Generalized Processor Sharing (GPS) is realized as the most important ideal fluid scheduling discipline in guaranteed QoS networks. The well-known problem of GPS-based rate-proportional servers is the bandwidth delay coupling which leads to inefficient resource utilization. In this paper we introduce the concept of non rate-proportional (or arbitrary) weighting of sessions in GPS systems. Such an approach in a GPS node of network constrained by leaky buckets can handle bandwidth and delay parameters independently, thus allowing better utilization of network resources. Moreover, we show that even under the traditional bandwidth delay coupled system (rate-proportional weighting) it is possible to determine tighter delay bounds of sessions than that of presented in earlier papers. A numerically inexpensive algorithm, which works in any arbitrary weighted GPS system is also presented for computing delay bounds. Besides the analytical work numerical examples are also shown. 1 a session is said to be backlogged if there are some data in its queue 2 an interval the server is continuously working
Absrmcr-Generslized Processor Sharing (GPS) is an ideal fluid schedul-ing discipline that supports well defined delay and loss bounds on Leakybucket constrained trafec Its packetized versions (WFQ, WFZQ, PGPS etc...) are considered as the packet scheduler of choice in IP routers and ATM switches of the future. The currently accepted approach for the design of GPS schedulers is based on determiaistic QoS guarantees, which is overly conservative due to the applied loose bounds and leads to Umitations on capacity. We developed a framework for the computation of tighter delay bounds, bandwidth and delay de-coupling ln GPS systems. In this paper, we propose several effective call admission control (CAC) algorithms that work in the bandwidth and delay decoupled system while using the tighter delay bounds presented hereln. One of the proposed CAC algorithm also handles the best-effort service class bestde the QoS guaranteed service classes. Performance evaluation of several CAC algorithms are presented herein.
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