Gholamreza HEYDARI, Mohsen NICKRAY, Dadmehr RAHBARI

Energy saving scheduling in a fog-based IoT application by Bayesian task classification approach

The Internet of things increases information volume in computer networks and the concept of fog will helpus to control this volume more efficiently. Scheduling resources in such an environment would be an NP-Hard problem.This article has studied the concept of scheduling in fog with Bayesian classification which could be applied to gain thetask requirements like the processing ones. After classification, virtual machines will be created in accordance with thepredicted requirements. The ifogsim simulator has been applied to study our fog-based Bayesian classification scheduling(FBCS) method performance in an EEG tractor application. Algorithms have been evaluated on a practical applicationof brain signal tracking system. According to the results, the FBCS method, compared with other methods, has reducedthe energy consumption in the cloud and the executing task cost in cloud; and also the average of energy consuming inmobiles has been decreased by smart decision making.

PDF

___

[1] Singh AB, Bhat JS, Raju R, D’Souza R. A comparative study of various scheduling algorithms in cloud computing. American Journal of Intelligent Systems 2017; 7 (3): 68-72. doi: 10.5923/j.ajis.20170703.06
[2] Yousefpour A, Fung C, Nguyen T, Kadiyala K, Jalali F et al. All one needs to know about fog computing and related edge computing paradigms: a complete survey. Journal of Systems Architecture 2019; doi: 10.1016/j.sysarc.2019.02.009
[3] Mutlag AA, Abd Ghani MK, Arunkumar NA, Mohamed MA, Mohd O. Enabling technologies for fog computing in healthcare IoT systems. Future Generation Computer Systems 2019; 62-78. doi: 10.1016/j.future.2018.07.049
[4] Bogale TE, Wang X, Le LB. Machine intelligence techniques for next-generation context-aware wireless networks. CoRR 2018; abs/1801.04223.
[5] Peralta G, Iglesias-Urika M, Barcelo M, Gomez R, Moran A et al. Fog computing based efficient IoT scheme for the Industry 4.0. In: International Workshop of Electronics, Control, Measurement, Signals and their Application to Mechatronics Conference (ECMSM); Donostia-San Sebastian, Spain; 2017. pp. 1-6.
[6] Etemad M, Aazam M, St-Hilaire M. Using DEVS for modeling and simulating a fog computing environment. In: 2017 International Conference on Computing, Networking and Communications Conference (ICNC); Santa Clara, CA, USA; 2017. pp. 849-854.
[7] Stojmenovic I, Wen S. The fog computing paradigm: scenarios and security issues. In: 2014 Federated Conference on Computer Science and Information Systems; Warsaw, Poland; 2014. pp. 1-8.
[8] Rabiul Alam MdG, Tun YK, Hong CS. Multi-agent and reinforcement learning based code offloading in mobile fog. In: 2016 International Conference on Information Networking (ICOIN); Kota Kinabalu, Malaysia; 2016. pp. 285-290.
[9] Yi S, Li C, Li Q. A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 Workshop on Mobile Big Data Conference; Hangzhou, China; 2015. pp. 37-42.
[10] Hussain M, Beg MM. Fog computing for Internet of ffigs (IoT)-aided smart grid architectures. Big Data and Cognitive Computing 2019; 3(1): 8 doi:10.3390/bdcc3010008
[11] Tang Z, Zhou X, Zhang F, Jia W, Zhao W. Migration modeling and learning algorithms for containers in fog computing. IEEE Transactions on Services Computing 2018; 14 (8): 1-14. doi: 10.1109/TSC.2018.2827070
[12] Zhang P, Zhou M. Dynamic cloud task scheduling based on a two-stage strategy. IEEE Transactions on Automation Science and Engineering 2018; 15 (2): 772-783. doi: 10.1109/TASE.2017.2693688
[13] Bittencourt LF, Diaz-Montes J, Buyya R, Rana OF, Parashar M. Mobility-aware application scheduling in fog computing. IEEE Cloud Computing 2017; 4(2): 26-35. doi: 10.1109/MCC.2017.27
[14] Jalali F, Vishwanath A, De Hoog J, Suits F. Interconnecting Fog computing and microgrids for greening IoT. In: 2016 IEEE Innovative Smart Grid Technologies-Asia (ISGT-Asia); Melbourne, VIC, Australia; 2016. pp. 693-698.
[15] Mathew T, Sekaran KS, Jose J. Study and analysis of various task scheduling algorithms in the cloud computing environment. In: 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI); New Delhi, India; 2014. pp. 658-664.
