To process big data in a cloud computing environment, a large scale of stream big data is one of the challenging issues. In particular, complex event processing is an emerging technology to handle massive events from multiple sources and process them in a near real time. In this paper, we analyze four performanceinfluencing factors on a virtualized event processing system: the number of query statements, garbage collection intervals, the quantity of virtual machine resources, and virtual CPU assignment types. In our experiments, we observe the performance effects of these performance parameters, by implementing and running an Esper-based event processing application on top of a Xen-based virtualized system. With experimental results, we analyze the memory consumption problem, and apply periodic garbage collection, to reduce unexpected memory consumption of JVM. Also, we analyze performance effects of the number of cores in a virtual machine (VM) and resource sharing among VMs. Under the virtualized environment in cloud computing infrastructure, one of the critical issues is the management of virtualized computing resources. Accordingly, we present the event processing performance on VMs as a function of virtual CPU assignment types and the number of VMs that share virtual CPUs.

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In this paper, we present a tool (named VMBootFailMonitor) to detect and analyze a failure of a VM boot creation caused by faults on virtual disks of a Xen-based VM. Also, we presents an architecture and detail analysis process of the virtual disk faults in our tool. Especially, VMBootFailMonitor provides a causual analysis result for a case of VM creation failure based on three modules which performs virtual disk analysis, virtualized system analysis and system log analysis. We also support a comparison result between boot times of normal VMs and fault•제1저자 : 구민오 •교신저자 : 민덕기

The rapid growth of networking and storage capacity allows collecting and analyzing massive amount of data by relying increasingly on scalable, flexible, and on-demand provisioned largescale computing resources. Virtualization is one of the feasible solution to provide large amounts of computational power with dynamic provisioning of underlying computing resources. Typically, distributed scientific applications for analyzing data run on cluster nodes to perform the same task in parallel. However, on-demand virtual disk provisioning for a set of virtual machines, called virtual cluster, is not a trivial task. This paper presents a feature model-based commonality and variability analysis system for virtual cluster disk provisioning to categorize types of virtual disks that should be provisioned. Also, we present an applicable case study to analyze common and variant software features between two different subgroups of the big data processing virtual cluster. Consequently, by using the analysis system, it is possible to provide an ability to accelerate the virtual disk creation process by reducing duplicate software installation activities on a set of virtual disks that need to be provisioned in the same virtual cluster.