Energy efficient task scheduling and resource management in a cloud environment using unique ranking and hsrlba approach

Abstract

Recently, the demand for processing and storing data has been newlinesignificantly raising in various domains such as government, finance, and newlinescience. Cloud and big data are considered major keystones in the era of data newlinescience. Therefore, a system that assists big data and holds them through newlinecloud computing has been established and utilized prominently. Big data is newlineaccountable for processing and storing the data, whereas the cloud is newlineresponsible for fault tolerant, reliable, and scalable environments in big data newlinedistributed management systems. Several social data accords to overall data newlinetraffic, and cloud storage is described as one of the methods employed for newlineassisting the big data components. However, in the cloud, due to centralized newlineproblems with job scheduling, Virtual Machines (VMs) are utilized to newlineschedule jobs. From a particular VM, waiting for a service reply or newlinetransferring to a large community from a far apart VM enhances the newlinecompletion time and cost of data traffic. newlineIn this work, a global proxy service model has been presented newlinecommonly for big data traffic, and it is termed as global service model newline(GloSM). Here, GloSM has certainly intercepted and served the big data newlinetraffic from various systems without interrupting other internet applications. newlineAlso, the local service model (LoSM) and GloSM are proposed merely to newlinesatisfy the service request from several hosts corresponding to service activity newlineand proximity. The main objective of LoSM and GloSM is to support users in newlinenavigating several jobs to active VMs. It is a novel method that searches and newlinestores unstructured data using several VMs. If there are any service misses in newlinethe network, the hybridrestoration assists in establishing service to the users. newline