Collaborative Autonomic Resource Management System for Mobile Cloud Computing

摘要

Mobile cloud computing promises more effective and efficient utilization of the ever-increasing pool of computing resources available on modern mobile devices. To support mobile cloud computing, we propose a Collaborative Autonomic Resource Management System (CARMS), which automatically manages task scheduling and resource allocation to realize efficient cloud formation and computing in a dynamic mobile environment. CARMS utilizes our previously proposed Global Resource Positioning System (GRPS) to track current and future availability of mobile resources. In this paper, we present CARMS architecture and its associated Adaptive List-based Scheduling and Allocation AlgorithM (ALSALAM) for adaptive task scheduling and resource allocation. ALSALAM uses the continually updated data from the loosely federated GRPS to automatically select appropriate mobile nodes to participate informing clouds, and to adjust both task scheduling and resource allocation according to the changing conditions due to the dynamicity of resources and tasks in an existing cloud. Our simulation results show that CARMS offers effective and efficient support for mobile cloud computing that has not yet been adequately provided by prior research.