Improving Resource Isolation of Critical Tasks in a Workload

Typical cluster schedulers co-locate critical tasks and background batch tasks to improve the utilization of resources in the cluster. However, this leads to resource contention and interference between the diverse co-located tasks. To ensure guaranteed resource allocation and predictability, critical tasks are executed within containers as they provide resource isolation using container resource allocation mechanisms. Linux-based containers achieve resource allocation and isolation using a kernel feature known as Control Groups (cgroups). Cgroups allow the division of CPU time into shares which can be allocated to different groups of tasks. In our study, we run workloads on servers with different hardware configurations and measure the CPU time per second, or the CPU bandwidth, that the critical tasks in the workloads can consume. Our workloads have been generated using a cluster trace published by Google, and contain a mixture of critical and background tasks. The results of the experiments show that under high CPU load conditions, the CPU bandwidth consumed by the critical tasks is inadequate and unstable because of the poor resource isolation offered by cgroups. However, when these tasks are scheduled with the careful use of SCHED_DEADLINE policy, which is based on the Global Earliest Deadline First and Constant Bandwidth Server algorithms, they steadily consume their required CPU bandwidth irrespective of the load on the CPU. As a result, when critical tasks are scheduled using SCHED_DEADLINE, they experience 3\(\times \)–40\(\times \) smaller delays than under cgroups.