Towards a multi-QoS human-centric cloud computing load balance resource allocation method

Abstract

In the large-scale clustering resource pool as human-centric cloud computing, peer load balance not only improves overall system efficiency, but also saves energy. As various factors should be considered in resource scheduling and each has different emphasis, resource allocation method adapted by different scene also has respective criteria. Based on resource allocation techniques, the multi-QoS load balance resource allocation method (MQLB-RAM) was proposed in the paper. It combines needs of users and service providers to constitute multi-QoS indexes. The needs from cost, system and network were met by quantitative analysis on load balancing using real-time load of peers. The algorithm also compares weight of each index in peer to match need and resource, so as to achieve the target of ensuring load balance, making full use of resources and saving money. Simulation experiment with CloudSim shows that the MQLB-RAM can achieve balance among load, resource access performance and cost.