[16] Anusha Bamini BE, Sharmini EME. Optimized resource scheduling using classification and regression tree and modified bacterial foraging optimization algorithm. International Journal of Applied Engineering Research 2015; 10 (16): 37170-37175. doi: 10.1002/cpe.3887
[17] Fang W, Zhou W, Li Y, Yao X, Xue F et al. A distributed admm approach for energy-efficient resource allocation in mobile edge computing. Turkish Journal of Electrical Engineering and Computer Sciences 2018; 26 (6): 3335-3344. doi: 10.3906/elk-1806-112
[18] Borthakur D, Dubey H, Constant N, Mahler L, Mankodiya K. Smart fog: fog computing framework for unsupervised clustering analytics in wearable internet of things. In: 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP); Montreal, QC, Canada; 2017. pp. 472-476.
[19] Yang R, Ouyang X, Chen Y, Townend P, Xu J. Intelligent resource scheduling at scale: a machine learning perspective. In: 2018 IEEE Symposium on Service-Oriented System Engineering (SOSE); Bamberg, Germany; 2018. pp. 132-141.
[20] Hormozi E, Hormozi H, Akbari MK, Sargolzai JM. Using of machine learning into cloud environment (a survey): managing and scheduling of resources in cloud systems. In: 2012 Seventh International Conference on P2P, Parallel, Grid, Cloud and Internet Computing; Victoria, BC, Canada; 2012. pp. 363-368.
[21] Cui D, Peng Z, Xiong J, Xu B, Lin W. A reinforcement learning-based mixed job scheduler scheme for grid or iaaS cloud. IEEE Transactions on Cloud Computing 2017; doi: 10.1109/TCC.2017.2773078
[22] Ezugwu AE, Frincu ME, Adewumi AO, Buhari SM, Juniadu SB. Neural network‐based multi‐agent approach for scheduling in distributed systems. Concurrency and Computation: Practice and Experience 2017; 29 (1): e3887. doi: 10.1002/cpe.3887
[23] Lavassani M, Forsström S, Jennehag U, Zhang T. Combining fog computing with sensor mote machine learning for industrial iot. Sensors 2018; 18 (5): 1532. doi: 10.3390/s18051532
[24] Zhang Q, Lin M, Yang LT, Chen Z, Li P. Energy-efficient scheduling for real-time systems based on deep q-learning model. IEEE Transactions on Sustainable Computing 2017; 4 (1): 132-141. doi: 10.1109/TSUSC.2017.2743704
[25] Zhao X, Zhao L, Liang K. An energy consumption oriented offloading algorithm for fog computing. In: International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness; Greader Noida, India; 2016. pp. 293-301.
[26] Aazam M, Zeadally S, Harras KA. Offloading in fog computing for IoT: Review, enabling technologies, and research opportunities. Future Generation Computer Systems 2018; 87. 278-289. doi: 10.1016/j.future.2018.04.057
[27] Zhu J, Song Y, Jiang D, Song H. A new deep-Q-learning-based transmission scheduling mechanism for the cognitive internet of things. IEEE Internet of Things Journal 2018; 5 (4): 2375-2385. doi: 10.1109/JIOT.2017.2759728
[28] Rahmani AM, Liljeberg P, Preden J-S, Jantsch A. Fog Computing in the Internet of Things: Intelligence at the Edge. Switzerland AG: Springer, 2018.
[29] Wang Y, Wang K, Huang H, Miyazaki T, Guo S. Traffic and computation co-offloading with reinforcement learning in fog computing for industrial applications. IEEE Transactions on Industrial Informatics 2019; 15 (2): 976-986. doi: 10.1109/TII.2018.2883991
[30] Lu J, Li L, Chen G, Shen D, Pham K et al. Machine learning based intelligent cognitive network using fog computing. In: Sensors and Systems for Space Applications X Conference; Anaheim, California, United States; 2017. pp. 101960G.
[31] Gupta H, Dastjerdi AV, Ghosh SK, Rajkumar B. 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 2017; 47 (9): 1275-1296. doi: 10.1002/spe.2509
[32] Osisanwo FY, Akinsola JET, Awodele O, Hinmikaiye JO, Olakanmi O et al. Supervised machine learning algorithms: classification and comparison. International Journal of Computer Trends and Technology (IJCTT) 2017; 48 (3): 128-138. doi: 10.14445/22312803/ijctt-v48p126
[33] Michael A, Armando F, Rean G, Anthony DJ, Randy KA et al. A view of cloud computing. Communications of the ACM 2010; 53(4): 50-58. doi: 10.1145/1721654.1721672