A fair multi-resource allocation mechanism for time-varying discrete jobs with placement constraints

A key issue in resource sharing in cloud computing is how to fairly and efficiently allocate the multi-resources to users with dynamic demand. Multi-resource fair allocation in a cloud computing system usually faces problems, such as subdividing the minimum granularity of users' resource requirements, and the mismatch between task requirements and server configurations. Most of the existing mechanisms for multi-resource fair allocation are based on the ideal assumption that the task demands of user are infinitely divisible or that the task execution and server configuration are matched, which makes it difficult to guarantee that the allocation is feasible. By analyzing the characteristics of time-varying indivisible task demands and task placement constraints, a time-varying task share fairness allocation mechanism based on cumulative task share fairness was designed to ensure the fairness and efficiency of resource allocation. Theoretical analysis shows that the TV-TSF mechanism satisfies the sharing incentive, envy-freeness up to one item, and Pareto optimal properties. Simulation results based on the Alibaba cluster dataset show that, compared with the existing fair allocation mechanisms, the TV-TSF mechanism proposed can effectively reduce the waiting time, job queuing time, and job completion time of users.