A mobility-aware service migration technique in fog computing environments
DOI:
https://doi.org/10.22399/ijcesen.4319Keywords:
Fog computing, Migration, Mobility, Internet of Things, Real-time applicationAbstract
Fog computing allows the utilization of resources near the Internet of Things (IoT) devices to serve various latency-sensitive applications. However, the mobility of users of IoT devices necessitates the migration of applications to maintain service continuity and quality of service (QoS). This study proposes a new migration technique that minimizes delay, network usage and energy consumption in the Fog network, providing a real-time user experience. An objective function-based decision-making approach is used to migrate the applications efficiently, guaranteeing service continuity and QoS. The proposed technique chose an appropriate fog node with sufficient resources by evaluating parameters like connection duration between the fog nodes and the users, resource availability, and application execution time at the fog nodes. The results indicate that the proposed approach has a remarkable improvement of up to 20% in average delay, 16% in network usage and 7% in energy consumption compared to the conventional approach. The number of migrations is also lowered by 18%, which is necessary to efficiently utilize limited fog node resources as each migration event consumes additional resources. The benefits of the proposed approach for the users are low latencies, low network usage, improved energy efficiency and better user experience.
References
[1.] Afrin, M., Jin, J., Rahman, A., Gasparri, A., Tian, Y.-C., & Kulkarni, A. (2021). Robotic edge resource allocation for agricultural cyber-physical system. IEEE Transactions on Network Science and Engineering. https://doi.org/10.1109/TNSE.2021.3103602
[2.] Alam, M., Ahmed, N., Matam, R., & Barbhuiya, F. A. (2022). L3Fog: Fog node selection and task offloading framework for mobile IoT. In INFOCOM Workshops 2022 – IEEE Conference on Computer Communications Workshops. https://doi.org/10.1109/INFOCOMWKSHPS54753.2022.9798118
[3.] Anawar, M. R., Wang, S., Azam Zia, M., Jadoon, A. K., Akram, U., & Raza, S. (2018). Fog computing: An overview of big IoT data analytics. Wireless Communications and Mobile Computing, 2018, Article 7157192. https://doi.org/10.1155/2018/7157192
[4.] Bellavista, P., & Zanni, A. (2017). Feasibility of fog computing deployment based on Docker containerization over Raspberry Pi. In ACM International Conference Proceeding Series. https://doi.org/10.1145/3007748.3007777
[5.] Bi, Y., Han, G., Lin, C., Deng, Q., Guo, L., & Li, F. (2018). Mobility support for fog computing: An SDN approach. IEEE Communications Magazine, 56(5), 53–59. https://doi.org/10.1109/MCOM.2018.1700908
[6.] Bittencourt, L. F., Diaz-Montes, I. J., Buyya, R., Rana, O. F., & Parashar, M. (2017). Mobility-aware application scheduling in fog computing. IEEE.
[7.] Bonomi, F., Milito, R., Natarajan, P., & Zhu, J. (2014). Fog computing: A platform for Internet of Things and analytics. In Studies in Computational Intelligence (Vol. 546). https://doi.org/10.1007/978-3-319-05029-4_7
[8.] Clark, C., et al. (2005). Live migration of virtual machines. In Proceedings of the 2nd Conference on Symposium on Networked Systems Design & Implementation (NSDI’05) (pp. 273–286). USENIX Association.
