# An Approach for Deployment of Service-Oriented Simulation Run-Time Resources
## 1 Introduction  
Training simulation is a safe, controllable, and cost-effective training method that can improve the abilities of trainees, becoming a critical method of talent cultivation in various industries. In large-scale geo-distributed training simulations, simulation members, i.e., users, can be categorized into three types [1]: live (L), virtual (V), and constructive (C) members, such as actual aircraft with various sensors, aircraft simulators controlled by simulated cockpits and computer generated aircraft, respectively. These members dispersed across different locations are integrated into a virtual space, where they can see and interact with each other. However, systems capable of supporting such large-scale simulation activities are often excessively large and require sufficient computing resources and network resources to support their operation. Otherwise, users will suffer from tremendous latency, which can significantly impair user experience quality. To address this issue, researchers have proposed deploying simulation systems in the cloud-edge collaborative computing environment, utilizing both the computing resources of cloud computing and the networking resources of edge computing [2]. Under the cloud-edge collaborative simulation paradigm, developers usually adopt the micro-service (MS)architecture to develop simulation systems. By encapsulating common functional modules into containers to form services, MS architecture can achieve higher development efficiency, more efficient resource reuse, and better elasticity [3, 4]. During the operation of an MS-based training simulation system deployed in cloud-edge collaborative computing architecture, simulation members send service requests to the system to realize the functions they need. They also send messages to other members through the middleware service to achieve the interaction between him/her and other members, and also promote the progress of the current simulation activity. As shown in Figure 1, each service request is composed of multiple micro-services and can be formulated as a directed acyclic graph (DAG) [5, 6], in which a node represents a task, defined as the execution of its corresponding MS instance. Each task also has its predecessor and successor tasks. After a task is completed, its successor tasks will either be completed on the same server or on other servers. The latter method will incur service communication time, a part of data transmission time. Furthermore, MS instances provide computation in a queue-based manner, which may lead to long service waiting times due to huge user demands and a small number of instances. Furthermore, in large-scale geo-distributed training simulations, message sending may lead to long-distance data transmission, generating user interaction time. For example, in a training simulation activity, if user A needs to send a message to user B, the message needs to be sent to the middleware service first, and then the middleware will forward it to user B. To implement the training simulation process mentioned above, the primary challenge is how to deploy edge servers, micro-services, and members in the cloud-edge collaborative computing environment before the start of the simulation. On the one hand, edge servers can provide both computing and communication capabilities, making them an important infrastructure for cloud-edge collaborative computing architecture. In reality, there often exist situations where there are \(n\) available edge servers but \(m\) (\(n<m\)) candidate server locations, typically base stations (BSs) [7, 8, 9, 10, 11]. Therefore, we need to determine where these \(n\) edge servers should be deployed. We assume that the cloud center has existed, so we do not focus on the deployment of it. On the other hand, simulation members are the main participants of training simulations, and micro-services are indispensable components for enabling members to perform their required functions, so they are also  
Figure 1: System Architecture. extremely important for training simulations.