A Novel Digital Twin Enabled Weighted Task Optimization Framework for Software Defined Data Centers in Industry 4.0

Today, the number of latency-sensitive tasks in Industry 4.0 is growing rapidly. With the emergence of smart factories, optimal task scheduling plays an important role in software-defined data centers (SDDC). There are many studies in the literature to solve this NP-hard problem, which do not consider the collective of data from the physical layer and optimal task scheduling in cloud servers. In addition, the intelligent transformation of the manufacturing industry by mapping the algorithm decision with real-world requirements has a major synchronization problem. In terms of CPU and memory utilization, the proposed solution should be easy to implement in real-life scenarios to improve the scalability of the server. The response time of task scheduling is also very important for latency-sensitive tasks in Industry 4.0. Therefore, we've proposed a novel Digital Twin (DT)-based SDDC platform that provides weighted task optimization. Thanks to virtual replicas of industrial objects in the physical layer, it optimizes task scheduling by monitoring and integrating virtualization and synchronization with the digital twin layer. A novel algorithm based on particle swarm optimization is proposed to maximize the profit of clustered tasks in different virtual machines of DT-based SDDC. The performance evaluation is compared with conventional dynamic programming and greedy approaches in terms of memory utilization, CPU utilization, response time, and profit maximization as the number of tasks increases. Our results show that the proposed approach achieves acceptable response time with higher profits.