Efficient Resource Allocation in Cloud Computing Using Hungarian Optimization in Aws

Abstract

Abstract Cloud computing is a recent technology which allows on-demand availability of computing services as well as resources to users without having direct control by the user. The various resources offered by cloud technology needs appropriate task scheduling strategies to provide better Quality of Service (QoS). Moreover, the Cloud Service Provider (CSP) concentrates more on providing various resources to a user based on demand. Thus, resource allocation occupies a crucial task in offering better QoS and profits to CSP. Although several techniques were deployed for various resource allocations in the cloud, the challenges still risk the CSP in accomplishing improvised resource allocation due to over demand and under availability of resources which results in increased makespan as well as Virtual Machine (VM) utilization factor. For this reason, this paper aims to introduce a novel resource allocation model that comprises appropriate resource discovery, task scheduling, and then resource allocation. The proposed methodology uses the EC2 Lambda instance for the resource discovery process. Depending on the EC2 Lambda instance, the recommended process is grouped to the available resources. Resource discovery is done by finding all the resources that meet the user’s needs and guiding them to nearby innovation. Initially, the executed jobs are recognized or analyzed and the available resources are grouped. Simultaneously the resources are allocated with the help of a Hungarian optimization technique. Eventually, the empirical analysis reveals the performance of the proposed resource allocation model in terms of CPU and network utilization.