In this paper, we study big data-driven Cyber-Physical Systems (CPS) through cloud platforms and design scheduling optimization algorithms to improve the efficiency of the system. A task scheduling scheme for large-sc...
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In this paper, we study big data-driven Cyber-Physical Systems (CPS) through cloud platforms and design scheduling optimization algorithms to improve the efficiency of the system. A task scheduling scheme for large-scale factory access under cloud-edge collaborative computing architecture is proposed. The method firstly merges the directed acyclic graphs on cloud-side servers and edge-side servers;secondly, divide the tasks using a critical path-based partitioning strategy to effectively improve the allocation accuracy;then achieves load balancing through reasonable processor allocation, and finally compares and analyses the proposed task schedulingalgorithm through simulation experiments. The experimental system is thoroughly analysed, hierarchically designed, and modelled, simulated, and the experimental data analysed and compared with related methods. The experimental results prove the effectiveness and correctness of the worst-case execution time analysis method and the idea of big data-driven CPS proposed in this paper and show that big data knowledge can help improve the accuracy of worst-case execution time analysis. This paper implements a big data-driven scheduling optimization algorithm for Cyber-Physical Systems based on a cloud platform, which improves the accuracy and efficiency of the algorithm by about 15% compared to other related studies.
Intelligent manufacturing workshop uses automatic guided vehicles as an important logistics and transportation carrier, and most of the existing research adopts the intelligent manufacturing workshop layout and Automa...
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Intelligent manufacturing workshop uses automatic guided vehicles as an important logistics and transportation carrier, and most of the existing research adopts the intelligent manufacturing workshop layout and Automated Guided Vehicle (AGV) path step-by-step optimization, which leads to problems such as low AGV operation efficiency and inability to achieve the optimal layout. For this reason, a smart manufacturing assembly line layout optimization model considering AGV path planning with the objective of minimizing the amount of material flow and the shortest AGV path is designed for the machining shop of a discrete manufacturing enterprise of a smart manufacturing company. Firstly, the information of the current node, the next node and the target node is added to the heuristic information, and the dynamic adjustment factor is added to make the heuristic information guiding in the early stage and the pheromone guiding in the later stage of iteration;secondly, the Laplace distribution is introduced to regulate the volatilization of the pheromone in the pheromone updating of the ant colony algorithm, which speeds up the speed of convergence;the path obtained by the ant colony algorithm is subjected to the deletion of the bi-directional redundant nodes, which enhances the path smoothing degree;and finally, the improved ant colony algorithm is fused with the improved dynamic window algorithm, so as to enable the robots to arrive at the end point safely. Simulation shows that in the same map environment, the ant colony algorithm compared with the basic ant colony algorithm reduces the path length by 40% to 67% compared to the basic ant colony algorithm and reduces the path inflection points by 34% to 60%, which is more suitable for complex environments. It also verifies the feasibility and superiority of the conflict-free path optimization strategy in solving the production scheduling problem of the flexible machining operation shop.
This paper introduces the integration of two data processing platforms, RHhadoop and SparkR, to carry out rapid big data retrieval and analytics using R programming, which can serve as part of business intelligence. B...
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This paper introduces the integration of two data processing platforms, RHhadoop and SparkR, to carry out rapid big data retrieval and analytics using R programming, which can serve as part of business intelligence. Besides, it has developed the job schedulingoptimization called Memory-Sensitive Heterogeneous Earliest Finish Time algorithm to enhance system throughput. However, the bottleneck of system throughput is definitely relevant to data traffic problem over network, especially a large amount of data exchange between distributed computing nodes within a cluster. The objective of this paper is to propose an intelligence approach to tackle the crucial problems of inefficient data traffic flow. Adaptive network-based fuzzy inference system along with particle swarm optimization has employed to tune the network-related parameters at computing nodes for improving network QoS and speed up data transportation significantly. In order to examine the computing efficiency, performance index has been evaluated for all of treatments in the experiment. (C) 2017 Elsevier Ltd. All rights reserved.
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