An Improved Hierarchal Agglomerative Grouping Process and Bi-Objective Fusion Optimization Process for Optimum Source Detection

Abstract

Computing in the cloud refers to a model that offers inexpensive, scalable computing resources like CPU, storage, and network bandwidth. Allows users to access a shared pool of resources via the internet on an as-needed, pay-per-use basis. In order to organize tools, this chapter suggests using the Hierarchical Agglomerative clustering algorithm. The time spent searching through a pool of available resources can be minimized by categorizing them. Thanks to the categorization of resources, the one needed to fulfill a request can be found and assigned in a flash. The following outline is used for this section. The paper explains the rationale behind the proposed approach and details the resource discovery method that underpins it. Describes and evaluates the proposed system's performance by utilizing a hybrid of the artificial bee colony (ABC) algorithm and the cuckoo search (CS) algorithm to allocate resources to requests. There are many issues with efficient resource allocation that cannot be solved using current methods. The existing research work identified and tackled the problem of optimizing the parameters (make span, execution time, deadline, execution cost, etc.) using optimization algorithms. However, most existing algorithms require more time to allocate resources because of the vast number of resources available in the cloud. The process also depends on the efficiency of the underlying optimization algorithm.