Work

Delay-aware partial task offloading using multicriteria decision model in IoT–fog–cloud networks

Year: 2025

Type: article

Abstract: Fog computing plays a prominent role in offloading computational tasks in heterogeneous environments since it provides less service delay than traditional cloud computing. The Internet of Things (IoT) devices cannot handle complex tasks due to less battery power, storage and computational capability. Full offloading has issues in providing efficient computation delay due to more response time and transmission cost. A suitable solution to overcome this problem is to partition the tasks into splittable subtasks. Considering multi-criteria decision parameters like processing efficiency and deadline helps to achieve efficient resource allocation and task assignment. The matching theory is applied to map task nodes to heterogeneous fog nodes and VMs for stability. Compared to baseline algorithms, proposed algorithms like Resource Allocation based on Processing Efficiency (RABP) and Task Assignment Based on Completion Time (TAC) are efficient enough to provide reasonable service delay and discard the non-beneficial tasks, i.e., tasks that do not execute within the deadline.

Source: Journal of Network and Computer Applications

DOI: 10.1016/j.jnca.2025.104278

Authors: Sushma S.A., Madhunisha E., Sourav Kanti Addya, Saifur Rahman, Shantanu Pal, Chandan Karmakar

Institutions: National Institute of Technology Karnataka, Siddaganga Institute of Technology, Deakin University

Language: English

Topic: IoT and Edge/Fog Computing

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $3,120