Abstract

Cloud computing has emerged as a mainstream paradigm for hosting various types of applications by supporting easy-to-use computing services. They are particularly relevant for applications requiring large volumes of computing power exceeding the computational capacity However, the use of hybrid clouds introduces the challenge of how much and when public cloud resources should be added to the pool of resources and especially when it is necessary to support quality of service requirements of applications with deadline constraints. These resource provisioning decisions are far from trivial if scheduling involves data-intensive applications using voluminous amounts of data. Issues such as the impact of network latency, bandwidth constraints, and location of data must be taken into account in order to minimize the execution cost while meeting the deadline for such applications. In this paper, we propose a new resource provisioning algorithm to support the deadline requirements of data-intensive applications in hybrid cloud environments. To evaluate our proposed algorithm, we implement it in the CloudSim which proves our resource provisioning algorithm is able to more efficiently allocate resources in cost effective manner compared to existing methods.

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