Exploring application and infrastructure adaptation on hybrid grid-cloud infrastructure

  • Authors:

  • Hyunjoo Kim;Yaakoub el-Khamra;Shantenu Jha;Manish Parashar

  • Affiliations:

  • Rutgers University, NJ;The University of Texas at Austin, Austin, Texas and Louisiana State University;Louisiana State University;Rutgers University, NJ

  • Venue:
  • Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
  • Year:
  • 2010

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Abstract

Clouds are emerging as an important class of distributed computational resources and are quickly becoming an integral part of production computational infrastructures. An important but oft-neglected question is, what new applications and application capabilities can be supported by clouds as part of a hybrid computational platform? In this paper we use the ensemble Kalman-filter based dynamic application workflow and investigate how clouds can be effectively used as an accelerator to address changing computational requirements as well as changing Quality of Service constraints (e.g., deadlines). Furthermore, we explore how application and system-level adaptivity can be used to improve application performance and achieve a more effective utilization of the hybrid platform. Specifically, we adapt the ensemble Kalman-filter based application formulation (serial versus parallel, different solvers etc.) so as to execute efficiently on a range of different infrastructure (from High Performance Computing grids to clouds that support single core and many-core virtual machines). Our results show that there are performance advantages to be had by supporting application and infrastructure level adaptivity. In general, we find that grid-cloud infrastructure can support novel usage modes, such as deadline-driven scheduling, for applications with tunable characteristics that can adapt to varying resource types.