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The economy of scale provided by cloud attracts a growing number of organizations and industrial companies to deploy their applications in cloud data centers (CDCs) and to provide services to users around the world. The uncertainty of arriving tasks makes it a big challenge for private CDC to cost-effectively schedule delay bounded tasks without exceeding their delay bounds. Unlike previous studies, this paper takes into account the cost minimization problem for private CDC in hybrid clouds, where the energy price of private CDC and execution price of public clouds both show the temporal diversity. Then, this paper proposes a temporal task scheduling algorithm (TTSA) to effectively dispatch all arriving tasks to private CDC and public clouds. In each iteration of TTSA, the cost minimization problem is modeled as a mixed integer linear program and solved by a hybrid simulated-annealing particle-swarm-optimization. The experimental results demonstrate that compared with the existing methods, the optimal or suboptimal scheduling strategy produced by TTSA can efficiently increase the throughput and reduce the cost of private CDC while meeting the delay bounds of all the tasks.

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Multiple heterogeneous applications concurrently run in distributed cloud data centers (CDCs) for better performance and lower cost. There is a highly challenging problem of how to minimize the total cost of a CDCs provider in a market where the bandwidth and energy cost show geographical diversity. To solve the problem, this paper first proposes a revenue-based workload admission control method to judiciously admit requests by considering factors including priority, revenue and the expected response time. Then, this paper presents a cost-aware workload scheduling method to jointly optimize the number of active servers in each CDC, and the selection of Internet service providers for the CDCs provider. Finally, trace-driven simulation results demonstrate that the proposed methods can greatly reduce the total cost and increase the throughput of the CDCs provider in comparison to existing methods. Note to Practitioners-A cloud provider deploys its applications in geographically distributed CDCs to improve stability and reliability. For cost and performance, each CDC provides services through multiple ISPs that deliver traffic between millions of users and the CDCs provider. The geographical diversity of the bandwidth and energy cost brings the CDCs provider a big challengeof how to minimize the bandwidth and energy cost of the CDCs provider. This paper first proposes a revenue-based workload admission control method to selectively admit requests. Then, this paper proposes a cost-aware workload scheduling method to allocate requests among multiple available Internet service providers connecting to distributed CDCs. The scheduling strategy can intelligently dispatch requests, and achieve lower cost and higher throughput for the CDCs provider.