Abstract-Server virtualization offers the ability to slice large, underutilized physical servers into smaller, parallel virtual machines (VMs), enabling diverse applications to run in isolated environments on a shared hardware platform. Effective management of virtualized cloud environments introduces new and unique challenges, such as efficient CPU scheduling for virtual machines, effective allocation of virtual machines to handle both CPU intensive and I/O intensive workloads. Although a fair number of research projects have dedicated to measuring, scheduling, and resource management of virtual machines, there still lacks of in-depth understanding of the performance factors that can impact the efficiency and effectiveness of resource multiplexing and resource scheduling among virtual machines. In this paper, we present our experimental study on the performance interference in parallel processing of CPU and network intensive workloads in the Xen Virtual Machine Monitors (VMMs). We conduct extensive experiments to measure the performance interference among VMs running network I/O workloads that are either CPU bound or network bound. Based on our experiments and observations, we conclude with four key findings that are critical to effective management of virtualized cloud environments for both cloud service providers and cloud consumers. First, running networkintensive workloads in isolated environments on a shared hardware platform can lead to high overheads due to extensive context switches and events in driver domain and VMM. Second, co-locating CPUintensive workloads in isolated environments on a shared hardware platform can incur high CPU contention due to the demand for fast memory pages exchanges in I/O channel. Third, running CPUintensive workloads and network-intensive workloads in conjunction incurs the least resource contention, delivering higher aggregate performance. Last but not the least, identifying factors that impact the total demand of the exchanged memory pages is critical to the indepth understanding of the interference overheads in I/O channel in the driver domain and VMM.

Abstract-User-perceived performance continues to be the most important QoS indicator in cloud-based data centers today. Effective allocation of virtual machines (VMs) to handle both CPU intensive and I/O intensive workloads is a crucial performance management capability in virtualized clouds. Although a fair amount of researches have dedicated to measuring and scheduling jobs among VMs, there still lacks of in-depth understanding of performance factors that impact the efficiency and effectiveness of resource multiplexing and scheduling among VMs. In this paper, we present the experimental research on performance interference in parallel processing of CPU-intensive and network-intensive workloads on Xen virtual machine monitor (VMM). Based on our study, we conclude with five key findings which are critical for effective performance management and tuning in virtualized clouds. First, colocating network-intensive workloads in isolated VMs incurs high overheads of switches and events in Dom0 and VMM. Second, colocating CPU-intensive workloads in isolated VMs incurs high CPU contention due to fast I/O processing in I/O channel. Third, running CPU-intensive and network-intensive workloads in conjunction incurs the least resource contention, delivering higher aggregate performance. Fourth, performance of network-intensive workload is insensitive to CPU assignment among VMs, whereas adaptive CPU assignment among VMs is critical to CPU-intensive workload. The more CPUs pinned on Dom0 the worse performance is achieved by CPU-intensive workload. Last, due to fast I/O processing in I/O channel, limitation on grant table is a potential bottleneck in Xen. We argue that identifying the factors that impact the total demand of exchanged memory pages is important to the in-depth understanding of interference costs in Dom0 and VMM.

Abstract-Server consolidation and application consolidation through virtualization are key performance optimizations in cloud-based service delivery industry. In this paper, we argue that it is important for both cloud consumers and cloud providers to understand the various factors that may have significant impact on the performance of applications running in a virtualized cloud. This paper presents an extensive performance study of network I/O workloads in a virtualized cloud environment. We first show that current implementation of virtual machine monitor (VMM) does not provide sufficient performance isolation to guarantee the effectiveness of resource sharing across multiple virtual machine instances (VMs) running on a single physical host machine, especially when applications running on neighboring VMs are competing for computing and communication resources. Then we study a set of representative workloads in cloud-based data centers, which compete for either CPU or network I/O resources, and present the detailed analysis on different factors that can impact the throughput performance and resource sharing effectiveness. For example, we analyze the cost and the benefit of running idle VM instances on a physical host where some applications are hosted concurrently. We also present an in-depth discussion on the performance impact of colocating applications that compete for either CPU or network I/O resources. Finally, we analyze the impact of different CPU resource scheduling strategies and different workload rates on the performance of applications running on different VMs hosted by the same physical machine.

Abstract-Virtualization

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Abstract-With Cloud Computing gaining tremendous importance in the recent past, understanding low-level implications of the cloud infrastructure becomes necessary. One of the key technologies deployed in large Cloud infrastructures namely the Amazon EC2 for providing isolation and separate protection domains for multiple clients is virtualization. Therefore, identifying the performance bottlenecks in a virtualized setup and understanding the implications of workload combinations and resource configurations on the overall I/O performance helps both the cloud providers in managing their infrastructure efficiently and also their customers by means of better performance.In this paper we present the measurement results of detailed experiments conducted on a virtualized setup focusing on the storage I/O performance. We categorize our experimental evaluation into four components, each of which presenting some significant factors that affect storage I/O performance. Our experimental results can be useful for cloud application developers to tune their applications for better I/O performance and for the cloud service providers to make more effective decisions on resource provisioning and workload scheduling.

Web service composition is to integrate component services for providing a value-added new service. With the growing number of component services and their dynamic nature, the centralized composition model can't manage them efficiently and accurately. In this paper, we proposed a distributed hash table (DHT)-based peer-to-peer (P2P) service composition framework, called ServiceStore. Compared with the central control in centralized model, in our ServiceStore, service selection and composition are distributed to the involved task brokers, requesting nodes and service nodes. Furthermore, a simple parallel service selection approach which can still satisfy global constraints is proposed and implemented in our multi-role cooperation (MRC) protocol. The results of experimental evaluation show that ServiceStore can achieve high scalability and efficiency.

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