Performance analysis of cloud computing services for many-tasks scientific computing

A. Iosup, S. Ostermann, M.N. Yigitbasi, R. Prodan, T. Fahringer, D.H.J. Epema

    Research output: Contribution to journalArticleAcademicpeer-review

    684 Citations (Scopus)
    477 Downloads (Pure)


    Cloud computing is an emerging commercial infrastructure paradigm that promises to eliminate the need for maintaining expensive computing facilities by companies and institutes alike. Through the use of virtualization and resource time sharing, clouds serve with a single set of physical resources a large user base with different needs. Thus, clouds have the potential to provide to their owners the benefits of an economy of scale and, at the same time, become an alternative for scientists to clusters, grids, and parallel production environments. However, the current commercial clouds have been built to support web and small database workloads, which are very different from typical scientific computing workloads. Moreover, the use of virtualization and resource time sharing may introduce significant performance penalties for the demanding scientific computing workloads. In this work, we analyze the performance of cloud computing services for scientific computing workloads. We quantify the presence in real scientific computing workloads of Many-Task Computing (MTC) users, that is, of users who employ loosely coupled applications comprising many tasks to achieve their scientific goals. Then, we perform an empirical evaluation of the performance of four commercial cloud computing services including Amazon EC2, which is currently the largest commercial cloud. Last, we compare through trace-based simulation the performance characteristics and cost models of clouds and other scientific computing platforms, for general and MTC-based scientific computing workloads. Our results indicate that the current clouds need an order of magnitude in performance improvement to be useful to the scientific community, and show which improvements should be considered first to address this discrepancy between offer and demand.
    Original languageEnglish
    Pages (from-to)931-945
    JournalIEEE Transactions on Parallel and Distributed Systems
    Issue number6
    Publication statusPublished - 2011


    Dive into the research topics of 'Performance analysis of cloud computing services for many-tasks scientific computing'. Together they form a unique fingerprint.

    Cite this