Time-Slotted Task Offloading and Resource Allocation for Cloud-Edge-End Cooperative Computing Networks

In time-slotted edge computing systems, task scheduling is conducted at the end of each time slot to make task offloading decisions and resource allocation for all the tasks pending for scheduling during the time slot. However, the existing works omitted the task scheduling delay, which is a period that a task has to wait from the task generation time point to the end of the current time slot. Such simplification is impractical in real scenarios because the task scheduling delay is a non-negligible part of the task processing delay, which was understood by existing works as the sum of only the task transmission and computing delays. In this paper, a novel time-slotted task offloading and resource allocation scheme for cloud-edge-end cooperative computing networks is proposed to realize the total task processing delay minimization for all the devices under the energy consumption constraint of each device. Our scheme makes task-offloading decision for each device from local processing, offloading to its affiliated base station (BS), to another BS, and to the cloud server. Besides, transmit power allocation, transmission rate allocation, and computing resource allocation are also jointly optimized in our optimization problem. We consider the impact of the task scheduling delay and design a two-stage distributed algorithm to decrease the negative impact by dividing the algorithm into a device-side part and a network-side part. The advantages of our scheme are validated by extensive simulations, where 4 reference schemes are compared in 8 different scenarios.