Interactivity-constrained server provisioning in large-scale distributed virtual environments
Maintaining interactivity is one of the key challenges in distributed virtual environments (DVE), e.g., online games, distributed simulations, etc., due to the large, heterogeneous Internet latencies; and the fact that clients in a DVE are usually geographically separated. In this paper, we consider...
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Main Authors: | , , , , , |
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Format: | text |
Language: | English |
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Institutional Knowledge at Singapore Management University
2011
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Online Access: | https://ink.library.smu.edu.sg/sis_research/4770 https://ink.library.smu.edu.sg/context/sis_research/article/5773/viewcontent/213390.pdf |
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Institution: | Singapore Management University |
Language: | English |
Summary: | Maintaining interactivity is one of the key challenges in distributed virtual environments (DVE), e.g., online games, distributed simulations, etc., due to the large, heterogeneous Internet latencies; and the fact that clients in a DVE are usually geographically separated. In this paper, we consider a new problem, termed the interactivity-constrained server provisioning problem, whose goal is to minimize the number of distributed servers needed to achieve a pre-determined level of interactivity. We identify and formulate two variants of this new problem and show that they are both NP-hard via reductions to the set covering problem. We then propose several computationally efficient approximation algorithms for solving the problem. The main algorithms exploit dependencies among distributed servers to make provisioning decisions. We conduct extensive experiments to evaluate the performance of the proposed algorithms. More specifically, we use both static Internet latency data available from prior measurements and topology generators, as well as the most recent, dynamic latency data collected via our own large-scale deployment of a DVE performance monitoring system over PlanetLab. The results show that the newly proposed algorithms that take into account interserver dependencies significantly outperform the well-established set covering algorithm for both problem variants. |
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