A New Non-Stigmergic-Ant Algorithm to Make Load Balancing Resilient in Big Data Processing for Enterprises

Abstract

Due to the continuous evolution of the Big Data phenomenon, data processing in Business Big Data Analytics (BBDA) needs new advanced load balancing techniques. This chapter proposes a new algorithm based on a nonstigmergic approach to address these concerns. The algorithm imitates a specific species of ants that communicate by the acoustics in situations of threats. Besides, the research methodology in this study presents a methodic filtration of the relevant metrics before carrying out the benchmarking trials of several ant-colony algorithms (i.e., makespan, response time, throughput, memory and CPU utilization, etc.). The experimentations' outcomes show the effectiveness of the proposed approach that might empower the research efforts in big data analytics, business intelligence, and intelligent autonomous software agents. The main objective of this research is to contribute to reinforcing the resilience of the Big Data processing environment for enterprises.

Keywords: Big Data processing, Business Big Data Analytics, Load balancing, Swarm intelligence, Workload Management.

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