No abstract

With the growing adoption of virtualized datacenters and cloud hosting services, the allocation and sizing of resources such as CPU, memory, and I/O bandwidth for virtual machines (VMs) is becoming increasingly important. Accurate performance modeling of an application would help users in better VM sizing, thus reducing costs. It can also benefit cloud service providers who can offer a new charging model based on the VMs' performance instead of their configured sizes.In this paper, we present techniques to model the performance of a VM-hosted application as a function of the resources allocated to the VM and the resource contention it experiences. To address this multi-dimensional modeling problem, we propose and refine the use of two machine learning techniques: artificial neural network (ANN) and support vector machine (SVM). We evaluate these modeling techniques using five virtualized applications from the RUBiS and Filebench suite of benchmarks and demonstrate that their median and 90 th percentile prediction errors are within 4.36% and 29.17% respectively. These results are substantially better than regression based approaches as well as direct applications of machine learning techniques without our refinements. We also present a simple and effective approach to VM sizing and empirically demonstrate that it can deliver optimal results for 65% of the sizing problems that we studied and produces close-to-optimal sizes for the remaining 35%.

Abstract-Host-side SSD caches represent a powerful knob for improving and controlling storage performance and improve performance isolation. We present Centaur, as a host-side SSD caching solution that uses cache sizing as a control knob to achieve storage performance goals. Centaur implements dynamically partitioned per-VM caches with per-partition local replacement to provide both lower cache miss rate, better performance isolation and performance control for VM workloads. It uses SSD cache sizing as a universal knob for meeting a variety of workloadspecific goals including per-VM latency and IOPS reservations, proportional share fairness, and aggregate optimizations such as minimizing the average latency across VMs. We implemented Centaur for the VMware ESX hypervisor. With Centaur, times for simultaneously booting 28 virtual desktops improve by 42% relative to a non-caching system and by 18% relative to a unified caching system. Centaur also implements per-VM shares for latency with less than 5% error when running microbenchmarks, and enforces latency and IOPS reservations on OLTP workloads with less than 10% error.

The convergence of data, voice, and multimedia communication over digital networks, coupled with continuous improvement in network capacity and reliability has resulted in a proliferation of communication technologies. Unfortunately, despite these new developments, there is no easy way to build new application-specific communication services. The stovepipe approach used today for building new communication services results in rigid technology, limited utility, lengthy and costly development cycle, and difficulty in integration. In this paper, we introduce communication virtual machine (CVM) that supports rapid conception, specification, and automatic realization of new application-specific communication services through a user-centric, model-driven approach. We present the concept, architecture, modeling language, prototypical design, and implementation of CVM in the context of a healthcare application. Published by Elsevier Inc.

In a Grid computing environment, resources