Robust Multi-Resource Allocation with Demand Uncertainties in Cloud Scheduler

Abstract

Cloud scheduler manages multi-resources (e.g., CPU, GPU, memory, storage etc.) in cloud platform to improve resource utilization and achieve cost-efficiency for cloud providers. The optimal allocation for multi-resources has become a key technique in cloud computing and attracted more and more researchers' attentions. The existing multi-resource allocation methods are developed based on a condition that the job has constant demands for multi-resources. However, these methods may not apply in a real cloud scheduler due to the dynamic resource demands in jobs' execution. In this paper, we study a robust multi-resource allocation problem with uncertainties brought by varying resource demands. To this end, the cost function is chosen as either of two multi-resource efficiency-fairness metrics called Fairness on Dominant Shares and Generalized Fairness on Jobs, and we model the resource demand uncertainties through three typical models, i.e., scenario demand uncertainty, box demand uncertainty and ellipsoidal demand uncertainty. By solving an optimization problem we get the solution for robust multi-resource allocation with uncertainties for cloud scheduler. The extensive simulations show that the proposed approach can handle the resource demand uncertainties and the cloud scheduler runs in an optimized and robust manner.