Scheduling Strategy of P2P Based High Performance Computing Platform Base on Session Time Prediction

  • Authors:
  • Hao Zhang;Hai Jin;Qin Zhang

  • Affiliations:
  • Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China 430074;Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China 430074;Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China 430074

  • Venue:
  • GPC '09 Proceedings of the 4th International Conference on Advances in Grid and Pervasive Computing
  • Year:
  • 2009

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Abstract

P2P based high performance computing (HPC) system introduces many new and interesting problems. P2P environment is heterogeneous and asynchronous. At the same time, P2P platform is not stable. The joining and leaving of peers are random. These characteristics make the P2P based HPC platform have great difference to the traditional HPC platform and the global computing project. To achieve effective job scheduling on P2P based platform, this paper introduces a DHT based monitor and task management scheme. Further, we propose a data structure of distributed bidirectional Skiplist to keep the prediction session time. Our scheme distributes the task to the nodes which have longer online session time. With such scheme, we can reduce the migration of tasks among different nodes and improve the resource utilization of computing nodes. Finally, we use a real trace to demonstrate the efficiency of our algorithms and scheduling schemes.