Spectral Expansion Method for Cloud Reliability Analysis

Abstract

Cloud Computing is a computing hypothesis, where a huge group of systems linked together in private, public or hybrid network, to offer dynamically amendable infrastructure for data storage, file storage and application. With this emerging technology, application hosting, delivery, content storage, and reduced computation cost, and it acts as an essential module for backbone of the Internet of Things (IOT). The efficiency of cloud Service providers (CSP) could be improved by considering significant factors such as availability, reliability, usability, security, responsiveness, and elasticity. Assessment of these factors leads to efficiency in designing scheduler for CSP. This metrics also improved the Quality of Service (QoS) in cloud. Many existing model and approaches evaluate this metrics. But these existing approaches doesn't offer efficient outcome. In this paper, a prominent performance model named as Spectral Expansion Method (SPM) evaluates cloud reliability. Spectral expansion Method (SPM) is a huge technique useful in reliability and performance modelling of computing system. This approach solves the Markov model of Cloud service Provider (CSP) to predict the reliability. The SPM is better compared to matrix geometric methods.