International Journal of Computer Networks and Applications

Open Access Open Access  Restricted Access Subscription Access

A Collaborative Offloading Task Framework for IoT Fog Computing


Affiliations
1 Department of Computer Science, Suez Canal University, Ismailia, Egypt
 

Fog computing is a viable approach to improving the performance of cloud computing, especially in terms of response time, which is critical to real-time applications. Specifically, the fog brings the cloud resources closer to terminal devices (TDs), thereby decreasing latency and increasing throughput. The problem of task offloading from TDs to the fog has enjoyed much research work, but the issue of TD mobility has not found enough attention, and hence is the present work. Herein, TD mobility in fog computing involves the transfer of services while the TD is moving from one fog to another, requiring delicate coordination between the fogs. To this end, a framework is proposed to ensure that the fogs together with the cloud collaborate, first to always keep track of the current location of the TD offloading the task, and second to accurately serve the task in a distributed fashion while the TD is moving. The framework dedicates two queues in each fog, one to receive fresh tasks from TDs and one to receive hand-over tasks from other fogs, and leverages a vigilant inter-fog messaging system capable of keeping all concerned components abreast of the latest status. A program has been written in Python to simulate the framework and example operational environments. The program has been used to perform extensive experiments in order to assess the performance of the framework under high and low mobility conditions. The findings indicate that the framework is highly reliable and can deliver, under various mobility modes, the right response to the right TD at the right time.

Keywords

Internet of Things, Task Offloading, Response Time, Mobility, Cloud Computing, Fog Computing.
User
Notifications
Font Size

  • Cisco Annual Internet Report (2018–2023), [Online]. Available at : [https://www.cisco.com/c/en/us/solutions/collateral/executiveperspectives/annual-internet-report/white-paper-c11-741490.pdf]

  • S. Smith, "IoT connections to grow 140% to hit 50 billion by 2022, as edge computing accelerates RoI," Juniper Research, 2018.

  • D. Mendes et al., "VITASENIOR-MT: A distributed and scalable cloud-based telehealth solution," 2019 IEEE 5th World Forum on Internet of Things (WF-IoT), Limerick, Ireland, 2019, pp. 767-772, doi: 10.1109/WF-IoT.2019.8767184.

  • K. Verma, A. Kumar, M. Salim Ul Islam, T. Kanwar, M. Bhushan, "Rank based mobility-aware scheduling in Fog computing", Informatics in Medicine Unlocked, vol. 24, 2021, 100619, doi.org/10.1016/j.imu.2021.100619.

  • Y. Kyung, "Performance Analysis of Task Offloading With Opportunistic Fog Nodes," in IEEE Access, vol. 10, pp. 4506-4512, 2022, doi: 10.1109/ACCESS.2022.3141199.

  • J. Kuliga, S. Massicot, R. Adhikari, M. Ruppel, N. Jux, H.-P. Steinrück and H. Marbach, "Conformation Controls Mobility: 2HTetranaphthylporphyrins on Cu (111)," ChemPhysChem, vol. 21, p. 423–427, 2020, doi.org/10.1002/cphc.201901135

  • 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, August 2012 , pp. 13–16, doi.org/10.1145/2342509.2342513

  • M. Chiang and T. Zhang, "Fog and IoT: An Overview of Research Opportunities," in IEEE Internet of Things Journal, vol. 3, no. 6, pp. 854-864, Dec. 2016, doi: 10.1109/JIOT.2016.2584538.

  • V. Dastjerdi, H. Gupta, R. N. Calheiros, S. K. Ghosh and R. Buyya, "Chapter 4 - Fog Computing: principles, architectures, and applications," in Internet of Things, R. Buyya and A. V. Dastjerdi, Eds., Morgan Kaufmann, 2016, pp. 61-75.

  • P. Bellavista, J. Berrocal, A. Corradi, S. K. Das, L. Foschini and A. Zanni, "A survey on fog computing for the Internet of Things," Pervasive and Mobile Computing, vol. 52, pp. 71-99, 2019, doi.org/10.1016/B978-0-12-805395-9.00004-6.

  • V. Dastjerdi and R. Buyya, "Fog Computing: Helping the Internet of Things Realize Its Potential," in Computer, vol. 49, no. 8, pp. 112-116, Aug. 2016, doi: 10.1109/MC.2016.245.

