equivalent EC2 instances.
Abstract
While High Performance Computing (HPC) centers continuously evolve to provide more computing power to their users, we observe a wish for the convergence between Cloud Computing (CC) and High Performance Computing (HPC) platforms, with the commercial hope to see Cloud Computing (CC) infrastructures to eventually replace in-house facilities. If we exclude the performance point of view where many previous studies highlight a non-negligible overhead induced by the virtualization layer at the heart of every Cloud middleware when running a HPC workload, the question of the real cost-effectiveness is often left aside with the intuition that, most probably, the instances offered by the Cloud providers are competitive from a cost point of view. In this article, we wanted to assert (or infirm) this intuition by analyzing what composes the Total Cost of Ownership (TCO) of an in-house HPC facility operated internally since 2007. This Total Cost of Ownership (TCO) model is then used to compare with the induced cost that would have been required to run the same platform (and the same workload) over a competitive Cloud IaaS offer. Our approach to address this price comparison is three-fold. First we propose a theoretical price-performance model based on the study of the actual Cloud instances proposed by one of the major Cloud IaaS actors: Amazon Elastic Compute Cloud (EC2). Then, based on the HPC facility TCO analysis we propose a hourly price comparison between our in-house cluster and the equivalent EC2 instances. Finally, based on the experimental benchmarking on the local cluster and on the Cloud instances we propose an update of the former theoretical price model to reflect the real system performance. The results obtained advocate in general for the acquisition of an in-house HPC facility, which balances the common intuition in favor of Cloud Computing platforms, would they be provided by the reference Cloud provider worldwide.

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Citations
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DolphinNext: a distributed data processing platform for high throughput genomics

TL;DR: DolphinNext is a flexible, intuitive, web-based data processing and analysis platform that enables creating, deploying, sharing, and executing complex Nextflow pipelines with extensive revisioning and interactive reporting to enhance reproducible results.
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Comparison of MongoDB and Cassandra Databases for Spectrum Monitoring As-a-Service

TL;DR: A hardware and software architecture for wideband radio spectrum monitoring inspired to the Lambda architecture is designed and implemented and a MapReduce job for spectrum visualization has been developed to show the potential of this approach and to identify the challenges in processing spectrum sensor data.
Proceedings ArticleDOI

Quantifying the Financial Value of Cloud Investments: A Systematic Literature Review

TL;DR: The review highlights the need for multi-disciplinary research which both explores and further develops the conceptualization of value in cloud computing research, and research which investigates how IT value manifests itself across the chain of service provision and in inter-organizational scenarios.
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Tracking the NGS revolution: managing life science research on shared high-performance computing clusters.

TL;DR: Use and efficiency metrics are developed and show that computing jobs for NGS projects use more RAM than non-NGS projects, are more variable in core usage, and rarely span multiple nodes, and NGS jobs use booked resources less efficiently for a variety of reasons.
Proceedings ArticleDOI

Cost-Benefit Analysis of Public Clouds for Offloading In-House HPC Jobs

TL;DR: A comparative cost-benefit analysis of the in-house compute facility at SERC with that of the commercial cloud providers like Amazon, Google Cloud, Microsoft Azure and Sabalcore is conducted.
References
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Proceedings ArticleDOI

Performance Analysis of High Performance Computing Applications on the Amazon Web Services Cloud

TL;DR: This work represents the most comprehensive evaluation to date comparing conventional HPC platforms to Amazon EC2, using real applications representative of the workload at a typical supercomputing center, and results indicate that EC2 is six times slower than a typical mid-range Linux cluster, and twenty times faster than a modern HPC system.
Proceedings Article

A Performance Analysis of EC2 Cloud Computing Services for Scientific Computing

TL;DR: In this paper, the authors evaluate the performance of the Amazon EC2 platform using micro-benchmarks and kernels and conclude that the current cloud services need an order of magnitude in performance improvement to be useful to the scientific community.
Proceedings ArticleDOI

The Characteristics of Cloud Computing

TL;DR: The characteristics of this area which make cloud computing being cloud computing and distinguish it from other research areas are proposed.
Proceedings ArticleDOI

Management of an academic HPC cluster: The UL experience

TL;DR: The UL HPC facility and the derived deployed services is a complex computing system to manage by its scale and the aspects in relation to the management of such a complex infrastructure, whether technical or administrative are covered.
Proceedings ArticleDOI

Case study for running HPC applications in public clouds

TL;DR: The results show that 1) virtualization technology, which is widely used by cloud computing, adds little performance overhead; 2) most current public clouds are not designed for running scientific applications primarily due to their poor networking capabilities; however, a cloud with moderately better network will deliver a significant performance improvement.
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