Search

Cloud computing represents a novel computing model in the contemporary technology world. In a cloud system, the computing power of virtual machines (VMs) and network status can greatly affect the completion time of data intensive tasks. However, most of the current resource allocation policies focus only on network conditions and physical hosts. And the computing power of VMs is largely ignored. This paper proposes a comprehensive resource allocation policy which consists of a data intensive task scheduling algorithm that takes account of computing power of VMs and a VM allocation policy that considers bandwidth between storage nodes and hosts. The VM allocation policy includes VM placement and VM migration algorithms. Related simulations show that the proposed algorithms can greatly reduce the task completion time and keep good load balance of physical hosts at the same time. [ABSTRACT FROM AUTHOR]

This paper focuses on fast algorithm for computing the assignment reduct in inconsistent incomplete decision systems. It is quite inconvenient to judge the assignment reduct directly according to its definition. We propose the judgment theorem for the assignment reduct in the inconsistent incomplete decision system, which greatly simplifies judging this type reduct. On such basis, we derive a novel attribute significance measure and construct the fast assignment reduction algorithm (F-ARA), intended for computing the assignment reduct in inconsistent incomplete decision systems. Finally, we make a comparison between F-ARA and the discernibility matrix-based method by experiments on 13 University of California at Irvine (UCI) datasets, and the experimental results prove that F-ARA is efficient and feasible. [ABSTRACT FROM AUTHOR]