Task Data Offloading and Resource Allocation in Fog Computing With Multi-Task Delay Guarantee

IEEE Access

Rakesh Matam¹  Constandinos X. Mavromoustakis²  Qi Zhang³  George Mastorakis⁴  Suman Kumar⁵  Mithun Mukherjee⁶  Yunrong Lv⁶ 

[1] Department of Computer Science and Engineering, Indian Institute of Information Technology Guwahati, Guwahati, India;Department of Computer Science, Mobile Systems Laboratory (MoSys Lab), University of Nicosia, Nicosia, Cyprus;Department of Engineering, DIGIT, Aarhus University, Aarhus, Denmark;Department of Management Science and Technology, Hellenic Mediterranean University, Crete, Greece;Department of Mathematics, IGNTU, Amarkantak, India;Guangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis, Guangdong University of Petrochemical Technology, Maoming, China;

关键词: 5G and beyond;    computation offloading;    mobile edge computing;    fog computing;    resource allocation;    offloading decision;   

DOI  :  10.1109/ACCESS.2019.2941741

来源: DOAJ

【 摘 要 】

With the emergence of delay-sensitive task completion, computational offloading becomes increasingly desirable due to the end-user's limitations in performing computation-intense applications. Interestingly, fog computing enables computational offloading for the end-users towards delay-sensitive task provisioning. In this paper, we study the computational offloading for the multiple tasks with various delay requirements for the end-users, initiated one task at a time in end-user side. In our scenario, the end-user offloads the task data to its primary fog node. However, due to the limited computing resources in fog nodes compared to the remote cloud server, it becomes a challenging issue to entirely process the task data at the primary fog node within the delay deadline imposed by the applications initialized by the end-users. In fact, the primary fog node is mainly responsible for deciding the amount of task data to be offloaded to the secondary fog node and/or remote cloud. Moreover, the computational resource allocation in term of CPU cycles to process each bit of the task data at fog node and transmission resource allocation between a fog node to the remote cloud are also important factors to be considered. We have formulated the above problem as a Quadratically Constraint Quadratic Programming (QCQP) and provided a solution. Our extensive simulation results demonstrate the effectiveness of the proposed offloading scheme under different delay deadlines and traffic intensity levels.

【 授权许可】

Unknown