The cyber-physical system is highly desirable for real-time perception and dynamic control, especially in task scheduling and network communication for large and complex engineering systems. In most of the applications, the resource scheduling of cyber and physical network are independent on each other, which may cause severe deterioration of the network performance. To implement the cognitive cooperation between the upper layer of industrial services and the underlying network, we propose a cooptimization method in this article. We adopt the software defined networking technology to extract the network control logic, which is integrated with the upper-level task scheduling. This cooptimization scheme can dynamically calculate optimal end-to-end virtual paths over the underlying network infrastructures. Meanwhile, it can also adjust the task scheduling of the upper-level network by signals derived from the underlying network. Simulation results show that our proposed scheme outperforms the traditional method in terms of task acceptance rate, average end-to-end delay, and network load balance degree.

Ant colony optimization (ACO) is a population-based metaheuristic for solving hard combinatorial optimization problems. Many studies are dedicated to accelerating ACO by parallel hardware, especially by graphics processing units (GPUs). However, due to the irregular (random) pattern of ACO algorithms in data access and control flow, the performance of GPU-based approaches is constrained by hardware limitations. CPU-based SIMD computing for ACOs is rarely investigated in previous literatures, and how well multicore-SIMD CPU-based parallel ACOs could perform remains unknown. In this paper, we present and evaluate a model of vector parallel ACO for multi-core SIMD CPU architecture. In the proposed model, each ant is mapped with a CPU core and the tour construction of each ant is accelerated by vector instructions. Furthermore, based on the model, we propose a new fitness proportionate selection approach named Vector-based Roulette Wheel (VRW) in the tour construction stage. In this approach, the fitness values are grouped into SIMD lanes and the prefix sum is computed in vector-parallel mode. The proposed algorithm is tested on standard TSP instances ranging from 198 to 4461 cities and shows a speedup factor of 57.8x compared to the single-threaded CPU counterpart. More significantly, we compare our approach with high performance GPU-based ACOs, and the results demonstrate the strong potential of CPU-based parallel ACOs.

Hardware/software partitioning is an essential step in Hardware/Software Co-design. This paper presents a GPU-accelerated adaptive neighborhood taboo search algorithm(GPU-accelerated ANTS) to solve the problem. Firstly, a pre-GPU version of ANTS is presented to test our idea, in which both the number of feasible candidates in neighborhood and the length of taboo list can be adaptively adjusted. Secondly, a GPU-accelerated version combined with the two merits from pre-GPU version is presented. Furthermore, in order to fully leverage the power of GPU, the special consideration for the optimization strategies and implementation details on GPU are explored for our GPU- accelerated ANTS algorithm. Finally, enough number of experiments show that our GPU-accelerated ANTS method outperforms state-of-the-art work of taboo search for the HW/SW partitioning in both quality and speed under mid/low range GPU platform.