Journal of Soft Computing and Information Technology

Journal of Soft Computing and Information Technology

Delay-Aware and Energy-Efficient Task Offloading in Fog-Enabled IoT Networks

Document Type : Persian Original Article

Authors
Om-kolsoom Shahryari 1
Hosein Pedram 2
Vahid Khajehvand 3
Mehdi Dehghan TakhtFooladi 2
1 - Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
2 Computer Engineering and Information Technology Department, Amirkabir University of Technology, Tehran, Iran
3 Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Abstract
Fog computing has emerged as a promising technique to provide agile and pervasive computing services to the Internet of Things devices (IDs) and to support complicated IoT applications. Fog computing brings computation resources to the edge of the network, near to the IDs, and provides low-latency services to users. By offloading computational tasks to fog nodes having greater computing capacities, can address the contradiction between the limited battery capacity of IDs and high computational intensity demand of tasks. Hence, the quality of service (QoS) demands of users can be fulfilled. Although task offloading to fog nodes leads to saving in energy consumption in the battery of IDs, it causes to increase in task completion time due to occurred delay in transmitting the task to the edge of the network. In this paper, to balancing the trade-off between energy consumption and task completion time, a task offloading scheme is proposed. The main objective of the proposed scheme is to minimize offloading overhead in terms of the weighted sum of energy consumption and task completion time by optimizing offloading decision, the destination of offloading, and computation resource allocation. We employ fuzzy logic to determine the weighting coefficient effectively. Task offloading to fog nodes is formulated as a mixed-integer nonlinear programming (MINLP) problem, which is NP-hard. A sub-optimal algorithm based on genetic algorithm (GA) is proposed to solve the formulated problem. Extensive simulations prove the convergence of the proposed algorithm and its superior performance in comparison with some baseline schemes.
Keywords
Task offloading
Internet of Things
Fog computing
Resource allocation
Genetic algorithm
[1] M. Chiang and T. Zhang, “Fog and IoT: An Overview of Research Opportunities,” IEEE Internet of Things Journal, vol. 4662, no. c, pp. 1–1, 2016.
[2] C. Mouradian, D. Naboulsi, S. Yangui, R. H. Glitho, M. J. Morrow, and P. A. Polakos, “A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges,” IEEE Communications Surveys and Tutorials, vol. 20, no. 1, pp. 416–464, 2018.
[3] P. Mach and Z. Becvar, “Mobile Edge Computing: A Survey on Architecture and Computation Offloading,” IEEE Communications Surveys & Tutorials, vol. 19, no. 3, pp. 1628–1656, 2017.
[4] M. Mukherjee, L. Shu, and D. Wang, “Survey of fog computing: Fundamental, network applications, and research challenges,” IEEE Communications Surveys and Tutorials, vol. 20, no. 3, pp. 1826–1857, 2018.
[5] F. Bonomi, R. Milito, J. Zhu, and S. Addepalli, “Fog computing and its role in the internet of things,” in Proceedings of the first edition of the MCC workshop on Mobile cloud computing - MCC ’12, 2012, p. 13.
[6] H. Shah-Mansouri and V. W. S. Wong, “Hierarchical Fog-Cloud Computing for IoT Systems: A Computation Offloading Game,” IEEE Internet of Things Journal, vol. 5, no. 4, pp. 3246–3257, Aug. 2018.
[7] A. Yousefpour, G. Ishigaki, R. Gour, and J. P. Jue, “On Reducing IoT Service Delay via Fog Offloading,” IEEE Internet of Things Journal, vol. 5, no. 2, pp. 998–1010, Apr. 2018.
[8] S. Josilo and G. Dan, “Decentralized Algorithm for Randomized Task Allocation in Fog Computing Systems,” IEEE/ACM Transactions on Networking, vol. 27, no. 1, pp. 85–97, Feb. 2019.
[9] W. Yu et al., “A Survey on the Edge Computing for the Internet of Things,” IEEE Access, vol. 6, pp. 6900–6919, 2018.
[10] T. X. Tran and D. Pompili, “Joint Task Offloading and Resource Allocation for Multi-Server Mobile-Edge Computing Networks,” IEEE Transactions on Vehicular Technology, vol. 68, no. 1, pp. 856–868, Jan. 2019.
[11] H. Guo, J. Zhang, J. Liu, and H. Zhang, “Energy-Aware Computation Offloading and Transmit Power Allocation in Ultradense IoT Networks,” IEEE Internet of Things Journal, vol. 6, no. 3, pp. 4317–4329, Jun. 2019.
[12] S. Misra and N. Saha, “Detour: Dynamic Task Offloading in Software-Defined Fog for IoT Applications,” IEEE Journal on Selected Areas in Communications, vol. 37, no. 5, pp. 1159–1166, May 2019.
