Performance metrics for consolidated servers

In spite of the widespread adoption of virtualization and consol- idation, there exists no consensus with respect to how to bench- mark consolidated servers that run multiple guest VMs on the same physical hardware. For example, VMware proposes VMmark which basically computes the geometric mean of normalized throughput values across the VMs; Intel uses vConsolidate which reports a weighted arithmetic average of normalized throughput values. These benchmarking methodologies focus on total system through- put (i.e., across all VMs in the system), and do not take into account per-VM performance. We argue that a benchmarking methodology for consolidated servers should quantify both total system through- put and per-VM performance in order to provide a meaningful and precise performance characterization. We therefore present two performance metrics, Total Normalized Throughput (TNT) to characterize total system performance, and Average Normalized Reduced Throughput (ANRT) to characterize per-VM performance. We compare TNT and ANRT against VMmark using published performance numbers, and report several cases for which the VM- mark score is misleading. This is, VMmark says one platform yields better performance than another, however, TNT and ANRT show that both platforms represent different trade-offs in total system throughput versus per-VM performance. Or, even worse, in a cou- ple cases we observe that VMmark yields opposite conclusions than TNT and ANRT, i.e., VMmark says one system performs better than another one which is contradicted by TNT/ANRT performance characterization

Measurements based performance analysis of Web services

Web services are increasingly used to enable interoperability and flexible integration of software systems. In this thesis we focus on measurement-based performance analysis of an e-commerce application which uses Web services components to execute business operations. In our experiments we use a session-oriented workload generated by a tool developed accordingly to TPC-W specification. The empirical results are obtained for two different user profiles, Browsing and Ordering, under different workload intensities. In addition to variation in workloads we also study the applications performance when Web services are implemented using .NET and J2EE. Unlike the previous work which was focused on the overall server response time and throughput, we present Web interaction, software architecture, and hardware resource level analysis of the system performance. In particular, we propose a method for extracting component level response times from the application server logs and study the impact of Web services and other components on the server performance. The results show that the response times of Web services components increase significantly under higher workload intensities when compared to other components. (Abstract shortened by UMI.)

A model-based approach for automatic recovery from memory leaks in enterprise applications

Large-scale distributed computing systems such as data centers are hosted on heterogeneous and networked servers that execute in a dynamic and uncertain operating environment, caused by factors such as time-varying user workload and various failures. Therefore, achieving stringent quality-of-service goals is a challenging task, requiring a comprehensive approach to performance control, fault diagnosis, and failure recovery. This work presents a model-based approach for fault management, which integrates limited lookahead control (LLC), diagnosis, and fault-tolerance concepts that: (1) enables systems to adapt to environment variations, (2) maintains the availability and reliability of the system, (3) facilitates system recovery from failures. We focused on memory leak errors in this thesis. A characterization function is designed to detect memory leaks. Then, a LLC is applied to enable the computing system to adapt efficiently to variations in the workload, and to enable the system recover from memory leaks and maintain functionality

ACTS in Need: Automatic Configuration Tuning with Scalability Guarantees

To support the variety of Big Data use cases, many Big Data related systems expose a large number of user-specifiable configuration parameters. Highlighted in our experiments, a MySQL deployment with well-tuned configuration parameters achieves a peak throughput as 12 times much as one with the default setting. However, finding the best setting for the tens or hundreds of configuration parameters is mission impossible for ordinary users. Worse still, many Big Data applications require the support of multiple systems co-deployed in the same cluster. As these co-deployed systems can interact to affect the overall performance, they must be tuned together. Automatic configuration tuning with scalability guarantees (ACTS) is in need to help system users. Solutions to ACTS must scale to various systems, workloads, deployments, parameters and resource limits. Proposing and implementing an ACTS solution, we demonstrate that ACTS can benefit users not only in improving system performance and resource utilization, but also in saving costs and enabling fairer benchmarking

A Performance Comparison of Hypervisors for Cloud Computing

The virtualization of IT infrastructure enables the consolidation and pooling of IT resources so that they can be shared over diverse applications to offset the limitation of shrinking resources and growing business needs. Virtualization provides a logical abstraction of physical computing resources and creates computing environments that are not restricted by physical configuration or implementation. Virtualization is very important for cloud computing because the delivery of services is simplified by providing a platform for optimizing complex IT resources in a scalable manner, which makes cloud computing more cost effective. Hypervisor plays an important role in the virtualization of hardware. It is a piece of software that provides a virtualized hardware environment to support running multiple operating systems concurrently using one physical server. Cloud computing has to support multiple operating environments and Hypervisor is the ideal delivery mechanism. The intent of this thesis is to quantitatively and qualitatively compare the performance of VMware ESXi 4.1, Citrix Systems Xen Server 5.6 and Ubuntu 11.04 Server KVM Hypervisors using standard benchmark SPECvirt_sc2010v1.01 formulated by Standard Performance Evaluation Corporation (SPEC) under various workloads simulating real life situations