A Distributed Application Placement and Migration Management Techniques for Edge and Fog Computing Environments

• Autorzy: Goudarzi Mohammad , Palaniswami Marimuthu , Buyya Rajkumar

Identyfikatory

DOI

10.15439/2021F005

• Konferencja: Federated Conference on Computer Science and Information Systems (16 ; 02-05.09.2021 ; online)
• Języki publikacji: EN

Abstrakty

EN

Fog/Edge computing model allows harnessing of resources in the proximity of the Internet of Things (IoT) devices to support various types of latency-sensitive IoT applications. However, due to the mobility of users and a wide range of IoT applications with different resource requirements, it is a challenging issue to satisfy these applications' requirements. The execution of IoT applications exclusively on one fog/edge server may not be always feasible due to limited resources, while the execution of IoT applications on different servers requires further collaboration and management among servers. Moreover, considering user mobility, some modules of each IoT application may require migration to other servers for execution, leading to service interruption and extra execution costs. In this article, we propose a new weighted cost model for hierarchical fog computing environments, in terms of the response time of IoT applications and energy consumption of IoT devices, to minimize the cost of running IoT applications and potential migrations. Besides, a distributed clustering technique is proposed to enable the collaborative execution of tasks, emitted from application modules, among servers. Also, we propose an application placement technique to minimize the overall cost of executing IoT applications on multiple servers in a distributed manner. Furthermore, a distributed migration management technique is proposed for the potential migration of applications' modules to other remote servers as the users move along their path. Besides, failure recovery methods are embedded in the clustering, application placement, and migration management techniques to recover from unpredicted failures. The performance results demonstrate that our technique significantly improves its counterparts in terms of placement deployment time, average execution cost of tasks, the total number of migrations, the total number of interrupted tasks, and cumulative migration cost.

Słowa kluczowe

EN

cloud computing; Internet of things; mobile computing; resource allocation; telecommunication network reliability

PL

chmura obliczeniowa; Internet rzeczy; informatyka mobilna; alokacja zasobów; niezawodność sieci telekomunikacyjnych

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2021
• Tom: Vol. 25
• Strony: 37--56
• Opis fizyczny: Bibliogr. 36 poz., tab., il., wz., wykr.

Twórcy

autor

Goudarzi Mohammad

• The Cloud Computing and Distributed Systems (CLOUDS) Laboratory School of Computing and Information Systems The University of Melbourne, Australia

autor

Palaniswami Marimuthu

• The Department of Electrical and Electronic Engineering, The University of Melbourne, Australia

autor

Buyya Rajkumar

• The Cloud Computing and Distributed Systems (CLOUDS) Laboratory School of Computing and Information Systems The University of Melbourne, Australia