[9.] Codeca, L., Frank, R., & Engel, T. (2015). Luxembourg SUMO Traffic (LuST) scenario: 24 hours of mobility for vehicular networking research. In 2015 IEEE Vehicular Networking Conference (VNC) (pp. 1–8). https://doi.org/10.1109/VNC.2015.7385539
[10.] Cisco Public. (2015). Fog computing and the Internet of Things: Extend the cloud to where the things are. https://www.cisco.com/c/dam/en_us/solutions/trends/iot/docs/computing-overview.pdf
[11.] Deswal, S., & Singhrova, A. (2020). Quality of service provisioning using multicriteria handover strategy in overlaid networks. Malaysian Journal of Computer Science, 33(1), 1–21. https://doi.org/10.22452/MJCS.VOL33NO1.1
[12.] Ghanavati, S., Abawajy, J., & Izadi, D. (2022). An energy aware task scheduling model using ant-mating optimization in fog computing environment. IEEE Transactions on Services Computing, 15(4), 2007–2017. https://doi.org/10.1109/TSC.2020.3028575
[13.] Goudarzi, M., Palaniswami, M., & Buyya, R. (2021). A distributed application placement and migration management techniques for edge and fog computing environments. arXiv. http://arxiv.org/abs/2108.02328
[14.] Guerrero, C., Lera, I., & Juiz, C. (2019). A lightweight decentralized service placement policy for performance optimization in fog computing. Journal of Ambient Intelligence and Humanized Computing, 10(6), 2435–2452. https://doi.org/10.1007/s12652-018-0914-0
[15.] Gupta, H., Dastjerdi, A. V., Ghosh, S. K., & Buyya, R. (2017). 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, 47(9), 1275–1296. https://doi.org/10.1002/spe.2509
[16.] Islam, M., Razzaque, A., & Islam, J. (2016). A genetic algorithm for virtual machine migration in heterogeneous mobile cloud computing. In Proceedings of the 2016 International Conference on Networking Systems and Security (NSysS). https://doi.org/10.1109/NSysS.2016.7400696
[17.] Kaur, K., Dhand, T., Kumar, N., & Zeadally, S. (2017). Container-as-a-service at the edge: Trade-off between energy efficiency and service availability at fog nano data centers. IEEE Wireless Communications, 24(3). https://doi.org/10.1109/MWC.2017.1600427
[18.] Liao, S., Li, J., Wu, J., Yang, W., & Guan, Z. (2019). Fog-enabled vehicle as a service for computing geographical migration in smart cities. IEEE Access, 7, 2890298. https://doi.org/10.1109/ACCESS.2018.2890298
[19.] Liu, C., Wang, J., Zhou, L., & Rezaeipanah, A. (2022). Solving the multi-objective problem of IoT service placement in fog computing using Cuckoo search algorithm. Neural Processing Letters, 54(3), 1823–1854. https://doi.org/10.1007/s11063-021-10708-2
[20.] Lopes, M. M., Higashino, W. A., Capretz, M. A. M., & Bittencourt, L. F. (2017). MyiFogSim. In Companion Proceedings of the 10th International Conference on Utility and Cloud Computing (UCC ’17 Companion) (pp. 47–52). https://doi.org/10.1145/3147234.3148101
[21.] Machen, A., Wang, S., Leung, K. K., Ko, B. J., & Salonidis, T. (2018). Live service migration in mobile edge clouds. IEEE Wireless Communications, 25(1), 140–147. https://doi.org/10.1109/MWC.2017.1700011
[22.] Mahmud, R., Pallewatta, S., Goudarzi, M., & Buyya, R. (2022). iFogSim2: An extended iFogSim simulator for mobility, clustering, and microservice management in edge and fog computing environments. Journal of Systems and Software, 190, 111351. https://doi.org/10.1016/j.jss.2022.111351
[23.] Mahmud, R., Ramamohanarao, K., & Buyya, R. (2018). Latency-aware application module management for fog computing environments. ACM Transactions on Internet Technology, 19(1), 1–21. https://doi.org/10.1145/3186592
[24.] Mahmud, R., Srirama, S. N., Ramamohanarao, K., & Buyya, R. (2019). QoE-aware placement of applications in fog computing environments. Journal of Parallel and Distributed Computing, 132, 62–71. https://doi.org/10.1016/j.jpdc.2018.03.004
[25.] Martin, J. P., Kandasamy, A., & Chandrasekaran, K. (2018). Exploring the support for high performance applications in the container runtime environment. Human-centric Computing and Information Sciences, 8(1). https://doi.org/10.1186/s13673-017-0124-3
