ÐIn this paper, we consider a new, session-based workload for measuring web server performance. We define a session as a sequence of client's individual requests. Using a simulation model, we show that an overloaded web server can experience a severe loss of throughput measured as a number of completed sessions compared against the server throughput measured in requests per second. Moreover, statistical analysis of completed sessions reveals that the overloaded web server discriminates against longer sessions. For e-commerce retail sites, longer sessions are typically the ones that would result in purchases, so they are precisely the ones for which the companies want to guarantee completion. To improve web QoS for commercial web servers, we introduce a session-based admission control (SBAC) to prevent a web server from becoming overloaded and to ensure that longer sessions can be completed. We show that a web server augmented with the admission control mechanism is able to provide a fair guarantee of completion, for any accepted session, independent of a session length. This provides a predictable and controllable platform for web applications and is a critical requirement for any e-business. Additionally, we propose two new adaptive admission control strategies, hybrid and predictive, aiming to optimize the performance of SBAC mechanism. These new adaptive strategies are based on a selftunable admission control function, which adjusts itself accordingly to variations in traffic loads.

Abstract. Virtual machines (VMs) have recently emerged as the basis for allocating resources in enterprise settings and hosting centers. One benefit of VMs in these environments is the ability to multiplex several operating systems on hardware based on dynamically changing system characteristics. However, such multiplexing must often be done while observing per-VM performance guarantees or service level agreements. Thus, one important requirement in this environment is effective performance isolation among VMs. In this paper, we address performance isolation across virtual machines in Xen [1]. For instance, while Xen can allocate fixed shares of CPU among competing VMs, it does not currently account for work done on behalf of individual VMs in device drivers. Thus, the behavior of one VM can negatively impact resources available to other VMs even if appropriate per-VM resource limits are in place.In this paper, we present the design and evaluation of a set of primitives implemented in Xen to address this issue. First, XenMon accurately measures per-VM resource consumption, including work done on behalf of a particular VM in Xen's driver domains. Next, our SEDF-DC scheduler accounts for aggregate VM resource consumption in allocating CPU. Finally, ShareGuard limits the total amount of resources consumed in privileged and driver domains based on administrator-specified limits. Our performance evaluation indicates that our mechanisms effectively enforce performance isolation for a variety of workloads and configurations.

The primary motivation for uptake of virtualization has been resource isolation, capacity management and resource customization allowing resource providers to consolidate their resources in virtual machines. Various approaches have been taken to integrate virtualization in to scientific Grids especially in the arena of High Performance Computing (HPC) to run grid jobs in virtual machines, thus enabling better provisioning of the underlying resources and customization of the execution environment on runtime. Despite the gains, virtualization layer also incur a performance penalty and its not very well understood that how such an overhead will impact the performance of systems where jobs are scheduled with tight deadlines. Since this overhead varies the types of workload whether they are memory intensive, CPU intensive or network I/O bound, and could lead to unpredictable deadline estimation for the running jobs in the system. In our study, we have attempted to tackle this problem by developing an intelligent scheduling technique for virtual machines which monitors the workload types and deadlines, and calculate the system over head in real time to maximize number of jobs finishing within their agreed deadlines.

Abstract-The multi-tier implementation has become the industry standard for developing scalable client-server enterprise applications. Since these applications are performance sensitive, effective models for dynamic resource provisioning and for delivering quality of service to these applications become critical. Workloads in such environments are characterized by client sessions of interdependent requests with changing transaction mix and load over time, making model adaptivity to the observed workload changes a critical requirement for model effectiveness. In this work, we apply a regression-based approximation of the CPU demand of client transactions on a given hardware. Then we use this approximation in an analytic model of a simple network of queues, each queue representing a tier, and show the approximation's effectiveness for modeling diverse workloads with a changing transaction mix over time. Using the TPC-W benchmark and its three different transaction mixes we investigate factors that impact the efficiency and accuracy of the proposed performance prediction models. Experimental results show that this regression-based approach provides a simple and powerful solution for efficient capacity planning and resource provisioning of multi-tier applications under changing workload conditions.

Abstract-Advances in virtualization technology are enabling the creation of resource pools of servers that permit multiple application workloads to share each server in the pool. Understanding the nature of enterprise workloads is crucial to properly designing and provisioning current and future services in such pools. This paper considers issues of workload analysis, performance modeling, and capacity planning. Our goal is to automate the efficient use of resource pools when hosting large numbers of enterprise services. We use a trace based approach for capacity management that relies on i) the characterization of workload demand patterns, ii) the generation of synthetic workloads that predict future demands based on the patterns, and iii) a workload placement recommendation service. The accuracy of capacity planning predictions depends on our ability to characterize workload demand patterns, to recognize trends for expected changes in future demands, and to reflect business forecasts for otherwise unexpected changes in future demands. A workload analysis demonstrates the burstiness and repetitive nature of enterprise workloads. Workloads are automatically classified according to their periodic behavior. The similarity among repeated occurrences of patterns is evaluated. Synthetic workloads are generated from the patterns in a manner that maintains the periodic nature, burstiness, and trending behavior of the workloads. A case study involving six months of data for 139 enterprise applications is used to apply and evaluate the enterprise workload analysis and related capacity planning methods. The results show that when consolidating to 8 processor systems, we predicted future per-server required capacity to within one processor 95% of the time. The accuracy of predictions for required capacity suggests that such resource savings can be achieved with little risk.