Nowadays, thousands of servers in a cloud datacenter coordinate tasks to provide more reliable and highly available cloud computing services, especially in multi-task processing, as a crucial step to achieve high performance. Therefore, we need effective mechanisms to prepare for a failure of computing nodes. So far, a number of research studies have been carried out, trying to eliminate these problems, yet a little has been found efficient. In this paper, we present a cost-bandwidth based on scheduling algorithm that makes recovery from a saved state faster on heterogeneous computing environments. This algorithm not only considers the network bandwidth but also looks carefully at the monetary cost, which is paid by cloud customers (CCs) for utilizing cloud resources. In order to justify our proposal, we conducted numerous simulations and compared our method with existing ones. The results show that our approach can achieve higher performance, including recovery time in case of failure, while overhead in the case of no failure is a little in typical scenarios.
The emergence of mobile cloud computing (MCC) brings benefits to mobile users and cloud providers. However, due to the inherent limitations of the device such as battery life time, CPU and memory capacity, a mobile thin client device (e.g. smart phones, tablets, iWatch, Google Glass, etc) cannot meet the requirements of some demanding applications. To alleviate this limitation, the mobile device should cooperate with external resources to increase its performance. Recently, current research approaches have been unable to offer an efficient, seamless computing experience. In this paper, we present a comprehensive thin-thick client collaboration that involves conventional desktop or laptop computers, known as thick clients, by allowing the thin client to borrow resources from thick clients, particularly for optimizing data distribution and utilizing MCC resources to meet Service-Level Agreements, Quality-of-Service requirements and cloud service customers' budget. Our work uses both numerical analysis and simulation to prove that our proposed architecture can improve resource allocation efficiency and achieve better performance than other existing approaches in some cases.
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