[13] Z. Wu, B. Li, Z. Fei, Z. Zheng, B. Li, and Z. Han, “Energy-Efficient Robust Computation Offloading for Fog-IoT Systems,” IEEE Transactions on Vehicular Technology, vol. 69, no. 4, pp. 4417–4425, Apr. 2020.
[14] Z. Zhou, H. Liao, B. Gu, S. Mumtaz, and J. Rodriguez, “Resource Sharing and Task Offloading in IoT Fog Computing: A Contract-Learning Approach,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 4, no. 3, pp. 227–240, Jun. 2020.
[15] Z. Zhao et al., “On the Design of Computation Offloading in Fog Radio Access Networks,” IEEE Transactions on Vehicular Technology, vol. 68, no. 7, pp. 7136–7149, Jul. 2019.
[16] F. Chiti, R. Fantacci, and B. Picano, “A Matching Theory Framework for Tasks Offloading in Fog Computing for IoT Systems,” IEEE Internet of Things Journal, vol. 5, no. 6, pp. 5089–5096, Dec. 2018.
[17] J. Du, L. Zhao, J. Feng, and X. Chu, “Computation Offloading and Resource Allocation in Mixed Fog/Cloud Computing Systems With Min-Max Fairness Guarantee,” IEEE Transactions on Communications, vol. 66, no. 4, pp. 1594–1608, Apr. 2018.
[18] J. Yao and N. Ansari, “QoS-Aware Fog Resource Provisioning and Mobile Device Power Control in IoT Networks,” IEEE Transactions on Network and Service Management, vol. 16, no. 1, pp. 167–175, Mar. 2019.
[19] L. Liu, Z. Chang, X. Guo, S. Mao, and T. Ristaniemi, “Multiobjective Optimization for Computation Offloading in Fog Computing,” IEEE Internet of Things Journal, vol. 5, no. 1, pp. 283–294, Feb. 2018.
[20] X. Gao, X. Huang, S. Bian, Z. Shao, and Y. Yang, “PORA: Predictive Offloading and Resource Allocation in Dynamic Fog Computing Systems,” IEEE Internet of Things Journal, vol. 7, no. 1, pp. 72–87, Jan. 2020.
[21] F. Guo, H. Zhang, H. Ji, X. Li, and V. C. M. Leung, “An Efficient Computation Offloading Management Scheme in the Densely Deployed Small Cell Networks With Mobile Edge Computing,” IEEE/ACM Transactions on Networking, vol. 26, no. 6, pp. 2651–2664, Dec. 2018.
[22] S. K. Mishra, D. Puthal, J. J. P. C. Rodrigues, B. Sahoo, and E. Dutkiewicz, “Sustainable Service Allocation Using a Metaheuristic Technique in a Fog Server for Industrial Applications,” IEEE Transactions on Industrial Informatics, vol. 14, no. 10, pp. 4497–4506, Oct. 2018.
[23] M. K. Hussein and M. H. Mousa, “Efficient Task Offloading for IoT-Based Applications in Fog Computing Using Ant Colony Optimization,” IEEE Access, vol. 8, pp. 37191–37201, 2020.
[24] J. Wang et al., “A Probability Preferred Priori Offloading Mechanism in Mobile Edge Computing,” IEEE Access, vol. 8, pp. 39758–39767, 2020.
[25] S. Rashidi and S. Sharifian, “A hybrid heuristic queue based algorithm for task assignment in mobile cloud,” Future Generation Computer Systems, vol. 68, pp. 331–345, Mar. 2017.
[26] B. Huang et al., “Security modeling and efficient computation offloading for service workflow in mobile edge computing,” Future Generation Computer Systems, vol. 97, pp. 755–774, Aug. 2019.
[27] T. Wang, X. Wei, C. Tang, and J. Fan, “Efficient multi-tasks scheduling algorithm in mobile cloud computing with time constraints,” Peer-to-Peer Networking and Applications, vol. 11, no. 4, pp. 793–807, Jul. 2018.
[28] Y. Wang, M. Sheng, X. Wang, L. Wang, and J. Li, “Mobile-Edge Computing: Partial Computation Offloading Using Dynamic Voltage Scaling,” IEEE Transactions on Communications, pp. 1–1, 2016.
[29] Y. Dai, D. Xu, S. Maharjan, and Y. Zhang, “Joint Computation Offloading and User Association in Multi-Task Mobile Edge Computing,” IEEE Transactions on Vehicular Technology, vol. 67, no. 12, pp. 12313–12325, Dec. 2018.
[30] Y. Kim, H.-W. Lee, and S. Chong, “Mobile Computation Offloading for Application Throughput Fairness and Energy Efficiency,” IEEE Transactions on Wireless Communications, vol. 18, no. 1, pp. 3–19, Jan. 2019.
[31] J. Zhang et al., “Energy-Latency Tradeoff for Energy-Aware Offloading in Mobile Edge Computing Networks,” IEEE Internet of Things Journal, vol. 5, no. 4, pp. 2633–2645, Aug. 2018.
[32] S. Han et al., “Energy Efficient Secure Computation Offloading in NOMA-Based mMTC Networks for IoT,” IEEE Internet of Things Journal, vol. 6, no. 3, pp. 5674–5690, Jun. 2019.
[33] K.-L. Du and M. N. S. Swamy, Search and Optimization by Metaheuristics. Cham: Springer International Publishing, 2016.
Journal of Soft Computing and Information Technology
Volume 12, Issue 2 - Serial Number 40
Summer
Summer 2023
Pages 32-47