5G networks were designed to provide sufficient throughput and reliable data communication services. To achieve the targeted QoS requirements of the 5G specifications, the use of mmWaves are essential. However, mmWavs are very vulnerable to signal blockages due their high frequency. Multi-path transmission control protocol (MPTCP) can deal with this problem by transmitting data through multiple subflows using TCP. The current congestion control scheme of MPTCP was designed for wired networks and is not suitable for use in wireless networks. In this paper, a new MPTCP congestion control algorithm named round trip time (RTT) estimation based Veno (REVeno) is proposed to better support wireless networks by distinguishing between loss due to congestion and wireless channel errors. The proposed REVeno scheme uses a novel backlog estimation formula that considers the total buffer size and parameters that distinguish loss due to congestion and loss due to the wireless channel. The REVeno scheme will use these estimations to minimizes the reordering delay by equalizing the equilibrium RTT of all subflows. The simulation results show that the proposed REVeno scheme performs better than the existing schemes in terms of goodput and latency without adversely affecting other concurrent TCP connections in the same network.

Multipath TCP (MPTCP) is a promising solution that can provide high end-to-end throughput in fifth generation (5G) mobile networks. Many next-generation applications will require high throughput and low latency simultaneously, but the current MPTCP congestion control algorithms cannot reliably satisfy this requirement. In this paper, a novel MPTCP congestion control scheme named delay-equalized FAST (DEFT) is proposed to achieve high throughput and low end-to-end (E2E) delay in 5G networks. First, in order to achieve high throughput, DEFT includes a novel window control algorithm that shows fast responsiveness when the state of the millimeter-wave (mmWave) link changes from line-of-sight (LOS) to non-LOS (NLOS) and vice versa. Second, in order to achieve low E2E delay, DEFT includes a delay-equalizing algorithm which minimizes additional reordering delay in the receive buffer. The performance of DEFT was evaluated based on ns-3 simulation and was compared with wVegas, Balia, and delay-adapted LIA. Simulation results show that DEFT can provide a significant goodput gain and application-level E2E delay reduction for the range of interest.

With the wide spread of various smart devices and the proliferation of internet of things (IoT) sensors, the amount of traffic on mobile networks is rapidly increasing, and applications with extreme requirements are increasing. Network function virtualization (NFV) and mobile edge computing (MEC) are emerging as core technologies to satisfy users' real-time service demands. Adapting NFV technology to MECs allows the ability to assign cloud-computing capabilities near the base stations (BSs) of radio access networks (RANs), resulting in extremely fast service access to user equipment (UE). However, placement of virtualized network functions (VNF) within the edge network need to consider the location and the requirements of the user which change in real-time. There has been almost no consideration in the existing research on VNF resource allocation (VNF-RA) based on these aspects. Therefore, in this paper, a VNF resource allocation scheme based on context-aware grouping (VNF-RACAG) technology is proposed that enables groups (based on the geographic context of users, such as location and velocity) to compute the optimal number of clusters to minimize the end-to-end delay of network services. Then, a graph partitioning algorithm is used to minimize user movement between clusters, optimizing the data rate that users lose due to VNF migration.

Autonomic remote system image management support (e.g., remote system installation and backup) is crucial for the management of clusters in large scale grid computing environments in which clusters at different locations are integrated into a single large sale computing system. We propose a Grid-based remote image management environment (called RISE) which is designed to support ease of use, high performance installation, and resiliency against failure and configuration changes. Major features of the RISE system are: (1) remote system image installation, backup and recovery via a web-based simple and intuitive user interface, (2) parallel installation, (3) backup image databases (2) self-configuration (autonomic configuration propagation and recovery protocol), (3) self-healing (autonomic backup image database replication and recovery). The development of the system with no support for grid environments has been completed and its commercial version by Samsung Electronics has been available since the June of 2006. The development of the grid support version is under-way.