Efficient SDN-based Task offloading in fog-assisted cloud environment





Task Offloading, Software defined networking, Energy Optimization, Queueing model, GI/G/r


A distributed computing model called "fog computing" provides cloud-like services which is closer to end devices, and is rapidly gaining popularity. It offers cloud-like computing including storage capabilities, but with less latency and bandwidth requirements, thereby improving the computation capabilities of IoT devices and mobile nodes. In addition, fog computing offers advantages such as support for context awareness, scalability, dependability, and node mobility. Fog computing is frequently used to offload tasks from end devices' applications, enabling quicker execution utilizing the fog nodes' capabilities. Because of the changing nature of the fog environment, task offloading is challenging and the multiple QoS criteria that depend on the type of application being used. This article proposes an SDN-based offloading technique to optimize the task offloading technique for scheduling and processing activities generated by the Internet of Space Things (IoST) devices. The proposed technique utilizes Software-Defined Networking (SDN) optimization to dynamically manage network resources and to facilitate the deployment and execution of offloaded tasks. To model the system which computes the optimal virtual machines (VM) to be allocated in the fog network in order to actively process the offloaded tasks, the GI/G/r queueing model is utilised. This approach minimizes the delay-sensitive task queue and minimises the necessary number of VMs while minimising the waiting time for the fog layer. The findings of the simulation are used to verify the effectiveness of the proposed model.


Download data is not yet available.
<br data-mce-bogus="1"> <br data-mce-bogus="1">


Samizadeh Nikoui, T, Rahmani, A. M, Balador, A, & Haj Seyyed Javadi, H. Internet of Things architecture challenges: A systematic review. International Journal of Communication Systems. 2021; 34(4): e4678.

Mukherjee M, Shu L, Wang D. Survey of fog computing: Fundamental, network applications, and research challenges. IEEE Communications Surveys & Tutorials. 2018; 20(3): 1826-1857.

Sotomayor, B, Montero, R. S, Llorente, I. M, Foster, I:Virtual infrastructure management in private and hybrid clouds. IEEE Internet computing. 2009; 13(5):14–22.

Sarkar S, Misra S: Theoretical modelling of fog computing: a green computing paradigm to support IoT applications, IET Networks. 2016; 5(2): 23–29.

Patra.S.S: Energy-efficient task consolidation for cloud data center. International Journal of Cloud Applications and Computing (IJCAC).2018; 8(1): 117-142.

Tassi A, Mavromatis I, Piechocki R, Nix A, Compton C, Poole T, Schuster W:Agile data offloading over novel fog computing infrastructure for CAVs. In 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring); 28 Apr-1 May 2019, Kuala Lumpur, Malaysia: IEEE; 2019,1-6.

Misra S, Saha N: Detour: Dynamic task offloading in software-defined fog for IoT applications. IEEE Journal on Selected Areas in Communications. 2019; 37(5):1159-1166.

Varshney K, Jain M, Sharma G. C: The M/M/m/K Queuing System with Additional Servers for a Large Queue. Proceeding of the seminar 65th Birthday celebration of Prof. S.C. Dasgupta, 1988, 277-282.

Jain M, Sharma B, Sharma G. C: On No passing Multiserver Queuing Model with Two Types of Customers and Discouragement. Journal of Mathematical Physics Scimago. 1989; 23(4): 319 - 329.

Garg K. M, Jain M, Sharma G. C: "G/Gy/m Queuing System with Discouragement Via diffusion Approximation. Microelectronics Reliability. 1993; 33(7): 1057-1059.

Varshney K, Jain M, Sharma G. C: Diffusion Approximation for G/G/m Queuing System with Discouragement. Journal of the Indian Statistical Association. 1987; 25: 91- 96.

Jain M: M/M/m Queue with Discouragement and Additional Servers. Gujarat Statistical Review. 1998; 25(1-2): 31-42.

Kumar R, Sharma S. K: An M/M/c/N queuing system with reneging and retention of reneged customers. International Journal of Operational Research. 2013; 17: 333–344.

Kumar R, Sharma S. K: A Markovian multi-server queuing model with retention of reneged customers and balking. International Journal of Operational Research. 2014; 20(4): 427–438.

Khazaei H, Misic J, Misic V. B: Performance analysis of cloud computing centers using ∕𝑔∕𝑚∕𝑚 + 𝑟 queuing systems. IEEE Transactions on Parallel and Distributed Systems. 2011; 23 (5): 936–943.

Outamazirt A, Barkaoui K, Aissani D: Maximizing profit in cloud computing using 𝑀∕𝐺∕𝑐∕𝑘 queuing model. In 2018 International Symposium on Programming and Systems, (ISPS); 24-26 Apr 2018, Algeria: IEEE, 2018, 1–6.

Khazaei H, Misic J, Misic V. B: Modelling of cloud computing centers using 𝑀∕𝐺∕𝑚 queues. In 2011 31st International Conference on Distributed Computing Systems Workshops; 20-24 June 2011, Minneapolis, Minnesota USA: IEEE, 2011, 87-92.

Goswami V, Patra S. S, Mund G. B: Performance analysis of cloud with queue-dependent virtual machines. In 2012 1st International conference on recent advances in information technology (RAIT); 15-17 Mar 2012, ISM Dhanbad, India: IEEE, 2012, 357-362.

Patra S. S, Govindaraj R, Chowdhury S, Shah M. A, Patro R, Rout S: Energy Efficient End Device Aware Solution Through SDN in Edge-Cloud Platform. IEEE Access. 2022; 10: 115192-115204.




How to Cite

B. B. Dash, R. Satpathy, and S. S. Patra, “Efficient SDN-based Task offloading in fog-assisted cloud environment”, EAI Endorsed Trans IoT, vol. 10, Dec. 2023.