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.
Other works, such as @cite_7 , 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 @cite_9 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.
@cite_9 @cite_7
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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. 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. High latency, network congestion and network bottleneck are some of problems in cloud computing. Moving from centralized to decentralized paradigm, Edge computing could offload the processing to the edge which indirectly reduces application response time and improves overall user experience. This paper evaluate Docker, a container based technology as a platform for Edge Computing. 4 fundamental criteria were evaluated 1) deployment and termination, 2) resource & service management, 3) fault tolerance and 4) caching. Based on our evaluation and experiment Docker provides fast deployment, small footprint and good performance which make it potentially a viable Edge Computing platform. In multi-tenant datacenters, jobs of different tenants compete for the shared datacenter network and can suffer poor performance and high cost from varying, unpredictable network performance. Recently, several virtual network abstractions have been proposed to provide explicit APIs for tenant jobs to specify and reserve virtual clusters (VC) with both explicit VMs and required network bandwidth between the VMs. However, all of the existing proposals reserve a fixed bandwidth throughout the entire execution of a job. In the paper, we first profile the traffic patterns of several popular cloud applications, and find that they generate substantial traffic during only 30 -60 of the entire execution, suggesting existing simple VC models waste precious networking resources. We then propose a fine-grained virtual network abstraction, Time-Interleaved Virtual Clusters (TIVC), that models the time-varying nature of the networking requirement of cloud applications. To demonstrate the effectiveness of TIVC, we develop Proteus, a system that implements the new abstraction. Using large-scale simulations of cloud application workloads and prototype implementation running actual cloud applications, we show the new abstraction significantly increases the utilization of the entire datacenter and reduces the cost to the tenants, compared to previous fixed-bandwidth abstractions.