A time-driven adaptive mecahnism for cloud resource allocation
In this paper, we propose using adaptive allocation to find the most appropriate data center and physical machine for both users and service providers in a cloud computing environment. The proposed model adaptively finds the proper data center for the user based on expected allocation time and location. For the provider, the proposed model considers the workload of physical machines in a data center. The proposed model is implemented on an agent based testbed. The testbed simulates the resource allocation model used in cloud computing. Empirical results were obtained using the testbed to compare the performance of several resource allocation models, including the proposed model. The results suggest that by adopting the prosed model, both the user and the provider achieved faster allocation time and faster expected response time than using other related resource allocation models.
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