Optimization of Task Allocation in Edge Computing to Industrial Internet with Simulated Annealing
Resumo
Com o crescimento do número de aplicações no contexto da Internet Industrial (IIoT), surgem novos requisitos para atender as tarefas críticas com relação ao tempo de resposta. A Computação na Borda oferece uma alternativa em relação ao processamento dos dados na nuvem computacional, pois emprega recursos na borda da rede local, possibilitando o processamento das tarefas dos dispositivos industriais com baixa latência. Neste trabalho, propõem-se uma Abordagem para a Alocação de Tarefas de veículos industriais, utilizandose de nós de borda (edge). O edge recebe simultaneamente múltiplas tarefas de diferentes veículos. Como consequência, adveio a demanda por definir a melhor ordem de processamento das tarefas que atenda aos requisitos de prazo impostos pelas aplicações. A solução foi modelada com base na meta-heurística Simulated Annealing para encontrar a melhor ordem para o atendimento dentro do tempo limite. Os resultados obtidos no simulador iFogSim, demonstram que a Abordagem para a Alocação de Tarefas pôde selecionar a melhor combinação de processamento que obedeça ao tempo de atendimento requerido.
Referências
de Figueiredo Marques, V. and Kniess, J. (2019). Mobility aware rpl (marpl): Mobility to rpl on neighbor variability. In Miani, R., Camargos, L., Zarpelão, B., Rosas, E., and Pasquini, R., editors, Green, Pervasive, and Cloud Computing, pages 59–73, Cham. Springer International Publishing.
Gupta, H., Vahid Dastjerdi, A., Ghosh, S. K., and Buyya, R. (2017). ifogsim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Software: Practice and Experience.
He, J. (2022). Optimization of edge delay sensitive task scheduling based on genetic algorithm. In 2022 International Conference on Algorithms, Data Mining, and Information Technology (ADMIT), pages 155–159.
Kennedy, J. and Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN’95 International Conference on Neural Networks, pages 1942–1948 vol.4.
Kiran, N., Pan, C., Wang, S., and Yin, C. (2020). Joint resource allocation and computation offloading in mobile edge computing for sdn based wireless networks. Journal of Communications and Networks, 22(1):1–11.
Kirkpatrick, S., Gelatt, C. D., and Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598):671–680.
Matrouk, K. e. (2023). Mobility aware-task scheduling and virtual fog for offloading in iot-fog-cloud environment. Wireless Personal Communications, 130:801–836.
Qiu, T., Chi, J., Zhou, X., Ning, Z., Atiquzzaman, M., and Wu, D. O. (2020). Edge computing in industrial internet of things: Architecture, advances and challenges. IEEE Communications Surveys and Tutorials, 22(4):2462–2488.
Sisinni, E., Saifullah, A., Han, S., Jennehag, U., and Gidlund, M. (2018). Industrial internet of things: Challenges, opportunities, and directions. IEEE Transactions on Industrial Informatics, 14(11):4724–4734.
Sonmez, C., Ozgovde, A., and Ersoy, C. (2018). Edgecloudsim: An environment for performance evaluation of edge computing systems. Transactions on Emerging Telecommunications Technologies, 29(11):e3493. e3493 ett.3493.
Wei, F., Chen, S., and Zou, W. (2018). A greedy algorithm for task offloading in mobile edge computing system. China Communications, 15(11):149–157.
Xue, Y., Wu, X., and Yue, J. (2020). An offloading algorithm of dense-tasks for mobile edge computing. icWCSN 2020, page 35–40, New York, NY, USA. Association for Computing Machinery.
Yang, B., Pang, Z., Wang, S., Mo, F., and Gao, Y. (2022). A coupling optimization method of production scheduling and computation offloading for intelligent workshops with cloud-edge-terminal architecture. Journal of Manufacturing Systems, 65:421–438.
You, Q. and Tang, B. (2021). Efficient task offloading using particle swarm optimization algorithm in edge computing for industrial internet of things. Journal of Cloud Computing, 10:1–11.
Yuan, H., Hu, Q., Wang, M., Bi, J., and Zhou, M. (2022). Cost-minimized user association and partial offloading for dependent tasks in hybrid cloud–edge systems. In 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE).
