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Heterogeneous Energy-aware Load Balancing for Industry 4.0 and IoT Environments

Usman Ahmed, Jerry Chun-Wei Lin, Gautam Srivastava
Western Norway University of Applied Sciences, Norway
ACM Transactions on Management Information Systems, 2022

@article{10.1145/3543859,

   author={Ahmed, Usman and Lin, Jerry Chun-Wei and Srivastava, Gautam},

   title={Heterogeneous Energy-Aware Load Balancing for Industry 4.0 and IoT Environments},

   year={2022},

   publisher={Association for Computing Machinery},

   address={New York, NY, USA},

   issn={2158-656X},

   url={https://doi.org/10.1145/3543859},

   doi={10.1145/3543859},

   note={Just Accepted},

   journal={ACM Trans. Manage. Inf. Syst.},

   month={jun},

   keywords={Scheduling, Classification, OpenCL, Feature selection, Optimization, Machine learning}

}

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With the improvement of global infrastructure, Cyber-Physical Systems (CPS) have become an important component of Industry 4.0. Both the application as well as the machine work together to improve the task of interdependencies. Machine learning methods in CPS require the monitoring of computational algorithms, including adopting optimizations, fine-tuning cyber systems, improving resource utilization, as well as reducing vulnerability as well as computation time. By leveraging the tremendous parallelism provided by General-Purpose Graphics Processing Units (GPGPU) as well as OpenCL, it is possible to dramatically reduce the execution time of data-parallel programs. However, when running an application with tiny amounts of data on a GPU, GPU resources are wasted because the program may not be able to fully utilize the GPU cores. This is because there is no mechanism for kernels to share a GPU due to the lack of OS support for GPUs. Optimal device selection is required to reduce the high power of the GPU. In this paper, we propose an energy reduction method for heterogeneous clustering. This study focuses on load balancing; resource-aware processor selection based on machine learning is performed using code features. The proposed method identifies energy-eicient kernel candidates (from the employment pool). Then, it selects a pair of kernel candidates from all possibilities that lead to a reduction in both energy consumption as well as execution time. Experimental results show that the proposed kernel approach reduces execution time by 2.23 times compared to a baseline scheduling system. Experiments have also shown that the execution time is 1.2 times faster than state-of-the-art approaches.
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