Cloud computing is a computing hypothesis, where a huge group of systems is linked together in private, public, or hybrid\nnetwork, to offer dynamically amendable infrastructure for data storage, file storage, and application. With this emerging\ntechnology, application hosting, delivery, content storage, and reduced computation cost are achieved, and it acts as an essential\nmodule for the backbone of the Internet of Things (IoT). The efficiency of cloud service providers (CSP) could be improved by\nconsidering significant factors such as availability, reliability, usability, security, responsiveness, and elasticity. Assessment of these\nfactors leads to efficiency in designing a scheduler for CSP. These metrics also improved the quality of service (QoS) in the cloud.\nMany existing models and approaches evaluate these metrics. But these existing approaches do not offer efficient outcome. In this\npaper, a prominent performance model named the â??spectral expansion method (SPM)â? evaluates cloud reliability. The spectral\nexpansion method (SPM) is a huge technique useful in reliability and performance modelling of the computing system. This\napproach solves the Markov model of cloud service providers (CSP) to predict the reliability. The SPM is better compared to\nmatrix-geometric methods.
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