Multi-Objective Task Scheduling in Fog-Cloud Environments: LS-NSGA-II Optimization and Blockchain Integration for Enhanced Security and Efficiency

Task scheduling in fog-cloud environments, particularly when managing complex resource allocations and task priorities, presents a significant challenge. This paper proposes a novel approach that integrates the hill climbing algorithm which is a well-known Local Search technique in the Non dominated Sorting Genetic Algorithm II, called LS- NSGA-II to address these issues. In fact, the LS-NSGA- II is used to optimize task assignments across nodes, aiming to minimize execution time while maximizing task priority. Simultaneously, ensures an immutable, transparent, and auditable record of all scheduling decisions, validating each task assignment against resource constraints to enhance system reliability and security. The approach begins by enhancing the reliability of the system initializing a population of candidate solutions, which are iteratively refined using a local search algorithm and then processed by NSGA-II to explore the pareto front of potential solutions. At each iteration, blockchain is employed to record and validate the most effective scheduling decisions, ensuring that only feasible and optimal solutions are preserved. Furthermore, blockchain facilitates audits, detects anomalies, and maintains system consistency by comparing the current state with previously recorded transactions. This mechanism provides an additional layer of security, safeguarding task scheduling against tampering and manipulation. This work establishes a foundation for future research into decentralized and secure task scheduling mechanisms in dynamic, distributed computing environments, with potential applications in edge computing, IoT, and beyond.