Resource optimization in distributed real-time multimedia applications

  • Authors:

  • Ran Yang;Robert D. Van Der Mei;Dennis Roubos;Frank J. Seinstra;Henri E. Bal

  • Affiliations:

  • Department of Mathematics, Faculty of Sciences, VU University, Amsterdam, The Netherlands 1081 HV and Centre of Mathematics and Computer Science, Amsterdam, The Netherlands 1098 XG;Department of Mathematics, Faculty of Sciences, VU University, Amsterdam, The Netherlands 1081 HV and Centre of Mathematics and Computer Science, Amsterdam, The Netherlands 1098 XG;Department of Mathematics, Faculty of Sciences, VU University, Amsterdam, The Netherlands 1081 HV;Department of Computer Science, Faculty of Sciences, VU University, Amsterdam, The Netherlands 1081 HV;Department of Computer Science, Faculty of Sciences, VU University, Amsterdam, The Netherlands 1081 HV

  • Venue:
  • Multimedia Tools and Applications
  • Year:
  • 2012

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Abstract

The research area of multimedia content analysis (MMCA) considers all aspects of the automated extraction of knowledge from multimedia archives and data streams. To adhere to strict time constraints, large-scale multimedia applications typically are being executed on distributed systems consisting of large collections of compute clusters. In a distributed scenario, it is first essential to determine the optimal number of compute nodes used by each cluster, properly balancing the complex tradeoff between computation and communication. This issue is referred as the "resource utilization" (RU) problem. Next, it is important to tune the transmission of newly generated data sent to each cluster, so as to obtain the highest service utilization, while minimizing the need for buffering. This latter issue is referred as the problem of "just-in-time" (JIT) communication. In this paper, we first present a simple and easy-to-implement method for the RU problem, which is based on the classical binary search method. Second, we address the JIT problem by introducing a smart adaptive control method that properly reacts to the continuously changing circumstances in distributed systems. Extensive experimental validation of the two approaches on a real distributed system shows that our optimization approaches are indeed highly effective.