[26.] Mishra, M., Roy, S. K., Mukherjee, A., De, D., Ghosh, S. K., & Buyya, R. (2020). An energy-aware multi-sensor geo-fog paradigm for mission critical applications. Journal of Ambient Intelligence and Humanized Computing, 11(8). https://doi.org/10.1007/s12652-019-01481-1
[27.] Naha, R. K., & Garg, S. (2021). Multi-criteria-based dynamic user behaviour-aware resource allocation in fog computing. ACM Transactions on Internet of Things, 2(1), 1–31. https://doi.org/10.1145/3423332
[28.] Naha, R. K., et al. (2018). Fog computing: Survey of trends, architectures, requirements, and research directions. IEEE Access, 6, 47980–48009. https://doi.org/10.1109/ACCESS.2018.2866491
[29.] Paul, J., Kandasamy, M. A., & Martin, J. P. (2020). Mobility aware autonomic approach for migration of application modules in fog computing environment. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-020-01854-x
[30.] Puliafito, C., Vallati, C., Mingozzi, E., Merlino, G., Longo, F., & Puliafito, A. (2019). Container migration in the fog: A performance evaluation. Sensors, 19(7), 1488. https://doi.org/10.3390/s19071488
[31.] Puliafito, C., et al. (2019). MobFogSim: Simulation of mobility and migration for fog computing. Simulation Modelling Practice and Theory, 94, 102062. https://doi.org/10.1016/j.simpat.2019.102062
[32.] Sarrafzade, N., Entezari-Maleki, R., & Sousa, L. (2022). A genetic-based approach for service placement in fog computing. Journal of Supercomputing, 78(8), 10854–10875. https://doi.org/10.1007/s11227-021-04254-w
[33.] Satyanarayanan, M. (2017). The emergence of edge computing. Computer, 50(1). https://doi.org/10.1109/MC.2017.9
[34.] Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x
[35.] Shckhar, S., Chhokra, A., Sun, H., Gokhale, A., Dubey, A., & Koutsoukos, X. (2019). URMILA: A performance and mobility-aware fog/edge resource management middleware. In Proceedings of the 2019 IEEE 22nd International Symposium on Real-Time Distributed Computing (ISORC). https://doi.org/10.1109/ISORC.2019.00033
[36.] Shi, W., & Dustdar, S. (2016). The promise of edge computing. Computer, 49(5). https://doi.org/10.1109/MC.2016.145
[37.] Statista. Vailshery, L. S. (2021). Number of IoT connected devices worldwide 2019–2021, with forecasts to 2030. https://www.statista.com/statistics/1183457/iot-connected-devices-worldwide/
[38.] Wang, D., Liu, Z., Wang, X., & Lan, Y. (2019). Mobility-aware task offloading and migration schemes in fog computing networks. IEEE Access, 7, 43356–43368. https://doi.org/10.1109/ACCESS.2019.2908263
[39.] Wang, S., Urgaonkar, R., He, T., Chan, K., Zafer, M., & Leung, K. K. (2017). Dynamic service placement for mobile micro-clouds with predicted future costs. IEEE Transactions on Parallel and Distributed Systems, 28(4). https://doi.org/10.1109/TPDS.2016.2604814
[40.] Wang, S., Guo, Y., Zhang, N., Yang, P., Zhou, A., & Shen, X. (2021). Delay-aware microservice coordination in mobile edge computing: A reinforcement learning approach. IEEE Transactions on Mobile Computing, 20(3). https://doi.org/10.1109/TMC.2019.2957804
[41.] Yi, S., Li, C., & Li, Q. (2015). A survey of fog computing: Concepts, applications, and issues. In MOBIDATA 2015: Workshop on Mobile Big Data. https://doi.org/10.1145/2757384.2757397
[42.] Zhao, D., Zou, Q., & Boshkani Zadeh, M. (2022). A QoS-aware IoT service placement mechanism in fog computing based on open-source development model. Journal of Grid Computing, 20(2). https://doi.org/10.1007/s10723-022-09604-3
[43.] Zhao, X., Liu, J., Ji, B., & Wang, L. (2021). Service migration policy optimization considering user mobility for e-healthcare applications. Journal of Healthcare Engineering, 2021, Article 9922876. https://doi.org/10.1155/2021/9922876
[44.] Zhu, C., Pastor, G., Xiao, Y., Li, Y., & Ylä-Jääski, A. (2018). Fog following me: Latency and quality balanced task allocation in vehicular fog computing. In 2018 IEEE SECON. https://doi.org/10.1109/SAHCN.2018.8397129
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