Efficiency Assessment of Parallel Workloads on Virtualized Resources

Abstract

Abstract-In cloud computing, virtual containers on physical resources are provisioned to requesting users. Resource providers may pack as many containers as possible onto each of their physical machines, or may pack specific types and quantities of virtual containers based on user or system QoS objectives. Such elastic provisioning schemes for resource sharing may present major challenges to scientific parallel applications that require task synchronization during execution. Such elastic schemes may also inadvertently lower utilization of computing resources. In this paper, we describe the elasticity constraint effect and ripple effect that cause a negative impact to application response time and system utilization. We quantify the impact using real workload traces through simulation. Then, we demonstrate that some resource scheduling techniques can be effective in mitigating the impacts. We find that a tradeoff is needed among the elasticity of virtual containers, the complexity of scheduling algorithms, and the response time of applications.General Terms: scheduling, virtualization, parallel application I. INTRODUCTION The current trend in resource provisioning for utility and cloud computing is to provide compute resources as virtual resource containers using virtualization technologies. The most common realization is the use of virtual machine (VM) technology, in which a virtual container manager (VCM) abstracts and controls the physical resources allotted to one or more VMs. The VM abstraction simplifies the deployment of application environments across a wide range of physical systems. The VCM provides fine-grained control of the amount of shared resources to apportion to each VM when they are competing for them. This provides resource providers flexible resource allocation control to satisfy quality of service of requests and to build cost and profit models [1]. This flexibility also allows application execution to be tuned in different ways. For example, execution of a job proceeds faster than expected when its VM borrows idle CPU cycles from collocated VMs. Moreover, parallel applications need not limit the degree of parallelism to the number of physical machines.The simplicity of application deployment and the flexibility of virtual resource acquisition enabled by the Cloud is creating interest among users of CPU-intensive parallel applications [2]. However, this flexibility can present a unique challenge when scheduling and executing them.

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