Abstract
Cloud computing consists of an advanced set of technologies that allow cloud providers to offer computing resources such as infrastructure, platforms and applications to be accessible over the Internet as services. Cloud computing relies on virtualization of resources in the cloud data centers, where a set of Virtual Machines (VMs) are deployed on Physical Machines (PMs) to provision and serve user requests. Due to the dynamic nature of cloud environments and complexity of resources virtualization, as well as the diversity of user’s requests, developing effective techniques to evaluate and analyze the performance of cloud centers has become highly required. In this paper, we propose the use of probabilistic model checking as an effective framework for the evaluation and the performance analysis of resource provisioning in the cloud. Based on an analytical model for resource provisioning in Infrastructure-as-a-Service (IaaS) cloud, we build a stochastic model using the probabilistic model checker PRISM and analyze it against a useful set of probabilistic and reward properties that help to measure and analyze cloud performance in an efficient way
Highlights
Cloud computing is a novel information technology that provides access to different IT services on demand over the Internet
We aim to show how probabilistic model checking can be used for the performance analysis and evaluation of IaaS cloud based on analytical modeling methods using Continuous-time Markov Chains (CTMC)
The authors described the performance model of concurrent Virtual Machines (VMs) live migration operations as a CTMC in PRISM language, and it has been verified against two main quantitative properties regarding the operations that can be stacked in waiting state at sender side, and the operations that are executed at server side
Summary
Since resources provisioning and usage is highly variable and uncertain, and since the arrival of customer requests is stochastic, these methods are stochastic in general, and employ queuing theory with different buffers to cope with a large number of requests given the available resources, and performance measures are quantified using probabilistic methods These stochastic methods can effectively capture the uncertainty beyond cloud provisioning behavior and estimate perfectly cloud metrics. We aim to show how probabilistic model checking can be used for the performance analysis and evaluation of IaaS cloud based on analytical modeling methods using CTMCs. Probabilistic model checking has appeared as an extension of model checking for analyzing systems that exhibit stochastic behavior.
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