# An Approach for Deployment of Service-Oriented Simulation Run-Time Resources
## 1 Introduction
As software resources, we need to determine their deployment schemes on servers. Note that the referred members here are constructive members, because in large-scale geo-distributed simulations, live members and virtual members are often difficult to move due to their large numbers, so we assume that their locations are known and fixed. For convenience in the description, we define servers, micro-services, and constructive members as simulation run-time resources (SRR), and refer to the latter two software resources as simulation run-time software resources (SRSR). Different deployment schemes of SRR have a great impact on the time cost of simulation systems, including data transmission time and service queuing time, as well as the resource occupancy rate of edge servers. Data transmission is caused by users' service requests and interactions between users. Without the loss of generality, it is usually assumed that a task is always scheduled to be processed by the server with the corresponding MS instance that is closest to its input data [11; 12]. Obviously, the same server will face different user demands under different deployment schemes of edge servers and simulation members. Deployment decisions of micro-services have a significant influence on task scheduling and service queuing time. In addition, scattered users will lead to large data transmission time due to their messages. On the other hand, although more micro-service instances can reduce time costs, they can increase resource usage of edge servers. This increases the risk of crashes and should be avoided as much as possible. Therefore [13], deployment schemes of SRR are closely coupled and should be considered together. However, there is currently little research on this problem. Existing works mostly study one problem in service deployment or server deployment. As mentioned earlier, they are coupled and it is difficult to achieve the overall optimum by researching them separately. In addition, most works only consider the deployment of monolithic services, instead of micro-services, thus ignoring the data dependencies between micro-services [11; 14]. However, this deployment scheme cannot fully capture the characteristics of systems based on MS architecture. Finally, the current related research focuses mainly on scenarios such as autonomous driving and the Internet of Things (IoT), rather than training simulation. However, there are some elements in training simulation that these fields do not possess, such as L, V, and C members and their interactions. This work studies the joint deployment of SRR in cloud-edge collaborative computing environments, aiming to minimize system time cost, while minimizing the resource consumption rate of edge servers. We formulate the joint deployment problem and design a learnable multiple-objective evolutionary algorithm to solve it. Furthermore, we target the service-oriented training simulation field, a more specialized scenario. In this paper, the main contributions are as follows:  
1. We propose a complete process for deploying edge servers and software resources together, taking a more complex service modeling approach into account. This is an improvement over existing work. 2. We formulate the joint deployment of edge servers and SRSR as a multi-objective optimization problem with two objectives: minimizing both the system's time cost and the resource usage rate of edge servers. To solve this problem, we proposed a learnable genetic algorithm for simulation run-time resource deployment (LGASRD), which is capable of adaptively selecting genetic operators and evolving under the guidance of an elite population. 3. Extensive experiments are implemented in Python to evaluate the performance of the LGASRD based on the real-world EUA data set [15] and scientific workflows [16]. The experimental results show that LGASRD outperforms the other typical benchmark policies. The remainder of this article is organized as follows. Section 2 provides an overview of related works.