Towards an Adaptive High-performance Execution of Scientific Applications in a Dynamic Cloud Environment

  IJPTT-book-cover
 
International Journal of P2P Network Trends and Technology (IJPTT)          
 
© 2015 by IJPTT Journal
Volume - 5 Issue - 1
Year of Publication : 2015
Authors : Mohamed-K Hussein
  10.14445/22492615/IJPTT-V15P401

MLA

Mohamed-K HUSSEIN. "Towards an Adaptive High-performance Execution of Scientific Applications in a Dynamic Cloud Environment". International Journal of P2P Network Trends and Technology (IJPTT), V5(1) :1-7 Jan - Feb 2015, ISSN:2249-2615, www.ijpttjournal.org, Published by Seventh Sense Research Group.

Abstract

During the last decade, the needs for high-performance computing for distributed scientific applications have been addressed over multiple high-performance environments including clusters and Grid computing technologies. Recently, cloud computing technology offers cheap and large-scale high-performance computing environment. Infrastructure as a Service cloud (IaaS) offers instant access to large-scale computing resources. However, the performance of the resources can dynamically varies according to the changing load conditions on the resources. Further, scientific applications require complex communication/computation pattern, such as optimized MPI for communication. For these reasons, it is challenging to achieve high-performance in a cloud environment. This paper presents an initial framework towards achieving adaptive high-performance execution for a distributed scientific application over a private dynamic cloud environment. The adaptation is achieved by migrating the distributed components of the benchmark application, which suffer performance degradation, to a promising different resource. The proposed framework contains a monitoring layer which monitors the execution times of the running application’s components. A decision layer issues the migration decision considering the execution times and the cost of the migration. Finally, the paper presents the applicability of the proposed framework on a private IaaS cloud managed by Eucalyptus.

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Keywords
Scientific computing, Cloud computing, high-performance computing, MPI applications, Eucalyptus, Adaptive execution.