Analysis of data scheduling algorithms in supporting real-time multi-item requests in on-demand broadcast environments

  • Authors:

  • Jun Chen; Kai Liu;Victor C. S. Lee

  • Affiliations:

  • School of Information Management, Wuhan University, Hubei, China;Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong;Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong

  • Venue:
  • IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
  • Year:
  • 2009

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Abstract

On-demand broadcast is an effective wireless data dissemination technique to enhance system scalability and capability to handle dynamic data access patterns. Previous studies on time-critical on-demand data broadcast were under the assumption that each client requests only one data item at a time. With rapid growth of time-critical information dissemination services in emerging applications, there is an increasing need for systems to support efficient processing of real-time multi-item requests. Little work, however, has been done. In this work, we study the behavior of six representative single-item request based scheduling algorithms in time-critical multi-item request environments. The results show that the performance of all algorithms deteriorates when dealing with multi-item requests. We observe that data popularity, which is an effective factor to save bandwidth and improve performance in scheduling single-item requests, becomes a hindrance to performance in multi-item request environments. Most multi-item requests scheduled by these algorithms suffer from a starvation problem, which is the root of performance deterioration.