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|Title:||Load sharing in distributed multimedia-on-demand systems|
|Authors:||Tay, Y.C. |
|Citation:||Tay, Y.C., Pang, H. (2000). Load sharing in distributed multimedia-on-demand systems. IEEE Transactions on Knowledge and Data Engineering 12 (3) : 410-428. ScholarBank@NUS Repository. https://doi.org/10.1109/69.846293|
|Abstract:||Service providers have begun to offer multimedia-on-demand services to residential estates by installing isolated, small-scale multimedia servers at individual estates. Such an arrangement allows the service providers to operate without relying on a high-speed, large-capacity metropolitan area network, which is still not available in many countries. Unfortunately, installing isolated servers could incur very high server costs, as each server requires spare bandwidth to cope with fluctuations in user demand. In this paper, we explore the feasibility of linking up several small multimedia servers to a (limited-capacity) network, and allowing servers with idle retrieval bandwidth to help out servers that are temporarily overloaded; the goal is to minimize the waiting time for service to begin. We identify four characteristics of load sharing in distributed multimedia system that differentiate it from load balancing in a conventional distributed system. We then introduce a GWQ load sharing algorithm that fits and exploits these characteristics; it puts all servers' pending requests in a global queue, from which a server with idle capacity obtains additional jobs. The performance of the algorithm is captured by an analytical model, which we validate through simulations. Both the analytical and simulation models show that the algorithm vastly reduces wait times at the servers. The analytical model also provides guidelines for capacity planning. Finally, we propose an enhanced GWQ+L algorithm that allows a server to reclaim active local requests that are being serviced remotely. Simulation experiments indicate that the scheduling decisions of GWQ+L are optimal, in the sense that it enables the distributed servers to approximate the performance of a large centralized server.|
|Source Title:||IEEE Transactions on Knowledge and Data Engineering|
|Appears in Collections:||Staff Publications|
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