A cost efficient model for minimizing energy consumption and processing time for IoT tasks in a Mobile Edge Computing environment
Resumo
In a scenario with increasingly mobile devices connected to the Internet, data-intensive applications and energy consumption limited by battery capacity, we propose a cost minimization model for IoT devices in a Mobile Edge Computing (MEC) architecture with the main objective of reducing total energy consumption and total elapsed times from task creation to conclusion. The cost model is implemented using the TEMS (Time and Energy Minimization Scheduler) scheduling algorithm and validated with simulation. The results show that it is possible to reduce the energy consumed in the system by up to 51.61% and the total elapsed time by up to 86.65% in the simulated cases with the parameters and characteristics defined in each experiment.
Palavras-chave:
Mobile Edge Computing, Internet Of Things, Cost Minimization Model, Energy Consumption, Scheduling Algorithm
Referências
Aijaz, A. (2016). Towards 5g-enabled tactile internet: Radio resource allocation for haptic communications. In 2016 IEEE Wireless Communications and Networking Conference Workshops (WCNCW), pages 145–150.
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., and Ayyash, M. (2015). Internet of things: A survey on enabling technologies, protocols, and applications. IEEE Communications Surveys Tutorials, 17(4):2347–2376.
Brogi, A., Forti, S., and Ibrahim, A. (2018). Deploying fog applications: How much does it cost, by the way? In CLOSER.
Chen, Y.-L., Chang, M.-F., Yu, C.-W., Chen, X.-Z., and Liang, W.-Y. (2018). Learningdirected dynamic voltage and frequency scaling scheme with adjustable performance for single-core and multi-core embedded and mobile systems. Sensors, 18(9):3068.
Gedawy, H., Habak, K., Harras, K. A., and Hamdi, M. (2018). Awakening the cloud within: Energy-aware task scheduling on edge iot devices. In 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pages 191–196.
Gupta, A. and Jha, R. K. (2015). A survey of 5g network: Architecture and emerging technologies. IEEE Access, 3:1206–1232.
Haouari, F., Faraj, R., and AlJa’am, J. M. (2018). Fog computing potentials, applications, and challenges. In 2018 International Conference on Computer and Applications (ICCA), pages 399–406.
IDC (2019). The growth in connected iot devices is expected to generate 79.4zb of data in 2025. Available at: https://www.idc.com/getdoc.jsp?containerId= prUS45213219.
Jansson, J. (2005). Collision Avoidance Theory with Application to Automotive Collision Mitigation. PhD thesis.
Jin, X. and Goto, S. (2012). Hilbert transform-based workload prediction and dynamic frequency scaling for power-efficient video encoding. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 31(5):649–661.
Liu, Y., Yang, H., Dick, R. P., Wang, H., and Shang, L. (2007). Thermal vs energy optimization for dvfs-enabled processors in embedded systems. In 8th International Symposium on Quality Electronic Design (ISQED’07), pages 204–209.
Sarangi, S. R., Goel, S., and Singh, B. (2018). Energy efficient scheduling in iot networks. In Proceedings of the 33rd Annual ACM Symposium on Applied Computing, SAC ’18, page 733–740, New York, NY, USA. Association for Computing Machinery.
Satyanarayanan, M., Bahl, P., Caceres, R., and Davies, N. (2009). The case for vm-based cloudlets in mobile computing. IEEE Pervasive Computing, 8(4):14–23.
Tanenbaum, A. S. and Austin, T. (2012). Structured Computer Organization. Prentice Hall, 6th edition.
Wan, J., Chen, B., Wang, S., Xia, M., Li, D., and Liu, C. (2018). Fog computing for energy-aware load balancing and scheduling in smart factory. IEEE Transactions on Industrial Informatics, 14(10):4548–4556.
Wang, C., Dong, C., Qin, J., Yang, X., and Wen, W. (2018). Energy-efficient offloading policy for resource allocation in distributed mobile edge computing. In 2018 IEEE Symposium on Computers and Communications (ISCC), pages 00366–00372.
Wu, H. and Lee, C. (2018). Energy efficient scheduling for heterogeneous fog computing architectures. In 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), volume 01, pages 555–560.
Yu, H., Wang, Q., and Guo, S. (2018). Energy-efficient task offloading and resource scheduling for mobile edge computing. In 2018 IEEE International Conference on Networking, Architecture and Storage (NAS), pages 1–4.
