Affiliation:
1. Department of Information Technology, Jerusalem College of Engineering, Chennai, Tamilnadu, India
2. Department of Computer Science and Engineering, Jeppiaar Engineering College, Chennai, Tamilnadu, India
Abstract
Cloud computing technology is playing a major role in the industry and real-life, for providing fast services such as data sharing and allocating the cloud resources that are paid and truly required. In this scenario, the cloud users are scheduled according to the rule-based systems for attempting to automate the matching between computing requirements and resources. Even though, the majority auto-scaling algorithms only helped as indicators for simple resource utilization and also not considered both cloud user needs and budget concerns. For this purpose, we propose a new model which is the combination of auto-scaling algorithms, resource allocation and scheduling for allocating the appropriate resources and scheduled them. This model consists of three new algorithms namely Grey Wolf Optimization and Fuzzy rules based Resource allocation and Scheduling Algorithm (GWOFRSA), Auto-Scaling Algorithm for Cloud based Web Application (ASACWA) and Auto-Scaling Algorithm for handling Distributed Computing Tasks (ASADCT). Here, we introduce new auto-scaling algorithms for enhancing the performance of cloud services. In this work, the optimization technique is used to predict the cloud server workload, resource requirements and it also uses fuzzy rules for monitoring the resource utilization and the size of virtual machine allocation process. According to the workload prediction, the completion time is estimated for each cloud server. The experiments are conducted by using a simulator called CloudSim environment of Java programming and compared with the existing works available in this direction in terms of resource utilization and enhance the cloud performance with better Quality of Service of Virtual Machine allocation, Missed Deadline, Demand Satisfaction, Power Utilization, CPU Load and throughput.
Subject
Artificial Intelligence,General Engineering,Statistics and Probability
Reference31 articles.
1. ElasticSFC: Auto-scaling techniques for elastic service function chaining in network functions virtualization-based clouds;Toosi;Journal of Systems and Software,2019
2. Jindal A. , Podolskiy V. and Gerndt M. , Multilayered Cloud Applications Autoscaling Performance Estimation, 2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2), 2017, 24–31.
3. AlexandruIosup, An Experimental Performance Evaluation of Autoscaling Policies for Complex Workflows;Ilyushkin;ICPE,2017
4. Chameleon: A Hybrid, Proactive Auto-Scaling Mechanism on a Level-Playing Field;Bauer;IEEE Transactions on Parallel and Distributed Systems,2019
5. HAS: Hybrid auto-scaler for resource scaling in cloud environment;BibalBenifa;Journal of Parallel and Distributed Computing,2018