Bibliografia

• 1. R. Mahmud, K. Ramamohanarao, and R. Buyya, “Latency-aware application module management for fog computing environments,” ACM Transactions on Internet Technology (TOIT), vol. 19, no. 1, p. 9, 2018.
• 2. M. Goudarzi, H. Wu, M. Palaniswami, and 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.
• 3. M. Goudarzi, M. Palaniswami, and R. Buyya, “A fog-driven dynamic resource allocation technique in ultra dense femtocell networks,” Journal of Network and Computer Applications, vol. 145, p. 102407, 2019.
• 4. F. Guo, H. Zhang, H. Ji, X. Li, and V. C. 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, 2018.
• 5. X. Chen, L. Jiao, W. Li, and X. Fu, “Efficient multi-user computation offloading for mobile-edge cloud computing,” IEEE/ACM Transactions on Networking, vol. 24, no. 5, pp. 2795–2808, 2015.
• 6. S. Shckhar, A. Chhokra, H. Sun, A. Gokhale, A. Dubey, and X. Koutsoukos, “Urmila: A performance and mobility-aware fog/edge resource management middleware,” in 2019 IEEE 22nd International Symposium on Real-Time Distributed Computing (ISORC). IEEE, 2019, pp. 118–125.
• 7. M. Taneja and A. Davy, “Resource aware placement of iot application modules in fog-cloud computing paradigm,” in 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM). IEEE, 2017, pp. 1222–1228.
• 8. A. Kiani, N. Ansari, and A. Khreishah, “Hierarchical capacity provisioning for fog computing,” IEEE/ACM Transactions on Networking, vol. 27, no. 3, pp. 962–971, 2019.
• 9. S. Pallewatta, V. Kostakos, and R. Buyya, “Microservices-based iot application placement within heterogeneous and resource constrained fog computing environments,” in Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing, 2019, pp. 71–81.
• 10. H. Gupta, A. Vahid Dastjerdi, S. K. Ghosh, and 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.
• 11. L. Yang, H. Zhang, X. Li, H. Ji, and V. C. Leung, “A distributed computation offloading strategy in small-cell networks integrated with mobile edge computing,” IEEE/ACM Transactions on Networking, vol. 26, no. 6, pp. 2762–2773, 2018.
• 12. S. Jošilo and G. Dán, “Computation offloading scheduling for periodic tasks in mobile edge computing,” IEEE/ACM Transactions on Networking, vol. 28, no. 2, pp. 667–680, 2020.
• 13. S. Wang, Y. Guo, N. Zhang, P. Yang, A. Zhou, and X. S. Shen, “Delay-aware microservice coordination in mobile edge computing: A reinforcement learning approach,” IEEE Transactions on Mobile Computing, 2019, (in press).
• 14. Q. Deng, M. Goudarzi, and R. Buyya, “Fogbus2: a lightweight and distributed container-based framework for integration of iot-enabled systems with edge and cloud computing,” in Proceedings of the International Workshop on Big Data in Emergent Distributed Environments, 2021, pp. 1–8.
• 15. A. Machen, S. Wang, K. K. Leung, B. J. Ko, and T. Salonidis, “Live service migration in mobile edge clouds,” IEEE Wireless Communications, vol. 25, no. 1, pp. 140–147, 2017.
• 16. S. Wang, R. Urgaonkar, T. He, K. Chan, M. Zafer, and K. K. Leung, “Dynamic service placement for mobile micro-clouds with predicted future costs,” IEEE Transactions on Parallel and Distributed Systems, vol. 28, no. 4, pp. 1002–1016, 2016.
• 17. T. Ouyang, Z. Zhou, and X. Chen, “Follow me at the edge: Mobility-aware dynamic service placement for mobile edge computing,” IEEE Journal on Selected Areas in Communications, vol. 36, no. 10, pp. 2333–2345, 2018.
• 18. M. Adhikari, S. N. Srirama, and T. Amgoth, “Application offloading strategy for hierarchical fog environment through swarm optimization,” IEEE Internet of Things Journal, vol. 7, no. 5, pp. 4317–4328, 2019.
• 19. L. F. Bittencourt, J. Diaz-Montes, R. Buyya, O. F. Rana, and M. Parashar, “Mobility-aware application scheduling in fog computing,” IEEE Cloud Computing, vol. 4, no. 2, pp. 26–35, 2017.
• 20. S. Wang, R. Urgaonkar, M. Zafer, T. He, K. Chan, and K. K. Leung, “Dynamic service migration in mobile edge computing based on markov decision process,” IEEE/ACM Transactions on Networking, vol. 27, no. 3, pp. 1272–1288, 2019.
• 21. Z. Wang, Z. Zhao, G. Min, X. Huang, Q. Ni, and R. Wang, “User mobility aware task assignment for mobile edge computing,” Future Generation Computer Systems, vol. 85, pp. 1–8, 2018.
• 22. C. Yang, Y. Liu, X. Chen, W. Zhong, and S. Xie, “Efficient mobility-aware task offloading for vehicular edge computing networks,” IEEE Access, vol. 7, pp. 26 652–26 664, 2019.
• 23. Z. Liu, X. Wang, D. Wang, Y. Lan, and J. Hou, “Mobility-aware task offloading and migration schemes in scns with mobile edge computing,” in 2019 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2019, pp. 1–6.
• 24. C. Zhu, G. Pastor, Y. Xiao, Y. Li, and A. Ylae-Jaeaeski, “Fog following me: Latency and quality balanced task allocation in vehicular fog computing,” in 2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON). IEEE, 2018, pp. 1–9.
• 25. C. Zhang and Z. Zheng, “Task migration for mobile edge computing using deep reinforcement learning,” Future Generation Computer Systems, vol. 96, pp. 111–118, 2019.
• 26. F. Yu, H. Chen, and J. Xu, “Dmpo: Dynamic mobility-aware partial offloading in mobile edge computing,” Future Generation Computer Systems, vol. 89, pp. 722–735, 2018.
• 27. Y. Sun, S. Zhou, and J. Xu, “Emm: Energy-aware mobility management for mobile edge computing in ultra dense networks,” IEEE Journal on Selected Areas in Communications, vol. 35, no. 11, pp. 2637–2646, 2017.
• 28. Q. Qi, J. Wang, Z. Ma, H. Sun, Y. Cao, L. Zhang, and J. Liao, “Knowledge-driven service offloading decision for vehicular edge computing: A deep reinforcement learning approach,” IEEE Transactions on Vehicular Technology, vol. 68, no. 5, pp. 4192–4203, 2019.
• 29. D. Wang, Z. Liu, X. Wang, and Y. Lan, “Mobility-aware task offloading and migration schemes in fog computing networks,” IEEE Access, vol. 7, pp. 43 356–43 368, 2019.
• 30. H. Sami, A. Mourad, and W. El-Hajj, “Vehicular-obus-as-on-demand-fogs: Resource and context aware deployment of containerized micro-services,” IEEE/ACM Transactions on Networking, vol. 28, no. 2, pp. 778–790, 2020.
• 31. C. Puliafito, C. Vallati, E. Mingozzi, G. Merlino, F. Longo, and A. Puliafito, “Container migration in the fog: a performance evaluation,” Sensors, vol. 19, no. 7, p. 1488, 2019.
• 32. M. Goudarzi, Z. Movahedi, and M. Nazari, “Mobile cloud computing: a multisite computation offloading,” in 2016 8th International Symposium on Telecommunications (IST). IEEE, 2016, pp. 660–665.
• 33. X. Xu, Q. Liu, Y. Luo, K. Peng, X. Zhang, S. Meng, and L. Qi, “A computation offloading method over big data for iot-enabled cloud-edge computing,” Future Generation Computer Systems, vol. 95, pp. 522–533, 2019.
• 34. W. Zhang, J. Chen, Y. Zhang, and D. Raychaudhuri, “Towards efficient edge cloud augmentation for virtual reality mmogs,” in Proceedings of the Second ACM/IEEE Symposium on Edge Computing, 2017, pp. 1–14.
• 35. K. Kumar and Y.-H. Lu, “Cloud computing for mobile users: Can offloading computation save energy?” Computer, no. 4, pp. 51–56, 2010.
• 36. M. Goudarzi, M. Zamani, and A. T. Haghighat, “A fast hybrid multisite computation offloading for mobile cloud computing,” Journal of Network and Computer Applications, vol. 80, pp. 219–231, 2017.

Uwagi

1. Track: Preface

2. Session: Invited Contributions