  • F. Jalali, K. Hinton, R. Ayre, T. Alpcan and R. S. Tucker, "Fog Computing May Help to Save Energy in Cloud Computing," in IEEE Journal on Selected Areas in Communications, vol. 34, no. 5, pp. 1728- 1739, May 2016, doi: 10.1109/JSAC.2016.2545559.

  • N. Kumari, A. Yadav and P. K. Jana, "Task offloading in fog computing: A survey of algorithms and optimization techniques," Computer Networks, vol. 214, pp. 109137, 2022, doi.org/10.1016/j.comnet.2022.109137.

  • R. Buyya,; S.Narayana Srirama, "Management and Orchestration of Network Slices in 5G, Fog, Edge, and Clouds," in Fog and Edge Computing: Principles and Paradigms , Wiley, 2019, pp.79-101, doi: 10.1002/9781119525080.ch4.

  • Lakhan, M. Ahmad, M. Bilal, A. Jolfaei and R. M. Mehmood, "Mobility Aware Blockchain Enabled Offloading and Scheduling in Vehicular Fog Cloud Computing," in IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 7, pp. 4212-4223, July 2021, doi: 10.1109/TITS.2021.3056461.

  • H. Raouf, R. Abdallah, H. Y. M. Soliman and R. Rizk, "MobilityAware Task Offloading Enhancement in Fog Computing Networks," in The 8th International Conference on Advanced Machine Learning and Technologies and Applications (AMLTA2022). AMLTA 2022, vol 113. Springer, Cham, doi.org/10.1007/978-3-031-03918-8_47.

  • F. Chiti, R. Fantacci and B. Picano, "A Matching Theory Framework for Tasks Offloading in Fog Computing for IoT Systems," in IEEE Internet of Things Journal, vol. 5, no. 6, pp. 5089-5096, Dec. 2018, doi: 10.1109/JIOT.2018.2871251.

  • Z. Zhao et al., "On the Design of Computation Offloading in Fog Radio Access Networks," in IEEE Transactions on Vehicular Technology, vol. 68, no. 7, pp. 7136-7149, July 2019, doi: 10.1109/TVT.2019.2919915.

  • 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," in IEEE Transactions on Communications, vol. 66, no. 4, pp. 1594-1608, April 2018, doi: 10.1109/TCOMM.2017.2787700.

  • S. Misra and N. Saha, "Detour: Dynamic Task Offloading in SoftwareDefined Fog for IoT Applications," in IEEE Journal on Selected Areas in Communications, vol. 37, no. 5, pp. 1159-1166, May 2019, doi: 10.1109/JSAC.2019.2906793.

  • L. Liu, Z. Chang, X. Guo, S. Mao and T. Ristaniemi, "Multiobjective Optimization for Computation Offloading in Fog Computing," in IEEE Internet of Things Journal, vol. 5, no. 1, pp. 283-294, Feb. 2018, doi: 10.1109/JIOT.2017.2780236.

  • J. Yao and N. Ansari, "QoS-Aware Fog Resource Provisioning and Mobile Device Power Control in IoT Networks," in IEEE Transactions on Network and Service Management, vol. 16, no. 1, pp. 167-175, March 2019, doi: 10.1109/TNSM.2018.2888481.

  • Yousefpour, G. Ishigaki, R. Gour and J. P. Jue, "On Reducing IoT Service Delay via Fog Offloading," in IEEE Internet of Things Journal, vol. 5, no. 2, pp. 998-1010, April 2018, doi: 10.1109/JIOT.2017.2788802.

  • S. Jošilo and G. Dán, "Decentralized Algorithm for Randomized Task Allocation in Fog Computing Systems," in IEEE/ACM Transactions on Networking, vol. 27, no. 1, pp. 85-97, Feb. 2019, doi: 10.1109/TNET.2018.2880874.

  • S. Ghosh, A. Mukherjee, S. K. Ghosh and R. Buyya, "Mobi-IoST: Mobility-Aware Cloud-Fog-Edge-IoT Collaborative Framework for Time-Critical Applications," in IEEE Transactions on Network Science and Engineering, vol. 7, no. 4, pp. 2271-2285, 1 Oct.-Dec. 2020, doi: 10.1109/TNSE.2019.2941754.

