Experimental Studies of Granularity Aware ( m , k ) Scheduling for Real-time Media Servers

Real-time media servers are becoming increasingly important as the Internet supports more and more multimedia applications. In order to meet these ever increasing demands, real-time media servers will be responsible for supporting a large number of clients with a wide range of QoS requirements. In this paper, we propose a granularity aware (m, k) scheduler (GAS) which successfully controls the tradeoff between QoS granularity and scalability. We propose algorithms for group balance, stream join, burst sending, and feedback that exploit individual stream characteristics while avoiding per-stream state cost. Besides presenting detailed examples of GAS, we evaluate our work through simulation studies and experiments.

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