Optimal Markovian Dynamic Control of Interference-Prone Server Farms

Abstract

Interference is a key performance challenge faced by cloud users, and can significantly degrade application performance on virtual machines (VMs). For load-balanced cloud applications, a key question is how to distribute the load among VMs in the presence of interference. Using a Markov decision process (MDP) model, we investigate dynamic control polices to assign jobs among a cluster of VMs that are prone to interference in a system with a central queue and an arbitrary number of VMs. We characterize the structural properties of the MDP optimality equation, and we prove that the optimal control policy is a threshold policy based on the queue length. The optimal policy is characterized by multiple thresholds depending on the current conditions of the VMs, including the number of busy under-interference VMs. We discuss the existence of an ordering among such thresholds, and we prove the ordering for a two-VM system. Our numerical results show that the optimal dynamic policy can significantly improve performance compared to the the commonly employed non-idling policy. For low utilization systems, we observe improvements on the order of around 20%. We further implement the optimal policy in a real-world testbed using the HAProxy load balancer, and show that it can reduce web server response times by as much as 40%–60%, even for time-varying request rates.