Document Type : Original/Review Paper


Department of Computer Engineering and Information Technology, Shahrood University of Technology, Shahrood, Iran.


Edge computing is an evolving approach for the growing computing and networking demands from end devices and smart things. Edge computing lets the computation to be offloaded from the cloud data centers to the network edge for lower latency, security, and privacy preservation. Although energy efficiency in cloud data centers has been widely studied, energy efficiency in edge computing has been left uninvestigated. In this paper, a new adaptive and decentralized approach is proposed for more energy efficiency in edge environments. In the proposed approach, edge servers collaborate with each other to achieve an efficient plan. The proposed approach is adaptive, and consider workload status in local, neighboring and global areas. The results of the conducted experiments show that the proposed approach can improve energy efficiency at network edges. e.g. by task completion rate of 100%, the proposed approach decreases energy consumption of edge servers from 1053 Kwh to 902 Kwh.


[1] M. Avgerinou, P. Bertoldi, and K. Castellazzi, "Trends in data centre energy consumption under the european code of conduct for data centre energy efficiency." Energies, vol. 10, no. 10, p. 1470, 2017.
[2] R. Ghaderi, M. Esnaashari, and M. R. Meybodi, "A Cellular Learning Automata-based Algorithm for Solving the Coverage and Connectivity Problem in Wireless Sensor Networks." Adhoc & Sensor Wireless Networks, vol. 22, 2014.
[3] M. Esnaashari and M. R. Meybodi. "Deployment of a mobile wireless sensor network with k-coverage constraint: a cellular learning automata approach." Wireless networks, Vol. 19, No. 5, pp. 945-968, 2013.
[4] M. K. Manshad, M. R. Meybodi, and A. Salajegheh. "A variable action set cellular learning automata-based algorithm for link prediction in online social networks." The Journal of Supercomputing, vol. 77, no. 7, pp. 7620-7648, 2021.
[5] M.D. Khomami, A. R. Rezvanian, and M. R. Meybodi. "A new cellular learning automata-based algorithm for community detection in complex social networks." Journal of computational science, vol. 24, pp. 413-426, 2018.
[6] M. Jahanshahi, M. Dehghan, and M. R. Meybodi. "On channel assignment and multicast routing in multi–channel multi–radio wireless mesh networks." International Journal of Ad Hoc and Ubiquitous Computing, vol. 12, no. 4, pp. 225-244, 2013.
[7] X. Zichuan, W. Liang, W. Xu, M. Jia, and S. Guo. "Capacitated cloudlet placements in wireless metropolitan area networks." in 2015 IEEE 40Th conference on local computer networks (LCN), 2015, pp. 570-578.
[8] F. Qiang and N. Ansari. "Cost aware cloudlet placement for big data processing at the edge." in 2017 IEEE International Conference on Communications (ICC), 2017, pp. 1-6.
[9] A.H. Safari-Bavil, S. Jabbehdari and M. Ghobaei-Arani, "An Efficient Approach to Solve Software-defined Networks based Virtual Machines Placement Problem using Moth-Flame Optimization in the Cloud Computing Environment." Journal of AI and Data Mining, vol. 9, no. 3, pp. 309-320, 2021.
[10] W. Yi and Y. Xia. "Energy optimal VM placement in the cloud." in 2016 IEEE 9th international conference on cloud computing (CLOUD), 2016, pp. 84-91.
[11] M. Sharma and R. Garg, “An artificial neural network based approach for energy efficient task scheduling in cloud data centers”, Sustainable Computing: Informatics and Systems. vol. 26, 2020.
[12] L. Yuanzhe and S. Wang. "An energy-aware edge server placement algorithm in mobile edge computing." in 2018 IEEE International Conference on Edge Computing (EDGE), 2018, pp. 66-73.
[13] M. Demirci, "A survey of machine learning applications for energy efficient resource management in cloud computing environments," in 14th International Conference on Machine Learning and Applications (ICMLA), 2015, pp. 1185-1190.
[14] L. Gu, J. Cai, D. Zeng, Y. Zhang, H. Jin, and W. Dai, "Energy efficient task allocation and energy scheduling in green energy powered edge computing." Future Generation Computer Systems, vol. 95, pp. 89-99. 2019.
[15] S. Nastic, T. Rausch, O. Scekic, S. Dustdar, M. Gusev, B. Koteska, M. Kostoska, B. Jakimovski, S. Ristov, and R. Prodan, "A serverless real-time data analytics platform for edge computing." IEEE Internet Computing, vol. 21, No. 4, pp. 64-71, 2017.
