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基于MO-SAC算法的工业软件组件边缘集群多目标决策部署
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Abstract:
随着工业4.0的发展，工业物联网(IIoT)中软件组件的分布式部署已成为提高生产效率和灵活性的关键手段，通过在边缘端进行数据处理和分析，能够实现更低的延迟和更高的可靠性。尽管分布式部署带来了诸多优势，但仍面临任务需求复杂，节点资源受限，响应时间要求严格、集群负载不均衡以及环境动态变化等挑战，限制了其在实际工业环境中的应用。为了在任务响应时间和受限的资源分配中寻找最优解，并保证负载的均衡与稳定，本文提出了一种工业软件组件应用程序分布式边缘部署的系统架构，并且利用改进的MO-SAC算法对工作流进行分布式部署，引入温度系数，鼓励模型探索更多更广的部署策略，避免陷入局部最优。大量仿真结果表明MO-SAC可以在有效降低任务响应时间的同时最小化资源消耗并且使集群的负载更加均衡。
With the development of Industry 4.0, the distributed deployment of software components in the Industrial Internet of Things (IIot) has become a key means to improve productivity and flexibility, enabling lower latency and higher reliability through data processing and analysis at the edge. Despite the many advantages of distributed deployment, it still faces challenges such as complex task requirements, limited node resources, strict response time requirements, unbalanced cluster loads, and dynamic changes in the environment, which limit its application in the actual industrial environment. In order to find the optimal solution in the task response time and limited resource allocation, and ensure the balance and stability of the load, this paper proposes a system architecture for distributed edge deployment of industrial software component applications, and uses the improved MO-SAC algorithm to distribute the workflow, introduces the temperature coefficient, encourages the model to explore more and wider deployment strategies, and avoids falling into the local optimum. A large number of simulation results show that MO-SAC can effectively reduce the task response time while minimizing resource consumption and making the load of the cluster more balanced.
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