Abstract. Ant Colony Optimization(ACO) is an intelligence-optimized algorithm Which can solve a large-scale task scheduling problem in cloud computing. However, the basic ant colony algorithm used in large scale task scheduling will easily lead to low distribution efficiency. To solve this problem, this paper analyzes the features of ACO and propose an load balancing ant colony optimization which will boost the utility of ants in the system. After that it use CloudSim, A tools for simulate the proposed algorithm as well as to compare it with the basic ACO. This result suggests that the proposed ACO can reduce the distribution scheduling execution time effectively and ensure the even distribution between nodes so that it can use in large scale scheduling problems.
In industry 4.0, CPS will play a huge role in the industrial production and supply management [1-3]. CPS makes the industrial system more intelligent, making the subsequent salesbusinesses more data-oriented and intelligent. However, the industry environment produces large of data during the production process. How to deal with these huge data quickly and analyze meaningful value message becomes the core issue of CPS. In this paper, a real-time processing system of CPS for industrial management system is proposed. The architecture is based on the characteristics of industrial management system and designed for solving the big stream data processing.
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