[16] C. Jiang, Y. Qiu, H. Gao, T. Fan, K. Li, and J. Wan, "An edge computing platform for intelligent operational monitoring in internet data centers." IEEE Access, vol. 7, 2019.
[17] C. Jiang, D. Ou,Y. Wang, Y. Li, J. Zhang, J. Wan, B. Luo, and W. Shi, "Energy efficiency comparison of hypervisors," Sustainable Computing: Informatics and Systems, vol. 22, pp. 311-321, 2019.
[18] J. Gao, "Machine learning applications for data center optimization," 2014.
[19] H. Momeni and N. Mabhoot, "An Energy-aware Real-time Task Scheduling Approach in a Cloud Computing Environment." Journal of AI and Data Mining, vol. 9, no. 2, pp. 213-226, 2021.
[20] M. Dabbagh, B. Hamdaoui, M. Guizani, and A. Rayes, “Energy efficient cloud resource management,” in IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 2014, pp. 386–391.
[21] I. AlQerm and B. Shihada, "Enhanced machine learning scheme for energy efficient resource allocation in 5G heterogeneous cloud radio access networks," in 28th Annual International IEEE Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), 2017, pp. 1-7.
[22] S. Jiang, S. R. Priya, N. Elango, J. Clay, and R. Sridhar, "An Energy Efficient In-Memory Computing Machine Learning Classifier Scheme," in 32nd International Conference on VLSI Design and 18th International Conference on Embedded Systems (VLSID), Delhi, NCR, India, 2019, pp. 157-162.
[23] R. Vafashoar, H. Morshedlou, A. Rezvanian, and M.R. Meybodi, Cellular Learning Automata: Theory and Applications, Vol. 307, Springer, 2021. [E-book] Available:
[24] A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei, “Large-scale video classification with convolutional neural networks." In IEEE conference on Computer Vision and Pattern Recognition, Columbus, OH, 2014, pp. 1725-1732.
[25] J. Yue-Hei, M. Hausknecht, S. Vijayanarasimhan, O. Vinyals, R. Monga, and G. Toderici, "Beyond short snippets: Deep networks for video classification." In IEEE conference on computer vision and pattern recognition, Boston, MA, 2015, pp. 4694-4702.
[26] H. Cao, M. Wachowicz, and S. Cha, "Developing an edge computing platform for real-time descriptive analytics." In IEEE International Conference on Big Data, 2017, pp. 4546-4554.
[27] M. Satyanarayanan, P. Simoens, Y. Xiao, P. Pillai, Z. Chen, K. Ha, W. Hu, and B. Amos, "Edge analytics in the internet of things." IEEE Pervasive Computing, vol. 14, No. 2, pp. 24-31, 2015.
[28] O. Skarlat, M. Nardelli, S. Schulte, and S. Dustdar, "Towards qos-aware fog service placement." in IEEE first international conference on Fog and Edge Computing (ICFEC)., 2017, pp. 89-96.
[29] M. I. Naas, P. R. Parvedy, J. Boukhobza, and L. Lemarchand, "iFogStor: an IoT data placement strategy for fog infrastructure." in IEEE 1st International Conference on Fog and Edge Computing (ICFEC)., 2017, pp. 97-104.
[30] C. Wu, D. Brooks, K. Chen, D. Chen, S. Choudhury, M. Dukhan, K. Hazelwood, E. Isaac, Y. Jia, and B. Jia, "Machine learning at facebook: Understanding inference at the edge," in 2019 IEEE International Symposium on High Performance Computer Architecture (HPCA), 2019, pp. 331-344.
[31] T. Chen, T. Moreau, Z. Jiang, L. Zheng, E. Yan, M. Cowan, H. Shen, L. Wang, Y. Hu, L. Ceze, C. Guestrin, and A. Krishnamurthy, "TVM: An automated end-to-end optimizing compiler for deep learning,"  [Online]. Available: [Accessed 2018].
[32] N. Rotem, J. Fix, S. Abdulrasool, S. Deng, J. H. Roman Dzhabarov, R. Levenstein, B. Maher, S. Nadathur, J. Olesen, J. Park, A. Rakhov, and M. Smelyanskiy, "Glow: Graph lowering compiler techniques for neural networks," [Online]. Available: [Accessed 2018].
[33] Google, "XLA is a compiler that optimizes TensorFlow computations." [Online]. Available:
[34] Apple Core ML, "Core ML: Integrate machine learning models into your app." [Online]. Available:
[35] NNPACK, "Acceleration package for neural networks on multi-core cpus." [Online]. Available:
[36] M. Dukhan, Y. Wu, and H. Lu, "QNNPACK: open source library for optimized mobile deep learning." [Online]. Available:
[37] N. Balasubramanian, A. Balasubramanian, and A. Venkataramani, “Energy consumption in mobile phones: A measurement study and implications for network applications,” in ACM SIGCOMM Conf. Internet Meas. Conf., 2009, pp. 280–293.
