You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Mobile Edge Computing (MEC) is an emerging network paradigm that provides cloud and IT services at the point of access of the network. Such proximity to the end user translates into ultra-low latency and high bandwidth, while, at the same time, it alleviates traffic congestion in the network core. Due to the need to run servers on edge nodes (e.g., an LTE-A macro eNodeB), a key element of MEC architectures is to ensure server portability and low overhead. A possible tool that can be used for this purpose is Docker, a framework that allows easy, fast deployment of Linux containers. This paper addresses the suitability of Docker in MEC scenar- ios by quantifying the CPU consumed by Docker when running two different containerized services: multiplayer gam- ing and video streaming. Our tests, run with varying numbers of clients and servers, yield different results for the two case studies: for the gaming service, the overhead logged by Docker increases only with the number of servers; con- versely, for the video streaming case, the overhead is not affected by the number of either clients or servers.

Cited Abstract(s):
[1]: Docker, the most popular container technique today, has accepted by more and more people and companies. It achieves agility and controls for Development and IT Operations teams to build, ship, and run any app, anywhere. Taking Docker's advantages, enterprises are able to leverage big data analytics more efficiently. In this paper, we use two network modes in Docker when building one Spark based streaming application. To get better achievement we design one experimental system to investigate performance between host and overlay network mode from different aspects. Through our working, we find host mode is faster overlay mode around 8 . At the same time, the overlay mode is more stable. We introduce coefficient of variation to see the difference of transfer latency among every package and overlay mode is better than host mode more than 50 . Therefore, overlay network mode is more suitable for the business with high quality requirement of network jitter like video conference. On the other hand, when need to get higher transfer latency, host mode is better choice.

[2]: In cloud gaming the game is rendered on a distant cloud server and the resulting video stream is sent back to the user who controls the game via a thin client. The high resource usage of cloud gaming servers is a challenge. Expensive hardware including GPUs have to be efficiently shared among multiple simultaneous users. The cloud servers use virtualization techniques to isolate users and share resources among dedicated servers. The traditional virtualization techniques can however inflict notable performance overhead limiting the user count for a single server. Operating-system-level virtualization instances known as containers are an emerging trend in cloud computing. Containers don't need to virtualize the entire operating system still providing most of the benefits of virtualization. In this paper, we evaluate the container-based alternative to traditional virtualization in cloud gaming systems through extensive experiments. We also discuss the differences needed in system implementation using the container approach and identify the existing limitations.
Other works, such as [2] , focus on the advantages of Docker containers rather than VMs, in the deployment of cloud gaming, i.e., architectures where the game is rendered on a distant cloud server and the resulting video stream is sent back to the user. We underline that in our work, instead, a dockerized multiplayer client-server architecture is analyzed. The authors of [1] investigate Docker in overlay-network mode and in host-network mode for the provisioning of a large-scale streaming system, finding that the advantages in terms of latency in host mode are balanced by the higher stability of an overlay network. Although related to their study, our work focuses on characterizing the overhead cost of the containerized solution for different applications.