TY - GEN
T1 - Dynamic resource allocation of shared data centers supporting multiclass requests
AU - Mahabhashyam, Sai Rajesh
AU - Gautam, Natarajan
PY - 2004
Y1 - 2004
N2 - In this paper, we consider a shared data center for web server) with requests from multiple companies: each company with two classes of requests: 1) streaming requests and 2) elastic requests. The processing capacity of the web server is shared by the companies' requests of the two classes. An analytical model is developed and matrix geometric method is used to derive the system performance measures. A cost model is developed to obtain a request admission control policy that utilizes the web server's processing capacity efficiently. The objective is to maximize the revenue of the data center, which is a function of the multiclass request characteristics and the quality of service measures. It is well known that the request characteristics like arrival rates vary dynamically over time. As a consequence, autonomic computing is the only practical way for determining on the fly how to partition the web server processing capacity over time among the companies.
AB - In this paper, we consider a shared data center for web server) with requests from multiple companies: each company with two classes of requests: 1) streaming requests and 2) elastic requests. The processing capacity of the web server is shared by the companies' requests of the two classes. An analytical model is developed and matrix geometric method is used to derive the system performance measures. A cost model is developed to obtain a request admission control policy that utilizes the web server's processing capacity efficiently. The objective is to maximize the revenue of the data center, which is a function of the multiclass request characteristics and the quality of service measures. It is well known that the request characteristics like arrival rates vary dynamically over time. As a consequence, autonomic computing is the only practical way for determining on the fly how to partition the web server processing capacity over time among the companies.
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M3 - Conference contribution
AN - SCOPUS:4544268232
SN - 0769521142
SN - 9780769521145
T3 - Proceedings - International Conference on Autonomic Computing
SP - 222
EP - 229
BT - Proceedings - International Conference on Autonomic Computing
T2 - Proceedings - International Conference on Autonomic Computing
Y2 - 17 May 2004 through 18 May 2004
ER -