# Game-Theory-Based Task Offloading and Resource Scheduling in Cloud-Edge Collaborative Systems
## 1 Introduction
[8] considered the service cache and D2D communication models, and they introduced opportunity networks in a multi-access network; integrated delay and energy consumption as the overall computational overhead; and designed a sequence-game-based suboptimal algorithm to solve the problem. Most of the existing research on MEC computation offloading only considers task offloading and resource scheduling between mobile devices [9] or between mobile devices and MEC servers [10] without considering the computational resources and processing capabilities of remote cloud servers, and the Cloud-Edge collaboration capability is neglected. With cloud providers aggressively expanding their data centers, cloud resources can be leveraged by deploying high-speed fiber networks. [PERSON] et al. [11] designed a decentralized computational offloading algorithm to solve the policy optimization problem in the case where users do not disclose their personal information. The problem is described as an observable Markovian decision process, which is solved by a policy-gradient-based deep reinforcement learning (DRL) approach. [PERSON] et al. [12] considered indistinguishable and delay-sensitive tasks and edge load dynamics to formulate the task offloading problem and proposed a distributed algorithm based on model-free deep reinforcement learning. However, none of them considered the computational resources and capacity of cloud servers. Based on the above problems, this paper studies the Cloud-Edge collaborative system, which is motivated by minimizing the delay, energy consumption and computing cost, and it constitutes the task offloading and resource allocation problem for multiple end users to maximize system utility. The contributions of this paper are summarized as follows:  
1. To address the utility maximization problem, this paper proposes a joint resource allocation and task offloading scheme based on game theory for Cloud-Edge collaboration, including computational resource allocation and task offloading strategy optimization. 2. The joint task offloading and resource allocation problem is described as mixed-integer nonlinear programming that combines task offloading decisions and resource allocation for offloading users to maximize system utility. 3. For the joint task offloading and resource allocation problem, an improved particle swarm optimization algorithm based on game theory is proposed to obtain the task offloading strategy, which achieves the Nash equilibrium of the multi-user computational offloading game. Other resource allocations and computational offloading schemes are used as comparison schemes for the GTPSO algorithm, and simulation experiments are conducted under different parameters. The results show that the proposed offloading scheme in this paper significantly improves the offloading utility of users. The rest of this paper is organized as follows: Section 2 reviews related work, Section 3 describes the problem, Section 4 presents the computational offloading resource allocation scheme, Section 5 proposes a multi-user computational offloading game and designs a computational offloading algorithm, Section 6 evaluates the performance of the algorithm and Section 7 concludes the work of this paper.