Public Cloud Virtual Machine Co-residency: Prediction and Implications

Abstract

Infrastructure-as-a-Service (IaaS) cloud platforms provide virtual machines (VMs) on demand to users hosted on the public cloud using shared or private physical servers. Reserving VMs on private dedicated hosts is expensive compared to renting the same on public shared servers, thus public shared servers are a widely preferred option for application deployment by cloud consumers. Despite considerable efforts to improve the performance of VMs on the public cloud, significant performance variation is still possible when a large number VMs share a single physical server. The information about VM co-residency is abstracted by the cloud provider and remains unknown to the cloud consumer. Due to this abstraction cloud consumers tend to make less informed decisions and choices when creating VMs on public clouds. This research evaluates performance degradation due to resource contention and utilizes memory benchmarks as performance metrics to predict VM co-residency evaluated across different Amazon Elastic Compute Cloud (EC2) VM placement groups. We conducted performance experiments leveraging memory benchmarks, to study performance implications for memory-intensive workloads executing on co-resident VMs. The benchmarking results obtained were used to train machine learning models to predict VM co-residency. The VM co-residency predictions are evaluated by launching VMs using EC2 placement groups, a feature that allows users to influence the physical placement of VMs on EC2.