Research On Task Offoloading And Service Management Technology With Edge-cloud Cooperation

Edge computing enables resource-constrained terminal devices to run high-resource-demand applications by improving the computing and storage capabilities of the network,and reducing the bandwidth and latency of the network.Therefore,it has become an effective solution for processing tasks that are computationally intensive and sensitive to latency.Many tasks that require real-time processing,such as digital twins,autonomous driving,interactive gaming,and blockchain-driven software,require low-latency-performance.Optimizing the issues involved in task management and task unloading during task processing in the context of edge computing is currently a focus of research.For a multi-task and multi-service scenario with edge-cloud cooperation,this thesis proposes a collaborative service placement,task scheduling,computing resource allocation,and transmission rate allocation scheme.The objective of our optimization problem is to minimize the total task processing delay while guaranteeing long-term task queuing stability.Considering the high complexity of the original optimization problem,we transform the problem into a deterministic problem for each time slot based on the Lyapunov optimization.Then,we design an iterative algorithm to obtain the whole solution to the problem efficiently based on a hybrid method using multiple numerical techniques.Further,considering the inherent difference in the optimization periods of the service placement,resource allocation,and task scheduling sub-problems,we design a multi-timescale algorithm to solve the sub-problems with different optimization periods.The complexity of the proposed algorithms is analyzed,and extensive simulations are conducted by varying multiple crucial parameters.The superiority of our scheme is demonstrated in comparison with four other schemes,the total delay of task processing is reduced by 16.6%,47.3%,56.5%and 58.1%,respectively.For users with strong mobile behaviors and high randomness,this thesis proposes a joint optimization scheme for service migration,computing resource allocation,and transmission speed allocation in edge networks,which are suitable for users with strong mobile behaviors and high randomness.The optimization objective of this scheme jointly consider the total task delay and total energy consumption in the system,and weights them to optimize them in different conditions.Firstly,the thesis decomposes the problem and propose a service migration algorithm based on selecting the best migrated user in each round to make the most beneficial service migration decisions.Then,it appropriately allocates computing resources based on the migrated users.In the simulation experiments,compared with three existing research methods,it is found that the weighted sum of total delay and total energy consumption of task processing is reduced by 30.2%,37.8%and 39.8%,respectively..In the context of edge computing,the thesis jointly optimizes resource allocation and service management techniques in two scenarios.Both scenarios achieve excellent results and show significant reductions in total task delay and total energy consumption.The optimization results show that the resource allocation and service management techniques can significantly improve server utilization rates and promote load balancing,thereby reducing the overall task processing latency and energy conssumption.