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.
Containerization systems for service delivery in 5G networks is a widely studied topic. In particular, works such as @cite_8 evaluate Docker as a platform for Edge Computing concluding that it provides fast deployment, small footprint and good performance. Other studies, e.g., @cite_3 , claim that distributed service delivery will be one of the hottest topics in 5G networks, and demonstrate the efficiency of Docker as containerization system in the implementation of Virtual Network Functions. Furthermore, Linux containers have been widely analyzed and compared against Virtual Machines under multiple aspects: @cite_4 explores the performance of traditional VM deployments, and compares this approach to the use of Linux containers. In line with our results, @cite_4 also demonstrates that containers lead to equal or better performance than VMs in all cases.
@cite_4 @cite_3 @cite_8
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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. Many ongoing research activities relevant to 5G mobile systems concern the virtualization of the mobile core network, including the evolved packet core (EPC) elements, aiming for system scalability, elasticity, flexibility, and cost-efficiency. Virtual EPC (vEPC) 5G core will principally rely on some key technologies, such as network function virtualization, software defined networking, and cloud computing, enabling the concept of mobile carrier cloud. The key idea beneath this concept, also known as core network as a service, consists in deploying virtual instances (i.e., virtual machines or containers) of key core network functions [i.e., virtual network functions (VNF) of 4G or 5G], such as the mobility management entity (MME), Serving GateWay (SGW), Packet Data network gateWay (PGW), access and mobility management function (AMF), session management function (SMF), authentication server function (AUSF), and user plane functions, over a federated cloud. In this vein, an efficient VNF placement algorithm is highly needed to sustain the quality of service (QoS) while reducing the deployment cost. Our contribution in this paper is twofold. First, we devise an algorithm that derives the optimal number of virtual instances of 4G (MME, SGW, and PGW) or 5G (AMF, SMF, and AUSF) core network elements to meet the requirements of a specific mobile traffic. Second, we propose an algorithm for the placement of these virtual instances over a federated cloud. While the first algorithm is based on mixed integer linear programming, the second is based on coalition formation game, wherein the aim is to build coalitions of cloud networks to host the virtual instances of the vEPC 5G core elements. The obtained results clearly indicate the advantages of the proposed algorithms in ensuring QoS given a fixed cost for vEPC 5G core deployment, while maximizing the profits of cloud operators. Current trends in virtualization, green computing, and cloud computing require ever increasing efficiency in consolidating virtual machines without degrading quality of service. In this work, we consider consolidating virtual machines on the minimum number of physical containers (e.g., hosts or racks) in a cloud where the physical network (e.g., network interface or top of the rack switch link) may become a bottleneck. Since virtual machines do not simultaneously use maximum of their nominal bandwidth, the capacity of the physical container can be multiplexed. We assume that each virtual machine has a probabilistic guarantee on realizing its bandwidth Requirements-as derived from its Service Level Agreement with the cloud provider. Therefore, the problem of consolidating virtual machines on the minimum number of physical containers, while preserving these bandwidth allocation guarantees, can be modeled as a Stochastic Bin Packing (SBP) problem, where each virtual machine's bandwidth demand is treated as a random variable. We consider both offline and online versions of SBP. Under the assumption that the virtual machines' bandwidth consumption obeys normal distribution, we show a 2-approximation algorithm for the offline version and improve the previously reported results by presenting a (2 +∈)-competitive algorithm for the online version. We also observe that a dual polynomial-time approximation scheme (PTAS) for SBP can be obtained via reduction to the two-dimensional vector bin packing problem. Finally, we perform a thorough performance evaluation study using both synthetic and real data to evaluate the behavior of our proposed algorithms, showing their practical applicability. High Performance Computing (HPC) applications require systems with environments for maximum use of limited resources to facilitate efficient computations. However, these systems are faced with a large trade-off between efficient resource allocation and minimum execution times for the applications executed on them. Also, deploying applications in newer environments is exacting. To alleviate this challenge, container-based systems are recently being deployed to reduce the trade-off. In this paper, we investigate container-based technology as an efficient virtualization technology for running high performance scientific applications. We select Docker as the container-based technology for our test bed. We execute autodock3, a molecular modeling simulation software mostly used for Protein-ligand docking, in Docker containers and VMs created using OpenStack. We compare the execution times of the docking process in both Docker containers and in VMs.