AUDIBLE: A Convolution-Based Resource Allocator for Oversubscribing Burstable Virtual Machines

In an effort to increase the utilization of data center resources cloud providers have introduced a new type of virtual machine (VM) offering, called a burstable VM (BVM). Our work is the first to study the characteristics of burstable VMs (based on traces from production systems at a major cloud provider) and resource allocation approaches for BVM workloads. We propose new approaches for BVM resource allocation and use extensive simulations driven by field data to compare them with two baseline approaches used in practice. We find that traditional approaches based on using a fixed oversubscription ratio or based on the Central Limit Theorem do not work well for BVMs: They lead to either low utilization or high server capacity violation rates. Based on the lessons learned from our workload study, we develop a new approach to BVM scheduling, called Audible, using a non-parametric statistical model, which makes the approach light-weight and workload independent, and obviates the need for training machine learning models and for tuning their parameters. We show that Audible achieves high system utilization while being able to enforce stringent requirements on server capacity violations.