A Machine-Learning Based Load Prediction Approach for Distributed Service-Oriented Applications

  • Authors:
  • Jun Wang;Yi Ren;Di Zheng;Quan-Yuan Wu

  • Affiliations:
  • School of Computer Science, National University of Defence Technology, Changsha, Hunan, 410073, China;School of Computer Science, National University of Defence Technology, Changsha, Hunan, 410073, China;School of Computer Science, National University of Defence Technology, Changsha, Hunan, 410073, China;School of Computer Science, National University of Defence Technology, Changsha, Hunan, 410073, China

  • Venue:
  • ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
  • Year:
  • 2007

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Abstract

By using middleware, we can satisfy the urgent demands of performance, scalability and availability in current distributed service-oriented applications. However to the complex applications, the load peak may make the system suffer extremely high load and the response time may be decreased for this kind of fluctuate. Therefore, to utilize the services effectively especially when the workloads fluctuate frequently, we should make the system react to the load fluctuate gradually and predictably. Many existing load balancing middleware use the dampeningtechnology to make the load to be predicative. However, distributed systems are inherently difficult to manage and the dampening factor cannot be treated as static and fixed. The dampening factor should be adjusted dynamically according to different load fluctuate. So we have proposed a new technique based on machine learning for adaptive and flexible load prediction mechanism based on our load balancing middleware.