Probability distribution based resource management for multitenant cloud clusters

Abstract

Accurate resource allocation and colocating jobs are effective ways to increase resource utilization and reduce costs in modern datacenters. The main challenges are fluctuations in resource consumption and interference among colocated jobs. Therefore, we propose a resource management schema based on the probability distributions of resource consumption and completion time for multitenant cloud clusters. First, we found that the characteristics of the task can be well described by the probability distribution of resource consumption and completion time, and the probability distribution function can be obtained by Gaussian fitting. Second, we propose a probability distribution based resource allocation (PDRA) strategy for batch jobs. Third, we design a tail latency aware allocation (TLAA) strategy to use transient resources efficiently while ensuring tail latency requirements. Finally, we design a cost‐effective resource revocation (CERR) strategy to revoke transient resources with minimal eviction costs. Experimental results demonstrate the efficiency of our resource management. Our resource allocation strategies (PDRA and TLAA) can effectively improve resource utilization and reduce job completion time. CERR can reduce the impact of resource revocation on batch jobs and perform better than existing resource revocation strategies.