Task Placement in Fog Computing Considering User Mobility and Overload

نوع مقاله : علمی-پژوهشی

نویسندگان

گروه فناوری اطلاعات، دانشکده مهندسی برق و کامپیوتر، دانشگاه سیستان و بلوچستان

چکیده

Efficient distribution of service requests between fog and cloud nodes considering user mobility and fog nodes’ overload is an important issue of fog computing. This paper proposes a heuristic method for task placement considering the mobility of users, aiming to serve a higher number of requested services and minimize their response time. This method introduces a formula to overload prediction based on the entry-exit ratio of users and the estimated time required to perform current requests that are waiting in the queue of a fog node. Then, it provides a solution to avoid the predicted overloading of fog nodes by sending all delay-tolerant requests in the overloaded fog node’s queue to the cloud to reduce the time required for servicing delay-sensitive requests and to increase their acceptance rate. In addition, to prevent requests from being rejected when the mobile user leaves the coverage area of the current fog node, the requests in the current fog node’s queue will be transferred to the destination fog node. Simulation results indicate that the proposed method is effective in avoiding the overloading of the fog nodes and outperforms the existing methods in terms of response time and acceptance rate.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Task Placement in Fog Computing Considering User Mobility and Overload

نویسندگان [English]

  • S. Ansari Moghaddam
  • S. Noferesti
  • M. Rajaei
Information Technology Department, Faculty of Electrical and Computer Engineering, University of Sistan and Baluchestan, Zahedan, Iran.
چکیده [English]

Efficient distribution of service requests between fog and cloud nodes considering user mobility and fog nodes’ overload is an important issue of fog computing. This paper proposes a heuristic method for task placement considering the mobility of users, aiming to serve a higher number of requested services and minimize their response time. This method introduces a formula to overload prediction based on the entry-exit ratio of users and the estimated time required to perform current requests that are waiting in the queue of a fog node. Then, it provides a solution to avoid the predicted overloading of fog nodes by sending all delay-tolerant requests in the overloaded fog node’s queue to the cloud to reduce the time required for servicing delay-sensitive requests and to increase their acceptance rate. In addition, to prevent requests from being rejected when the mobile user leaves the coverage area of the current fog node, the requests in the current fog node’s queue will be transferred to the destination fog node. Simulation results indicate that the proposed method is effective in avoiding the overloading of the fog nodes and outperforms the existing methods in terms of response time and acceptance rate.

کلیدواژه‌ها [English]

  • Fog computing
  • Task placement
  • User mobility
  • Overload prediction
[1] K. Ashton, “That internet of things thing”, RFID Journal, vol. 22, no. 7, pp. 97–114, 2009.
[2] M. Bahrami, M. Singhal, “The role of cloud computing architecture in big data”, Information granularity, big data, and computational intelligence, pp. 275–295, 2015.
[3] A. Kumari, S. Tanwar, S. Tyagi, N. Kumar, “Fog computing for healthcare 4.0 environment: Opportunities and challenges”, Computers & Electrical Engineering, vol. 72, pp. 1–13, 2018.
[4] M. Mukherjee, L. Shu, D. Wang, “Survey of fog computing: Fundamental, network applications, and research challenges”, IEEE Communications Surveys & Tutorials, vol. 20, no. 3, pp. 1826–1857, 2018.
[5] S. Mostafavi, F. Ahmadi, M. Agha Sarram, “Reinforcement-Learning-based Foresighted Task Scheduling in Cloud Computing”, Tabriz Journal of Electrical Engineering, vol. 50, no. 1, pp. 387-401, 2020 (in persian).
[6] S. Ghasemi-Falavarjani, M.A. Nematbakhsh, B. Shahgholi Ghahfarokhi, “Multi-Objective Task Allocation in Offloading to Mobile Cloud”, Tabriz Journal of Electrical Engineering, vol. 46, no. 4, pp. 217-232, 2017 (in persian).
[7] B. Nair, M.S.B. Somasundaram, “Overload prediction and avoidance for maintaining optimal working condition in a fog node”, Computers & Electrical Engineering, vol. 77, pp. 147–162, 2019.
[8] K. Gasmi, K. Dilek, S. Tosun, S. Ozdemir, “A survey on computation offloading and service placement in fog computing-based IoT”, The Journal of Supercomputing, vol. 78, no. 2, pp. 1983-2014, 2022.
[10] M.Q. Tran, D.T. Nguyen, V.A. Le, D.H. Nguyen, T.V. Pham, “Task placement on fog computing made efficient for IoT application provision”, Wireless Communications and Mobile Computing, pp. 1-17, 2019.
[11] Y. Xia, X. Etchevers, L. Letondeur, T. Coupaye, F. Desprez, “Combining hardware nodes and software components ordering-based heuristics for optimizing the placement of distributed IoT applications in the fog”, In Proceedings of the 33rd Annual ACM Symposium on Applied Computing, 2018, pp. 751-760.
[12] F. Khosroabadi, F. Fotouhi-Ghazvini, H Fotouhi, “Scatter: Service placement in real-time fog-assisted iot networks”, Journal of Sensor and Actuator Networks, vol. 10, no. 2, pp. 26, 2021.
[13] B.V. Natesha, R.M.R. Guddeti, “Meta-heuristic based hybrid service placement strategies for two-level fog computing architecture”, Journal of Network and Systems Management, vol. 30, no. 3, pp. 47, 2022.
[14] M. Goudarzi, H. Wu, M. Palaniswami, R. Buyya, “An application placement technique for concurrent IoT applications in edge and fog computing environments”, IEEE Transactions on Mobile Computing, vol. 20, no. 4, pp. 1298-1311, 2020.
[15] B. Kopras, B. Bossy, F. Idzikowski, P. Kryszkiewicz, H. Bogucka, “Task Allocation for Energy Optimization in Fog Computing Networks with Latency Constraints”, IEEE Transactions on Communications, vol. 70, no. 12, pp. 8229-8243, 2022.
[16] I. Sarkar, M. Adhikari, N. Kumar, S. Kumar, “Dynamic task placement for deadline-aware IoT applications in federated fog networks”, IEEE Internet of Things Journal, vol. 9, no. 2, pp. 1469-1478, 2021.
[17] A. Mseddi, W. Jaafar, H. Elbiaze, W. Ajib, “Joint container placement and task provisioning in dynamic fog computing”, IEEE Internet of Things Journal, vol. 6, no. 6, pp. 10028-10040, 2019.
[18] D. Wang, Z. Liu, X. Wang, Y. Lan, “Mobility-aware task offloading and migration schemes in fog computing networks”, IEEE Access, vol. 7, pp. 43356-43368, 2019.
[19] M. Peixoto, T. Genez, L.F. Bittencourt, “Hierarchical scheduling mechanisms in multi-level fog computing”, IEEE Transactions on Services Computing, vol. 15, no. 5, pp. 2824-2837, 2021.
[20] S. Sarkar, S. Misra, “Theoretical modelling of fog computing: a green computing paradigm to support iot applications”, Iet Networks, vol. 5, no. 2, pp. 23–29, 2016.
[21] H. Gupta, A.V. Dastjerdi, SK. Ghosh, R. Buyya, “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, vol. 47, no. 9, pp. 1275–1296, 2017.
[22] M. Goudarzi, M. Palaniswami, R Buyya, “A distributed application placement and migration management techniques for edge and fog computing environments”, In 2021 16th Conference on Computer Science and Intelligence Systems (FedCSIS), 2021, pp. 37–56.