Cost-effective clonal selection and AIS-based load balancing in cloud computing environment

Abstract

One of the challenges in cloud computing is increasing efficiency, which calls for the application of task scheduling and load balancing. Numerous load-balancing methods that have been suggested for the cloud environment can increase efficiency. The Throttled policy is one of them that aids in evenly distributing user requests among virtual machines. In the first part of this article, we examined the cost of the Clonal selection algorithm with Throttled load balancing Policy and the Clonal selection algorithm with Distributed Service Broker Policy and contrasted them to the clonal selection and other immune systems mechanisms. Additionally, we suggest two additional mechanisms based on the artificial immune system. In the two proposed mechanisms, algorithms are introduced so that by benefiting from load balancing policies when receiving requests from users, in the shortest possible time and the fastest processing of data and with the lowest cost, they respond to users. In the second part, we introduce two other new algorithms that were created as a result of combining an evolutionary algorithm with the artificial immune system mechanisms. Algorithms have been evaluated and compared with other algorithms using the Cloud Analyst simulator in terms of average response time, average data center processing time, total data transfer cost, and total cost. The results attained show how much better the suggested algorithms are.

Adaptive Threshold Tuning-based Load Balancing (ATTLB) for Cost Minimization in Cloud Computing

“…Several researchers have highlighted that dynamic pricing models in public clouds call for adaptive load distribution strategies that can optimize thresholds in line with price fluctuations [26] [27] [28]. However, most existing studies have focused heavily on VM provisioning and placement policies [29] [30], traffic distribution algorithms [31] [32], and auto-scaling techniques [33] [34] for load-balancing.…”