## 1 Introduction
Although the MEC network paradigm can overcome some of the drawbacks of cloud computing and meet the service constraints of real-time applications, there are still many challenges. Specifically, from the perspective of data sharing, in the current edge computing domain, the most well-known approach is based on blockchain [7] for distributed data storage and synchronized data sharing. However, this approach faces serious performance issues for high-throughput applications due to the low performance of blockchain networks, high costs associated with storing block data, and scalability challenges posed by large-scale data synchronization. These issues present difficulties in scheduling real-time applications. To address these issues, our work focuses on leveraging performance optimization methods in MEC networks to provide real-time services for MUs. The main contributions of this study can be summarized as follows:  
Figure 1: The computation and processing workflow of online real-time services. * The thesis proposes a Distributed Edge Service Caching and Offloading (DESCO) architecture based on edge servers' collaboration to provide real-time services for mobile users. Within DESCO, the optimization problem of minimizing the long-term average energy consumption of users while satisfying latency constraints is established, for which cache sharing, replacement, and computation offloading methods are leveraged to further optimize user costs. * A decentralized consistent hashing-based cache-sharing mechanism named \"Cache Chord\" is designed. This mechanism leverages the communication backhaul links at the edge layer and expands the DESCO framework by allowing edge servers to self-organize into a circular logical topology, facilitating application data sharing. Additionally, a cache IP list mechanism is devised to link application resource key-values in the overlay network with actual data in the real network. * The real-time computation offloading (RCO) problem is transformed into a multi-player static game among MUs within the wireless network coverage of each server in order to reduce the current time slot energy consumption. A multi-dimensional discrete particle swarm optimization algorithm is applied to solve this problem after proving the existence of the Nash equilibrium (NE) solution for the game. Furthermore, the exploration coefficients and iteration rules of the algorithm are designed to meet environment constraints. * Finally, simulation experiments are conducted to evaluate the performance of the proposed framework and algorithms. Results demonstrate that the proposed offloading method effectively reduces overall energy consumption at the user layer and obtains better converges compared to baseline algorithms. The remainder of the paper is organized as follows: Section 2 introduces the related work of computation offloading and cache-sharing technology. Section 3 describes the system model of DESCO, and further introduces the cache-sharing mechanism. In Section 4, we transfer the RCO problem into a static game and proof the existence of NE solutions, then design the MDPSO algorithm. Section 5 presents simulation experiments to explore the superiority of the MDPSO algorithm and Cache Chord. Finally, the conclusion is given in Section 6.