HARNESS Project: Managing Heterogeneous Computing Resources for a Cloud Platform

  • J. G. F. Coutinho
  • Oliver Pell
  • E. O’Neill
  • P. Sanders
  • J. McGlone
  • P. Grigoras
  • W. Luk
  • C. Ragusa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8405)

Abstract

Most cloud service offerings are based on homogeneous commodity resources, such as large numbers of inexpensive machines interconnected by off-the-shelf networking equipment and disk drives, to provide low-cost application hosting. However, cloud service providers have reached a limit in satisfying performance and cost requirements for important classes of applications, such as geo-exploration and real-time business analytics. The HARNESS project aims to fill this gap by developing architectural principles that enable the next generation cloud platforms to incorporate heterogeneous technologies such as reconfigurable Dataflow Engines (DFEs), programmable routers, and SSDs, and provide as a result vastly increased performance, reduced energy consumption, and lower cost profiles. In this paper we focus on three challenges for supporting heterogeneous computing resources in the context of a cloud platform, namely: (1) cross-optimisation of heterogeneous computing resources, (2) resource virtualisation and (3) programming heterogeneous platforms.

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References

  1. 1.
    Cardoso, J.M.P., Carvalho, T., Coutinho, J.G.F., Luk, W., Nobre, R., Diniz, P., Petrov, Z.: LARA: An aspect-oriented programming language for embedded systems. In: Proceedings of the Annual International Conference on Aspect-Oriented Software Development, pp. 179–190 (2012)Google Scholar
  2. 2.
    Graepel, T., et al.: Web-scale Bayesian click-through rate prediction for sponsored search advertising in Microsoft’s Bing search engine. In: Proc. of the Intl. Conf. on Machine Learning, pp. 13–20 (2010)Google Scholar
  3. 3.
    Grigoras, P., Niu, X., Coutinho, J.G.F., Luk, W., Bower, J., Pell, O.: Aspect driven compilation for Dataflow designs. In: Proc. of the IEEE Conference on App-Specific Sys. Arch. and Proc. (ASAP), pp. 18–25 (2013)Google Scholar
  4. 4.
    O’Neill, E., McGlone, J., et al.: SHEPARD: Scheduling on HEterogeneous Platforms using Application Resource Demands. In: Proc. of the Intl. Conf. on Parallel, Distributed and Network-based Processing (2014) (to appear)Google Scholar
  5. 5.
    Pell, O., Averbukh, V.: Maximum performance computing with Dataflow engines. Computing in Science Engineering 14(4), 98–103 (2012)CrossRefGoogle Scholar
  6. 6.
    Schubert, L., et al.: Advances in clouds: Research in future cloud computing. Expert Group Report, European Commission, Information Society and Media (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • J. G. F. Coutinho
    • 1
  • Oliver Pell
    • 2
  • E. O’Neill
    • 3
  • P. Sanders
    • 2
  • J. McGlone
    • 3
  • P. Grigoras
    • 1
  • W. Luk
    • 1
  • C. Ragusa
    • 3
  1. 1.Imperial College LondonUK
  2. 2.Maxeler TechnologiesUK
  3. 3.SAP HANA Cloud ComputingSystems EngineeringBelfastUK

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