Studies on prediction of resource contention in cloud and edge computing architectures using markov models

Abstract

Cloud computing provides an on-demand access of the services newlinehosted by the cloud provider through the internet. Cloud users can pay when newlinethe services are needed and disconnect when not needed. The effort of purchasing, newlineinstalling, configuring and managing resources becomes the responsibility newlineof the cloud service provider. The resources requested by the users should be newlineallocated as per the desired performance mentioned by the users in the Service newlineLevel Agreement (SLA). Hence, resource management across the cloud is of newlinesignificant importance in cloud computing. newlineEdge computing works jointly with the cloud to provide flexible solutions. newlineFor real-time applications that generate a large amount of data, edge newlinecomputing is the ideal solution. It provides real-time analytics where processing newlinetakes place closer to the asset, thereby reducing the reliance to the centralised newlinecloud. Even if the connectivity to the cloud cannot be made immediately, edge newlinecomputing enables processing and storage across the local network. At the same newlinetime, the cloud provides a centralized storage location for large-scale data analytics. newline