UAV Service Caching for Task Offloading and Resource Allocation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In mobile edge computing (MEC) systems, the severe signal attenuation due to shadow fading and multipath effects in ground communication hinders efficient task offloading and resource allocation, particularly for computationally intensive and time-sensitive applications like facial recognition and virtual reality, where real-time delays and communication costs are high.
Innovation Solution
An unmanned aerial vehicle (UAV) assisted task offloading and resource allocation method based on service caching, which involves obtaining local popularity to cache services, optimizing task offloading decisions, UAV resource allocation, and trajectory planning using mixed integer nonlinear programming, slack variables, Lagrange multipliers, and Taylor expansion methods to minimize total time delay for ground terminal devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ground communication is used for task offloading, then infrastructure cost is reduced, but signal attenuation due to shadow fading and multipath effects causes severe communication reliability degradation
Solution Approach 1:
The patent transitions the communication medium from ground-based two-dimensional infrastructure to three-dimensional aerial UAV platforms. By deploying MEC servers on UAVs that can hover and move in three-dimensional space, the system bypasses ground-based shadow fading and multipath effects, establishing direct line-of-sight communication channels that significantly improve signal reliability and reduce attenuation.
Solution Approach 2:
The UAV acts as an intermediary carrier between ground terminal devices and the cloud network. Instead of direct ground-to-ground communication or ground-to-cloud through fixed infrastructure, the UAV serves as a mobile relay platform that receives tasks from ground devices and executes them via onboard MEC servers, mediating the communication and computing processes to overcome ground-based signal degradation.
2Productivity
If service programs are cached on MEC server, then task execution capability is improved, but real-time time delay for obtaining and initializing services increases
Solution Approach 1:
The system performs preliminary actions by pre-caching service programs not only on the UAV-based MEC server but also on ground terminal devices themselves. This advance preparation ensures that when tasks arrive, the necessary service programs are already available locally or can be quickly retrieved from the UAV's cache, eliminating initialization delays and enabling immediate task execution.
Solution Approach 2:
The patent implements a nested caching architecture where service programs are cached at multiple hierarchical levels: ground terminal devices contain local caches, UAVs contain regional caches, and the cloud contains the complete library. This nested structure allows tasks to be executed at the nearest available cache level, minimizing access time while maintaining comprehensive service availability across the distributed system.
3Loss of time
If computationally intensive tasks are processed on ground terminal devices, then communication cost is reduced, but task completion time increases due to limited local computing resources
Solution Approach 1:
The system dynamically allocates computing resources between ground terminal devices and UAV-based MEC servers based on task characteristics, device capabilities, and real-time conditions. Tasks can be flexibly offloaded to the UAV when local resources are insufficient, while simpler tasks remain local, creating a dynamic computing architecture that optimizes both time and energy consumption adaptively.
Data Source
AI summary
An unmanned aerial vehicle assisted task offloading and resource allocation method based on service caching is provided. The unmanned aerial vehicles are capable to cache a portion of programs to perform computation tasks for offloading of ground terminal devices, while a local terminal device is capable to cache a small number of programs. Under the constraints of task completion delay requirements for all devices and UAVs, as well as limited energy of UAVs, an optimization problem of minimizing the total time delay of request tasks of all ground terminal devices are established. This problem is a mixed integer nonlinear programming problem, which is decoupled into three sub-problems: task offloading decision, UAV resource allocation, and UAV trajectory. Slack variables, Lagrange multiplier method, and Taylor expansion method are used to iteratively solve the three sub-problems, and the optimal solutions for task offloading decision, UAV resource allocation, and UAV trajectory are obtained.