  • M. M. Razaq, B. Tak, L. Peng and M. Guizani, "Privacy-Aware Collaborative Task Offloading in Fog Computing," in IEEE Transactions on Computational Social Systems, vol. 9, no. 1, pp. 88-96, Feb. 2022, doi: 10.1109/TCSS.2020.3047382.

  • M. Keshavarznejad, M. H. Rezvani and S. Adabi, "Delay-aware optimization of energy consumption for task offloading in fog environments using metaheuristic algorithms," Cluster Comput vol. 24, pp.1825–1853 (2021). https://doi.org/10.1007/s10586-020-03230-y.

  • Qayyum, T., Trabelsi, Z., Waqar Malik, A. et al. Mobility-aware hierarchical fog computing framework for Industrial Internet of Things (IIoT). Journal of Cloud Computing vol. 11, no. 72 (2022). https://doi.org/10.1186/s13677-022-00345-y

  • Rahbari, D., Nickray, M. Task offloading in mobile fog computing by classification and regression tree. Peer-to-Peer Networking and Applications, vol. 13 , pp. 104–122 (2020). https://doi.org/10.1007/s12083-019-00721-7

  • Z. Ning et al., "Partial Computation Offloading and Adaptive Task Scheduling for 5G-Enabled Vehicular Networks," in IEEE Transactions on Mobile Computing, vol. 21, no. 4, pp. 1319-1333, 1 April 2022, doi: 10.1109/TMC.2020.3025116.

  • ALVI, Ahmad Naseem, et al. Intelligent task offloading in fog computing based vehicular networks. Applied Sciences, 2022, vol. 12, no. 9, 4521, https://doi.org/10.3390/app12094521

  • Hosseini, E., Nickray, M. & Ghanbari, S. Energy-efficient scheduling based on task prioritization in mobile fog computing. Computing vol. 105, pp. 187–215 (2023). https://doi.org/10.1007/s00607-022-01108-y

  • Aisha Muhammad A. Hamdi, Farookh Khadeer Hussain, Omar K. Hussain, Task offloading in vehicular fog computing: State-of-the-art and open issues, Future Generation Computer Systems, vol. 133, 2022, pp. 201-212, https://doi.org/10.1016/j.future.2022.03.019.


Abstract Views: 244

PDF Views: 1




  • A Collaborative Offloading Task Framework for IoT Fog Computing

Abstract Views: 244  |  PDF Views: 1

Authors

Amira S. Ibrahim
Department of Computer Science, Suez Canal University, Ismailia, Egypt
Hassan Al-Mahdi
Department of Computer Science, Suez Canal University, Ismailia, Egypt
Hamed Nassar
Department of Computer Science, Suez Canal University, Ismailia, Egypt

Abstract


Fog computing is a viable approach to improving the performance of cloud computing, especially in terms of response time, which is critical to real-time applications. Specifically, the fog brings the cloud resources closer to terminal devices (TDs), thereby decreasing latency and increasing throughput. The problem of task offloading from TDs to the fog has enjoyed much research work, but the issue of TD mobility has not found enough attention, and hence is the present work. Herein, TD mobility in fog computing involves the transfer of services while the TD is moving from one fog to another, requiring delicate coordination between the fogs. To this end, a framework is proposed to ensure that the fogs together with the cloud collaborate, first to always keep track of the current location of the TD offloading the task, and second to accurately serve the task in a distributed fashion while the TD is moving. The framework dedicates two queues in each fog, one to receive fresh tasks from TDs and one to receive hand-over tasks from other fogs, and leverages a vigilant inter-fog messaging system capable of keeping all concerned components abreast of the latest status. A program has been written in Python to simulate the framework and example operational environments. The program has been used to perform extensive experiments in order to assess the performance of the framework under high and low mobility conditions. The findings indicate that the framework is highly reliable and can deliver, under various mobility modes, the right response to the right TD at the right time.

Keywords


Internet of Things, Task Offloading, Response Time, Mobility, Cloud Computing, Fog Computing.

References





DOI: https://doi.org/10.22247/ijcna%2F2023%2F220739