Sparrow Search Algorithm-Based Decomposition for Task Scheduling in Fog-Cloud System Integrating Blockchain Technology

Abstract

In the rapidly evolving landscape of distributed computing, the integration of fog computing, cloud paradigms, and blockchain technology has emerged as an innovative approach to enhance the efficiency and security of task scheduling. This article introduces a novel method using the Sparrow Search Algorithm (SSA), along with its decomposition variant SSA/D, to optimize task scheduling within this integrated system. This paper presents a comprehensive framework that outlines the unique characteristics of fog, cloud, and blockchain environments, addressing their individual constraints while leveraging their combined strengths. The adaptability and robustness of SSA/D are employed to address the multi-objective nature of the scheduling problem, focusing on minimizing latency, energy consumption, and financial cost while ensuring data integrity through the blockchain's immutable ledger. We have employed the Sparrow Search Algorithm (SSA), a recent optimization method proven effective in other applications. Our main contribution is the integration of a decomposition strategy to enhance the exploration of the search space and enable parallel execution of the SSA process. Experimental results show that the proposed approach outperforms existing algorithms in terms of both efficiency and effectiveness, providing significant improvements over popular task scheduling metaheuristics. This paper not only offers a new algorithmic solution but also sets a precedent for future research in integrated fog-cloud-blockchain systems, with a view towards integrating machine learning techniques to further optimize these problems.