A Novel Weight-Assignment Load Balancing Algorithm for Cloud Applications

Abstract

Web applications commonly suffer from flash crowds and resource failure, resulting in performance degradation. Flash crowds are large, sudden, yet legitimate influxes of user requests that constitute a critical problem because of their potential economic damage. For cloud providers, resource estimation is challenging, while distributing workload and sustaining performance. To alleviate flash crowds and resource failure problems, we propose a novel weight assignment load balancing algorithm that combines five carefully selected server metrics to efficiently distribute the workload of three-tier web applications among virtual machines. We experimentally characterised, using a private cloud running OpenStack, the load distribution ability of our proposed novel algorithm, as well as a baseline algorithm and round-robin algorithm. We compared the performance of the three algorithms by simulating resource failures and flash crowds, while carefully measuring response times. Our experimental results show that our approach improves average response times by 12.5% when compared to the baseline algorithm and 22.3% when compared to the round-robin algorithm in the flash crowds’ situation. In addition, average response time was improved by 20.7% when compared to the baseline algorithm and 21.4% when compared to the round-robin algorithm in resource failure situations. These experiments show that our novel algorithm is more resilient to fluctuating loads and resource failures than baseline algorithms.