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Task Data Offloading and Resource Allocation in Fog Computing With Multi-Task Delay Guarantee
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DOI：10.1109/ACCESS.2019.2941741.png)
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Abstract
En 中文
With the emergence of delay-sensitive task completion, computational offloading becomes increasingly desirable due to the end-users 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.
Keywords:
Task analysis
Delays
Cloud computing
Edge computing
Resource management
Servers
Computational modeling
5G and beyond
computation offloading
mobile edge computing
fog computing
resource allocation
offloading decision
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