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 90th 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%.

Performance models provide the ability to predict application performance for a given set of hardware resources and are used for capacity planning and resource management. Traditional performance models assume the availability of dedicated hardware for the application. With growing application deployment on virtualized hardware, hardware resources are increasingly shared across multiple virtual machines. In this paper, we build performance models for applications in virtualized environments. We identify a key set of virtualization architecture independent parameters that influence application performance for a diverse and representative set of applications. We explore several conventional modeling techniques and evaluate their effectiveness in modeling application performance in a virtualized environment. We propose an iterative model training technique based on artificial neural networks which is found to be accurate across a range of applications. The proposed approach is implemented as a prototype in Xen-based virtual machine environments and evaluated for accuracy, sensitivity to the training process, and overhead. Median modeling error in the range 1.16-6.65% across a diverse application set and low modeling overhead suggest the suitability of our approach in production virtualized environments.

Database storage management at data centers is a manual, time-consuming, and error-prone task. Such management involves regular movement of database objects across storage nodes in an attempt to balance the I/O bandwidth utilization across disk drives. Achieving such balance is critical for avoiding I/O bottlenecks and thereby maximizing the utilization of the storage system. However, manual management of the aforesaid task, apart from increasing administrative costs, encumbers the greater risks of untimely and erroneous operations. We address the preceding concerns with STORM, an automated approach that combines low-overhead information gathering of database access and storage usage patterns with efficient analysis to generate accurate and timely hints for the administrator regarding data movement operations. STORM's primary objective is minimizing the volume of data movement required (to minimize potential down-time or reduction in performance) during the reconfiguration operation, with the secondary constraints of space and balanced I/O-bandwidth-utilization across the storage devices. We analyze and evaluate STORM theoretically, using a simulation framework, as well as experimentally. We show that the dynamic data layout reconfiguration problem is NP-hard and we present a heuristic that provides an approximate solution in O ( Nlog ( N / M ) + ( N / M ) 2 ) time, where M is the number of storage devices and N is the total number of database objects residing in the storage devices. A simulation study shows that the heuristic converges to an acceptable solution that is successful in balancing storage utilization with an accuracy that lies within 7% of the ideal solution. Finally, an experimental study demonstrates that the STORM approach can improve the overall performance of the TPC-C benchmark by as much as 22%, by reconfiguring an initial random, but evenly distributed, placement of database objects.