Abstract
Distributed cloud has been widely adopted to support service requests from dispersed regions, especially for large enterprise which requests virtual desktops for multiple geodistributed branch companies. The cloud service provider (CSP) aims to deliver satisfactory services at the least cost. CSP selects proper data centers (DCs) closer to the branch companies so as to shorten the response time to user request. At the same time, it also strives to cut cost considering both DC level and server level. At DC level, the expensive long distance inter-DC bandwidth consumption should be reduced and lower electricity price is sought. Inside each tree-like DC, servers are trying to be used as little as possible so as to save equipment cost and power. In nature, there is a noncooperative relation between the DC level and server level in the selection. To attain these objectives and capture the noncooperative relation, multiobjective bilevel programming is used to formulate the problem. Then a unified genetic algorithm is proposed to solve the problem which realizes the selection of DC and server simultaneously. The extensive simulation shows that the proposed algorithm outperforms baseline algorithm in both quality of service guaranteeing and cost saving.
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