By Topic

An Exploration of the Effects of State Granularity through (m, k) Real-Time Streams

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Yingxin Jiang ; University of Notre Dame, Notre Dame ; Aaron Striegel

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. While techniques to aggregate state information for scalability have been proposed in the literature such as with Differentiated Services; the per-stream effects of such aggregation are poorly understood. Based on the (m,k)-firm model to schedule loss-tolerant streams, we explore the effects of aggregated state information in this paper and describe our scheme, called granularity aware (m,k) queue management (GAQM). GAQM improves control over the tradeoff between scalability and per-stream QoS performance. Specifically, we identify the necessity of balancing aggregation groups according to characteristics such as relative deadlines. Another key finding of this work is that with proper biasing, the inaccuracy of aggregate state lends itself to burst scheduling rather than simply extending traditional scheduling mechanisms. This finding is profound in that the result is counterintuitive: less frequent scheduling leads to increased per-stream performance. We present detailed examples of GAQM and evaluate our work through simulation studies and Markov chain analysis.

Published in:

IEEE Transactions on Computers  (Volume:58 ,  Issue: 6 )