Shaready: A resource-isolatedworkload co-location system

Over a decade, cloud and subsequent joint cloud computing has been evolving into one of biggest disruptive technologies in modern digital age. The rapidly maturing cloud service and system management still heavily relies on virtualization which underpins Infrastructure as a Service (IaaS) to offer on-demand and low-cost computing services. Nevertheless datacenters still suffer from low utilization and resource imbalance. IaaS systems and their workloads, as legacy estates, are intricate to be migrated or re-planned, thereby increasing the complexity of utilization improvement. Arguably workload co-location of long-running applications encapsulated in virtual machines and latency-insensitive batch jobs is an alternative to improve overall resource utilization. However, guaranteeing the quality of long-running services is still challenging. In this context, we proposed an isolation-based cluster resource sharing system Shaready to enable workload co-residences. By means of global resource quota configuration and multi-resource isolation, long-running services in virtual machines can be prioritized with maximized resource provisioning. We implemented and validated it based on Openstack and Yarn clusters, and experiments demonstrate that system CPU and memory utilization can be improved by roughly 50% and 16.67% respectively on average with at most 7% performance degradation.