# Efficient Dynamic Deployment of Simulation Tasks in Collaborative Cloud and Edge Environments
## 1 Introduction  
By incorporating grid computing, virtualization technology, and the idea of servitization, cloud computing, as a new computational paradigm, has been widely studied and applied in many fields since its emergence. The most influential definition of cloud computing is given by National Institute of Standards and Technology (NIST) [1], who firstly proposed three service models: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS), and four deployment patterns, namely public cloud, private cloud, community cloud, and hybrid cloud [2]. The main advantages of cloud computing can be summarized as serve-as-you-need, widely network access, elastic scaling, and efficient management. Computer simulation, as a kind of computer application, has been used widely, such as education, entertainment, medical care, and military field. However, with the deepening and extension of its application, simulation also confronts many new issues [3]. On the one hand, the increasing number of simulation entities and the continuously refined model functions call for more computing resources. Simulation users need to pay a great amount of money to buy, operate, and manage these high-performance servers. On the other hand, the heterogeneous simulation models, systems, and other simulation resources developed by different institutes are isolated with each other, which may result in repeated development and waste of resources. Considering the issues associated with simulation and the advantages of cloud computing, a new research area called cloud-based simulation or cloud simulation comes into being [3; 4; 5]. [PERSON] et al. [3] summarized the benefits of combining simulation and cloud technology from four aspects: (1) reducing the usage cost of simulation; (2) simplifying the usage of simulation; (3) helping the operation and management of simulation resources; (4) improving the capacity of fault tolerance. Although cloud computing can bring many benefits to simulations, some limitations also exist. For example, cloud data centers are usually located far away from users, which may incur long data transmission delay. As some simulation applications, especially those involved with real human or equipment, are sensitive to the time delay, edge computing [6] is introduced as a compliment for cloud computing. Unlike the relatively independent tasks submitted by common users, simulation tasks tend to be tightly coupled. At the same time, they are usually highly heterogeneous and both communication-sensitive and computation-sensitive [7]. To achieve the high performance of executing simulations in the cloud, we need to assign all the tasks to proper hosts in a reasonable time span. In this paper, we mainly focus on the efficient task scheduling of simulation tasks in collaborative cloud and edge environments. Considering the frequent changes of the status of tasks and hosts, it is actually an NP-hard dynamic bin-packing problem. Many scholars have conducted in-depth research in this field and put forward many useful solutions. To achieve the fast responses to users' requests, task schedulers, which have been seen as the core modules in modern clusters, are needed to assign the submitted tasks to hosts efficiently. From the perspective of the working mechanism, they can be classified as queue-based [8; 9; 10] and batching-based schedulers [11; 12; 13]. In queue-based schedulers, tasks are assigned sequentially according to some predefined rules. To cope with a great number of tasks, these queue-based schedulers can be further divided as centralized, distributed, and hybrid designs. As tasks are processed independently, this kind of schedulers can be easily implemented in a parallel or distributed manner and can be deployed on-line without the need of a priori knowledge about tasks. On the contrary, batching-based schedulers try to deploy tasks jointly aimed at finding the global optimal assignment decision.