A Heterogeneous Cloud Computing Architecture and Multi-Resource-Joint Fairness Allocation Strategy

Resource allocation strategies are an important research hotspot about cloud computing at present. The most fundamental problem is how to fairly allocate the finite amount of resources to multiple users or applications in complex application under heterogeneous cloud computing architecture, at the same time, to achieve maximize resource utilization or efficiency. However, tasks or users are often greedy for classical resource allocation problems, therefore, under the condition of finite amount of resource, the fairness of resource allocation is particularly important. To meet different task requirements and achieve multiple types resource fairness, we design a heterogeneous cloud computing architecture and present an algorithm of maximizing multi-resource fairness based on dominant resource(MDRF). We further prove the related attributions of our algorithm such as Pareto efficiency, and give the definition of dominant resource entropy (DRE) and dominant resource weight (DRW). DRE accurately depicts the adaption degree between the resource requirement of user and the resource type of server allocated for user tasks, and makes the system more adaptive and improves the system resource utilization. DRW guarantees the priority of users obtaining resource when cooperating with the adopted Max-Min strategy guaranteeing fairness, and makes the system resource allocation more ordered. Experimental results demonstrate that our strategy has more higher resource utilization and makes resource requirements and resource provision more matching. Furthermore, our algorithm makes users achieve more dominant resource and improves the quality of service.