Joint Task Offloading and Resource Allocation for Delay-Sensitive Fog Networks

Computational offloading becomes an important and essential research issue for the delay-sensitive task completion at resource-constraint end-users. Fog computing that extends the computing and storage resources of the cloud computing to the network edge emerges as a potential solution towards low-latency task provisioning via computational offloading. In our offloading scenario, each end-user will first offload the task to its primary fog node. When the primary fog node cannot meet the tolerable latency, it has the possibility to offload to the cloud and/or assisting fog node to obtain extra computing resource to shorten the computing latency at the expense of additional transmission latency. Therefore, a trade-off needs to be carefully made in the offloading decision. At the same time, in addition to the task data from the end-users under its primary coverage, the primary fog node receives the tasks from other end-users via its neighbor fog nodes. Thus, to jointly optimize the computing and communication resources in the fog node, we formulate a delay-sensitive data offloading problem that mainly considers the local task execution delay and transmission delay. An approximate solution is obtained via Quadratically Constraint Quadratic Programming (QCQP). Finally, the extensive simulation results demonstrate the effectiveness of the proposed solution, while guaranteeing minimum end-to-end latency for various task processing densities and traffic intensity levels.