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Title	An Offloading Strategy Based On Cloud And Edge Computing For Industrial Internet
ID_Doc	8787
Authors	Yang L.; Dai Z.; Li K.
Year	2019
Published	Proceedings - 21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019
DOI	http://dx.doi.org/10.1109/HPCC/SmartCity/DSS.2019.00228
Abstract	With the proliferation in the number of devices involved in Industrial Internet, it is becoming more and more difficult to meet simultaneously the requirements of industrial applications in terms of latency and economy only using traditional cloud computing paradigm. Edge computing paradigm can make up for this problem to a certain extent with constrained resources. In this paper, an integrated architecture is proposed, which can allow industrial devices to offload tasks to cloud or edge servers. Subsequently, an offloading strategy called ASO is also proposed based on this integrated architecture. The strategy, aiming at minimizing the energy consumption generated by industrial devices and cloud computing costs under the deadline constraints of industrial applications, consists of three sub-algorithms. First, a subdeadline is assigned to each task using a directed acyclic graph of the industrial application through subdeadline allocation algorithm. Then, tasks are topologically sorted to ensure the order of execution through topology sorting algorithm. Finally, a computing server is assigned to each task through task offloading algorithm. Experimental results demonstrate that compared with several baseline and heuristic algorithms, the proposed strategy can effectively reduce the energy consumption of industrial devices and cloud computing costs. © 2019 IEEE.
Author Keywords	cloud computing; edge computing; industrial internet; multi-objective optimization; task offloading

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