Virtual Machine Provisioning Based on Analytical Performance and QoS in Cloud Computing Environments

TLDR

Cloud computing delivers IT resources as services, yet provisioning and delivery face barriers in workload modeling, virtualization, performance modeling, deployment, and monitoring, which, if resolved, would improve efficiency, cut costs, reduce resource under‑utilization, and enhance peak‑load performance. The authors propose a provisioning technique that automatically adapts to application workload changes to enable adaptive management and guarantee end‑user QoS in large, autonomous, highly dynamic cloud environments. They model application and cloud resource behavior with an analytical queueing‑network performance model and use workload information to provide intelligent input to a provisioner that has limited knowledge of the physical infrastructure, thereby improving system efficiency. Simulation experiments with production workload models show the technique detects changes in workload intensity and allocates virtualized resources to meet QoS targets.

Abstract

Cloud computing is the latest computing paradigm that delivers IT resources as services in which users are free from the burden of worrying about the low-level implementation or system administration details. However, there are significant problems that exist with regard to efficient provisioning and delivery of applications using Cloud-based IT resources. These barriers concern various levels such as workload modeling, virtualization, performance modeling, deployment, and monitoring of applications on virtualized IT resources. If these problems can be solved, then applications can operate more efficiently, with reduced financial and environmental costs, reduced under-utilization of resources, and better performance at times of peak load. In this paper, we present a provisioning technique that automatically adapts to workload changes related to applications for facilitating the adaptive management of system and offering end-users guaranteed Quality of Services (QoS) in large, autonomous, and highly dynamic environments. We model the behavior and performance of applications and Cloud-based IT resources to adaptively serve end-user requests. To improve the efficiency of the system, we use analytical performance (queueing network system model) and workload information to supply intelligent input about system requirements to an application provisioner with limited information about the physical infrastructure. Our simulation-based experimental results using production workload models indicate that the proposed provisioning technique detects changes in workload intensity (arrival pattern, resource demands) that occur over time and allocates multiple virtualized IT resources accordingly to achieve application QoS targets.