[38] A. Sharma, V. Navda, R. Ramjee, V. N. Padmanabhan, and E. M. Belding, “Cool-Tether: Energy efficient on-the-fly wifi hot-spots using mobile phones,” in ACM Emerging Netw. Exp. Technol., 2009, pp. 109–120.
[39] Z. Tang, S. Guo, P. Li, T. Miyazaki, H. Jin, and X. Liao, "Energy-Efficient Transmission Scheduling in Mobile Phones Using Machine Learning and Participatory Sensing," in IEEE Transactions on Vehicular Technology, Vol. 64, No. 7, pp. 3167-3176, July 2015.
[40] A. Kumar, S. Goyal, and M. Varma, "Resource-efficient machine learning in 2 KB RAM for the internet of things," in 34th International Conference on Machine Learning, vol. 70, 2017, pp. 1935-1944.
[41] X. Zhang, A. Ramachandran, C. Zhuge, D. He, W. Zuo, Z. Cheng, K. Rupnow, and D. Chen, "Machine learning on FPGAs to face the IoT revolution," in Proceedings of the 36th International Conference on Computer-Aided Design, 2017, pp. 819-826.
[42] G. Anastasi, M. Conti, M.D. Francesco, A. Passarella, "Energy conservation in wireless sensor networks: A survey", Ad Hoc Networks. Vol. 7, pp. 537–568, 2009.
[43] M. A. Razzaque, C. Bleakley, and S. Dobson, "Compression in wireless sensor networks: A survey and comparative evaluation," ACM Transactions on Sensor Networks (TOSN), Vol. 10, pp. 1-44, 2013.
[44] H. Li, K. Ota, and M. Dong, "Learning IoT in edge: Deep learning for the Internet of Things with edge computing," IEEE Network, Vol. 32, pp. 96-101, 2018.
[45] N.D. Lane, P. Georgiev, L. Qendro, "Deepear: Robust Smartphone Audio Sensing in Unconstrained Acoustic Environments Using Deep Learning", in Proc. 2015 ACM Int'l. Joint Conf. Pervasive and Ubiquitous Computing, 2015, pp. 283-94.
[46] H. Harb, A. Makhoul, and C. A. Jaoude, "En-route data filtering technique for maximizing wireless sensor network lifetime," in 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC), 2018, pp. 298-303.
[47] J. Azar, A. Makhoul, R. Darazi, J. Demerjian, and R. Couturier, "On the performance of resource-aware compression techniques for vital signs data in wireless body sensor networks," in 2018 IEEE Middle East and North Africa Communications Conference (MENACOMM), 2018, pp. 1-6.
[48] J. Azar, R. Darazi, C. Habib, A. Makhoul, and J. Demerjian, "Using DWT lifting scheme for lossless data compression in wireless body sensor networks," in 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC), 2018, pp. 1465-1470.
[49] J. Azar, A. Makhoul, M. Barhamgi, and R. Couturier, "An energy efficient IoT data compression approach for edge machine learning," Future Generation Computer Systems, Vol. 96, pp. 168- 175, 2019.
[50] Y. Wang, X. Dai, J. M. Wang and B. Bensaou, "A Reinforcement Learning Approach to Energy Efficiency and QoS in 5G Wireless Networks," IEEE Journal on Selected Areas in Communications, Vol. 37, No. 6, pp. 1413-1423, June 2019.
[51] Q. Zeng, Y. Du, KK. Leung, and K. Huang, "Energy-efficient radio resource allocation for federated edge learning," in 2020 IEEE International Conference on Communications Workshops (ICC Workshops), 2020, pp. 1-6.
[52] Y. Liu, C. He, X. Li, C. Zhang and C. Tian, "Power Allocation Schemes Based on Machine Learning for Distributed Antenna Systems," IEEE Access, Vol. 7, pp. 20577-20584, 2019.
[53] C. He, Y. Zhou, G. Qian, X. Li, and D. Feng, "Energy Efficient Power Allocation Based on Machine Learning Generated Clusters for Distributed Antenna Systems," IEEE Access, Vol. 7, pp. 575-584, 2019.
[54] K. Thangaramya, K. Kulothungan, R. Logambigai, M. Selvi, S. Ganapathy, and A. Kannan, "Energy aware cluster and neuro-fuzzy based routing algorithm for wireless sensor networks in IoT," Computer Networks, Vol. 151, pp. 211-223, 2019.