Fine-grained scheduling in multi-resource clusters

Abstract

In multi-resource clusters, many schedulers allocate resources based on fixed quantities. However, fixed allocations can easily lead to resource fragmentation and over-commitment problems, which may result in lower resource utilization and performance degradation. This paper proposes a fine-grained method (FGM) to improve the allocation granularity of resource allocation. This method divides tasks into execution stages according to the task requirement estimated using similar tasks at the runtime. Then, task resource requirements are matched with the available server resources by stages to refine two aspects of allocation granularity: allocation duration and allocation quantity. In addition, the FGM may over-allocate resources deliberately to further improve resource utilization and performance. The paper tested the FGM in three environments using both online and offline workloads. The test results show that the FGM can resolve resource fragmentation and over-commitment problems by significantly improving resource utilization and performance with acceptable fairness and scheduling response times.