Spectral Expansion Method for Cloud Reliability Analysis

Abstract

Cloud computing is a computing hypothesis, where a huge group of systems is 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 are achieved, and it acts as an essential module for the 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 a scheduler for CSP. These metrics also improved the quality of service (QoS) in the cloud. Many existing models and approaches evaluate these metrics. But these existing approaches do not offer efficient outcome. In this paper, a prominent performance model named the "spectral expansion method (SPM)" evaluates cloud reliability. The spectral expansion method (SPM) is a huge technique useful in reliability and performance modelling of the computing system. This approach solves the Markov model of cloud service providers (CSP) to predict the reliability. The SPM is better compared to matrix-geometric methods.