Tabassum, Nadia and Khan, Saleem Khan and Abbas, Sagheer and Alyas, Tahir and Athar, Atifa and Khan, Muhammad Adnan (2019) Intelligent reliability management in hyper-convergence cloud infrastructure using fuzzy inference system. EAI Endorsed Transactions on Scalable Information Systems, 6 (23): e1. ISSN 2032-9407
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Abstract
Hyper-convergence is a new innovation in data center technology, it changes the way clouds manage and maintain enterprise IT infrastructure. Hyper-convergence is more efficient and basically agile technology environment. Cloud computing is a latest technology due to provision of latest cloud services over the internet. The cloud service providers cannot promise accurate reliability of their services i.e. problem in provisioning of software or hardware failure etc. Reliability of cloud computing services depends on the ability of fault tolerance during the execution of services. There are so many factors can cause faults, such as network failure, browser crash, request time out or hacker attacks. When users are facing these types of faults, they usually resubmit their requests. However, if there is any key element involved in faults or errors, additional action may be needed to deal with system logs. If there is anomaly behavior occurred in faulted virtual machine, these VMs may need extra attention from cloud system protection and security point of view. In this paper, provision of reliability management in hyper-convergence cloud infrastructure is proposed and self-healing techniques in software as a service on the basis of failure in cloud services. Intelligent cloud service reliability framework will increase the reliability during execution of cloud service.
Item Type: | Article |
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Uncontrolled Keywords: | Hyper-convergence, Cloud Computing, Datacenter Software as a service, reliability management |
Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science QA75 Electronic computers. Computer science |
Depositing User: | EAI Editor II. |
Date Deposited: | 08 Oct 2020 13:53 |
Last Modified: | 08 Oct 2020 13:53 |
URI: | https://eprints.eudl.eu/id/eprint/689 |