SPO: Security-Aware Multi-Objective IoT Task Scheduling in Distributed Cloud Environment

Abstract

The fast growth of Internet of Things (IoT) applications within the distributed cloud has posed considerable difficulties in terms of secure, scalable and efficient task scheduling and adherence to strict Quality of Service (QoS) and Service Level Agreement (SLA) requirements. In order to overcome these challenges, this paper presents a proposal of an Intelligent Security-Aware Metaheuristic-based schedule tool using the Stochastic Paint Optimizer (SPO) on the CloudSim simulation platform. The suggested model combines security requirements and adaptive stochastic exploration to optimize the allocation of tasks with the help of the Google Cloud Jobs Dataset (GoCJ) and achieve a better system robustness and performance. The quality of the SPO method is compared to the state-of-the-art methods, such as Particle Swarm Optimization (PSO), Deep Reinforcement Learning (DRL), and Deep Deterministic Policy Gradient (DDPG). The experimental findings show that the proposed SPO method has high performance improvements, such as a latency decrease of 27.8%, scalability improvement of 31.4%, SLA compliance increase of 29.6%, and QoS improvement of 33.2% in comparison to the current methods. These findings suggest that the SPO-based scheduling framework does not only improve resource utilization and security awareness but also provides reliable and efficient execution of tasks in dynamic distributed clouds environments, which makes it a promising solution to next-generation IoT-cloud systems.