User-Centric Interference-Aware Load Balancing for Cloud-Deployed Applications

Abstract

VMs deployed in cloud environments are prone to performance interference due to dynamic and unpredictable contention for shared physical resources among colocated tenants. Current provider-centric solutions, such as careful co-scheduling of VMs and/or VM migration, require a priori profiling of customer VMs, which is infeasible in public clouds. Further, such solutions are not always aware of the user s SLO requirements or application bottlenecks. This paper presents DIAL, an interference-aware load balancing framework that can directly be employed by cloud users without requiring any assistance from the provider. The key idea behind DIAL is to infer the demand for contended resources on the physical hosts, which is otherwise hidden from users. Estimates of the colocated load are then used to dynamically shift load away from compromised VMs without violating the application s tail latency SLOs. We implement DIAL for web and online analytical processing applications, and show, via experimental results on OpenStack and AWS clouds, that DIAL can reduce tail latencies by as much as 70% compared to existing solutions.