A dynamic scheduling method for collaborated cloud with thick clients

Nowadays, the emergence of computation-intensive applications brings benefits to individuals and the commercial organization. However, it still faces many challenges due to the limited processing capacity of the local computing resources. Besides, the local computing resources require a lot of finance and human forces. This problem, fortunately, has been made less severe, thanks to the recent adoption of Cloud Computing (CC) platform. CC enables offloading heavy processing tasks up to the "cloud", leaving only simple jobs to the user-end capacity-limited clients. Conversely, as CC is a pay-as-you-go model, it is necessary to find out an approach that guarantees the highly efficient execution time of cloud systems as well as the monetary cost for cloud resource use. Heretofore, a lot of research studies have been carried out, trying to eradicate problems, but they have still proved to be trivial. In this paper, we present a novel architecture, which is a collaboration of the computing resources on cloud provider side and the local computing resources (thick clients) on client side. In addition, the main factor of this framework is the dynamic genetic task scheduling to globally minimize the completion time in cloud service, while taking into account network condition and cloud cost paid by customers. Our simulation and comparison with other scheduling approaches show that the proposal produces a reasonable performance together with a noteworthy cost saving for

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