Adaptive Online Compression in Clouds-Making Informed Decisions in Virtual Machine Environments
Infrastructure as a Service clouds often use virtual machines to host different customers on the same physical hardware. This form of resource sharing can lead to unpredicatable performance degradations for the individual customer, especially with regard to data-intensive applications, which heavily depend on stable I/O characteristics. One traditional approach to cope with I/O fluctuations is adaptive online compression. In this paper we present a new scheme for adaptive online compression which has been explicitly designed to work in co-located virtual machine environments. In contrast to existing adaptive online compression schemes, the decision model of our approach does not rely on the system metrics CPU utilization and I/O bandwidth, which we demonstrate to be often displayed inaccurately inside XEN, KVM, and Amazon EC2-based virtual machines. Instead, it only considers the application data rate. Without requiring any calibration or training phase our adaptive compression scheme can improve the I/O throughput of virtual machines up to a factor of four as shown through extended experimental evaluations.
adaptive compression cloud computing i/o performance