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High-density model for server allocation and placement
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Source Joint International Conference on Measurement and Modeling of Computer Systems archive
Proceedings of the 2002 ACM SIGMETRICS international conference on Measurement and modeling of computer systems table of contents
Marina Del Rey, California
SESSION: Web table of contents
Pages: 152 - 159  
Year of Publication: 2002
ISBN:1-58113-531-9
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Authors
Craig W. Cameron  Caltech, Pasadena, CA
Steven H. Low  Caltech, Pasadena, CA
David X. Wei  Caltech, Pasadena, CA
Sponsor
SIGMETRICS: ACM Special Interest Group on Measurement and Evaluation
Publisher
ACM  New York, NY, USA
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ABSTRACT

It is well known that optimal server placement is NP-hard. We present an approximate model for the case when both clients and servers are dense, and propose a simple server allocation and placement algorithm based on high-rate vector quantization theory. The key idea is to regard the location of a request as a random variable with probability density that is proportional to the demand at that location, and the problem of server placement as source coding, i.e., to optimally map a source value (request location) to a code-word (server location) to minimize distortion (network cost). This view has led to a joint server allocation and placement algorithm that has a time-complexity that is linear in the number of clients. Simulations are presented to illustrate its performance.


REFERENCES

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Collaborative Colleagues:
Craig W. Cameron: colleagues
Steven H. Low: colleagues
David X. Wei: colleagues