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24th IEEE International Conference on Distributed Computing Systems (ICDCS'04)   pp. 740-747
Modeling and Analysis of 2D Service Differentiation on e-Commerce Servers

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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDCS.2004.1281642
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Abstract
A scalable e-Commerce server should be able to provide different levels of quality of service (QoS) to different types of requests according to clients’ navigation patterns and the server capacity. In this paper, we propose a two-dimensional (2D) service differentiation (DiffServ) model for on-line transactions: inter-session and intra-session. The inter-session model aims to provide different levels of QoS to sessions from different customer classes, and the intrasession model aims to provide different levels of QoS to requests in different states of a session. We introduce service slowdown as a QoS metric of e-Commerce servers. It is defined as the weighted sum of request slowdown in different sessions and in different session states. We formulate the problem of 2D DiffServ provisioning as an optimization of processing rate allocation with the objective of minimizing service slowdown. We derive the optimal allocations for an M/G/1 server under various server load conditions and prove that the optimal allocations guarantees requests’ slowdown to be square-root proportional to their pre-specified differentiation weights in both dimensions. We evaluate the optimal allocation scheme via extensive simulations and compare it with a tailored proportional DiffServ scheme. Simulation results validate that both allocation schemes can achieve predictable, controllable, and fair 2D slowdown differentiation on e-Commerce servers. The optimal allocation scheme guarantees 2D DiffServ at a minimum cost of service slowdown.
Additional Information

Citation:  Xiaobo Zhou, Jianbin Wei, Cheng-Zhong Xu, "Modeling and Analysis of 2D Service Differentiation on e-Commerce Servers," icdcs, pp. 740-747,  24th IEEE International Conference on Distributed Computing Systems (ICDCS'04),  2004

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