Adaptive Virtual Machine Management in the Cloud

Abstract

The success of cloud computing technologies heavily depends on both the underlying hardware and system software support for virtualization. In this study, we propose to elevate the capability of the hypervisor to monitor and manage co-running virtual machines (VMs) by capturing their dynamic behavior at runtime and adaptively schedule and migrate VMs across cores to minimize contention on system resources hence maximize the system throughput. Implemented at the hypervisor level, our proposed scheme does not require any changes or adjustments to the VMs themselves or the applications running inside them, and minimal changes to the host OS. It also does not require any changes to existing hardware structures. These facts reduce the complexity of our approach and improve portability at the same time. The main intuition behind our approach is that because the host OS schedules entire virtual machines, it loses sight of the processes and threads that are running within the VMs; it only sees the averaged resource demands from the past time slice. In our design, we sought to recreate some of this low level information by using performance counters and simple virtual machine introspection techniques. We implemented an initial prototype on the Kernel Virtual Machine (KVM) and our experimental results show the presented approach is of great potential to improve the overall system throughput in the Cloud environment.

Understanding Cloud Workloads Performance in a Production like Environment

“…However, authors do not consider reducing unpredictability through resource partitioning. AVMMC [10], on the other hand, determines dynamically the best VM to core mapping on the studied system. In this ap- proach, authors pursue to improve the capabilities of the hypervisor to monitor and manage co-running VMs.…”

Interference Analysis and Resource Management in Server Processors: from HPC to Cloud Computing

“…To determine the best VM to physical core mapping, in [90], VMs are classified as compute or memory intensive, considering readings from several performance counters. In particular, only three performance events (L2 cache misses, L1 cache misses, and committed instructions) are used to classify applications.…”