Yu, Y. (2016). Mobile edge computing towards 5g: Vision, recent progress, and open challenges. China Communications, 13(Supplement2):89–99.
Zhang, G., Zhang, W., Cao, Y., Li, D., and Wang, L. (2018). Energy-delay tradeoff for dynamic offloading in mobile-edge computing system with energy harvesting devices. IEEE Transactions on Industrial Informatics, 14(10):4642–4655.
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., and Ayyash, M. (2015). Internet of things: A survey on enabling technologies, protocols, and applications. IEEE Communications Surveys Tutorials, 17(4):2347–2376.
Brogi, A., Forti, S., and Ibrahim, A. (2018). Deploying fog applications: How much does it cost, by the way? In CLOSER.
Chen, Y.-L., Chang, M.-F., Yu, C.-W., Chen, X.-Z., and Liang, W.-Y. (2018). Learningdirected dynamic voltage and frequency scaling scheme with adjustable performance for single-core and multi-core embedded and mobile systems. Sensors, 18(9):3068.
Gedawy, H., Habak, K., Harras, K. A., and Hamdi, M. (2018). Awakening the cloud within: Energy-aware task scheduling on edge iot devices. In 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pages 191–196.
Gupta, A. and Jha, R. K. (2015). A survey of 5g network: Architecture and emerging technologies. IEEE Access, 3:1206–1232.
Haouari, F., Faraj, R., and AlJa’am, J. M. (2018). Fog computing potentials, applications, and challenges. In 2018 International Conference on Computer and Applications (ICCA), pages 399–406.
IDC (2019). The growth in connected iot devices is expected to generate 79.4zb of data in 2025. Available at: https://www.idc.com/getdoc.jsp?containerId= prUS45213219.
Jansson, J. (2005). Collision Avoidance Theory with Application to Automotive Collision Mitigation. PhD thesis.
Jin, X. and Goto, S. (2012). Hilbert transform-based workload prediction and dynamic frequency scaling for power-efficient video encoding. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 31(5):649–661.
Liu, Y., Yang, H., Dick, R. P., Wang, H., and Shang, L. (2007). Thermal vs energy optimization for dvfs-enabled processors in embedded systems. In 8th International Symposium on Quality Electronic Design (ISQED’07), pages 204–209.
Sarangi, S. R., Goel, S., and Singh, B. (2018). Energy efficient scheduling in iot networks. In Proceedings of the 33rd Annual ACM Symposium on Applied Computing, SAC ’18, page 733–740, New York, NY, USA. Association for Computing Machinery.
Satyanarayanan, M., Bahl, P., Caceres, R., and Davies, N. (2009). The case for vm-based cloudlets in mobile computing. IEEE Pervasive Computing, 8(4):14–23.
Tanenbaum, A. S. and Austin, T. (2012). Structured Computer Organization. Prentice Hall, 6th edition.
Wan, J., Chen, B., Wang, S., Xia, M., Li, D., and Liu, C. (2018). Fog computing for energy-aware load balancing and scheduling in smart factory. IEEE Transactions on Industrial Informatics, 14(10):4548–4556.
Wang, C., Dong, C., Qin, J., Yang, X., and Wen, W. (2018). Energy-efficient offloading policy for resource allocation in distributed mobile edge computing. In 2018 IEEE Symposium on Computers and Communications (ISCC), pages 00366–00372.
Wu, H. and Lee, C. (2018). Energy efficient scheduling for heterogeneous fog computing architectures. In 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), volume 01, pages 555–560.
Yu, H., Wang, Q., and Guo, S. (2018). Energy-efficient task offloading and resource scheduling for mobile edge computing. In 2018 IEEE International Conference on Networking, Architecture and Storage (NAS), pages 1–4.
Yu, Y. (2016). Mobile edge computing towards 5g: Vision, recent progress, and open challenges. China Communications, 13(Supplement2):89–99.
Zhang, G., Zhang, W., Cao, Y., Li, D., and Wang, L. (2018). Energy-delay tradeoff for dynamic offloading in mobile-edge computing system with energy harvesting devices. IEEE Transactions on Industrial Informatics, 14(10):4642–4655.
Publicado
30/06/2020
Como Citar
GROSS, João Luiz Grave; GEYER, Cláudio Fernando Fernando Resin.
A cost efficient model for minimizing energy consumption and processing time for IoT tasks in a Mobile Edge Computing environment. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 12. , 2020, Cuiabá.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 41-50.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2020.11210.
