Content Caching And Computation Offloading Mechanism For Smart Maintenance Of Communication Network

Various problems arise in the on-site maintenance of communication networks.such as insufficient work experience of on-site maintenance personnel and lack of timely guidance,heavy on-site maintenance workload,low maintenance efficiency,and Devices used for maintenance are limited by resources,including computing resources and battery life.Moreover,with the development of artificial intelligence,big data technology,and the Internet,the maintenance system based on the traditional cloud computing-based centralized processing model can no longer meet the requirements.For example,on-site maintenance personnel send massive content requests to the core network,resulting in slower information flow or even network breakdown.The long-distance communication between on-site maintenance personnel and the core network causes the backhaul delay and energy consumption to increase continuously.In response to these problems,this paper proposes a communication network edge maintenance architecture based on smart wearable technology and introduces content caching and computing offloading technology.At present,there have been many studies on content caching mechanism and computation offloading mechanism,but there are still some limitations.Most of the existing researches only consider a single factor in the cache decision and offload decision,such as minimizing delay or energy consumption.However,in the on-site maintenance work of the communication network,there are sudden tasks that are sensitive to delay and routine tasks that require a large amount of energy consumption.In terms of content caching,many studies have not considered the cloud-side-to-end collaborative caching of content,communication between users via D2D(Device-to-Device),and adaptability to the movement of on-site maintenance personnel.In terms of computing offloading,most of the work have not propose an effective task segmentation strategy,have not solve the user's hunger problem,and have not consider the dynamic scene and dynamic sensing efficiency,and could not improve the efficiency of on-site maintenance work.Aiming at the problems of on-site maintenance of communication networks and the deficiencies of existing research work,this paper proposes a content caching and computation offloading mechanism for intelligent maintenance of communication networks.Specifically:(1)Research on content collaborative caching mechanism based on joint decision of download delay and energy consumption.First of all,we integrate network coding and content caching technology,and deploy content in the form of coding close to network edge to improve the efficiency of content distribution,reduce the redundant transmission of content,and improve the quality of terminal user experience.Then,we discuss how to build a suitable model to design an efficient caching strategy.From the user's point of view,we consider the user's mobility and establish an average delay model for users to download content.From the perspective of content caching and distribution,we have established an energy consumption model including cache energy consumption,D2D communication energy consumption,cooperative transmission energy consumption,and backhaul transmission energy consumption.We consider that there are multiple types in the on-site maintenance scenarios of communication networks,including delay-sensitive and energy-consuming types.We use download delay and energy consumption as evaluation indicators to establish a QoS(Quality of Service)model.Finally,with the goal of maximizing the QoS problem,a new and efficient content collaborative caching strategy between MEC(Mobile Edge Computing)servers and terminal device users is designed.We propose a ?-hybrid Q-Learning algorithm to optimize the cache file placement scheme,and make the cache action selection based on the combination of an improved heuristic greedy algorithm and a simulated annealing algorithm.In this way,the complexity of the algorithm is reduced,the performance is improved,and the state changes of the system are sensed.The experimental results show that the caching strategy proposed in this paper can improve the hit rate of content,reduce the total delay-energy,thereby improving the quality and efficiency of on-site maintenance work in the communication network.(2)Research on computation offloading mechanism based on joint decision of transmission delay and energy consumption.Before offloading,we propose a multi-merged computing sorting algorithm to divide a part of the task to offload.When making an offloading decision,we access a suitable MEC service node for each user with the lowest transmission cost and establish a related model.We use an improved KM(Kuhn Munkras)algorithm that considers fairness among users to solve this model.After that,we propose a dynamic energy-efficiency awareness strategy.When tasks are processed locally,we optimize the CPU(Central Processing Unit)clock frequency.When tasks are offloaded,we adaptively allocate the transmission power.Finally,we conduct a simulation experiment.The results demonstrate that the proposed scheme can reduce the transmission cost and improve the performance,thereby increasing the level of on-site maintenance work.