The emerging services of 5G and beyond (5G/B5G) have imposed challenging resiliency demands that make reliable slicing in radio access networks (RAN) an imperative concern. In previous literature, service protection schemes for one single domain are well studied, e.g., lightpath protection for transport domain. However, 5G/B5G will evolve towards a converged network where data transport and function processing coordinate together, thus the single-domain protection is inadequate. In this paper, we are dedicated to investigating a multi-domain protection scheme, which can achieve customized protection for specific reliability demands and reduce backup redundancy. To this end, we propose a topology-level based protection scheme (TLPS) to customize virtual networks (VN) for slices to guarantee reliability while reducing redundancy. The designed VNs are then embedded onto the substrate network with our proposed mixed integer linear programming (MILP) model and heuristic, which aim at minimizing processing and transport resources. We also introduce the multi-service function chain (MSFC) scheme for comparison, which is extended from the existing “Primary + Backup” solution. In the simulation, different cases are numerically evaluated where TLPS outperforms MSFC in terms of processing and transport resource consumption. Our analyses can provide insights into achieving customized protection for RAN slicing in 5G and beyond.

Super-resolution is a promising solution to improve the quality of experience (QoE) for cloud-based video streaming when the network resources between clients and the cloud vendors become scarce. Specifically, the received video can be enhanced with a trained super-resolution model running on the client-side. However, all the existing solutions ignore the content-induced performance variability of Super-Resolution Deep Neural Network (SR-DNN) models, which means the same super-resolution models have different enhancement effects on the different parts of videos because of video content variation. That leads to unreasonable bitrate selection, resulting in low video QoE, e.g., low bitrate, rebuffering, or video quality jitters. Thus, in this paper, we propose SR-ABR, a super-resolution integrated adaptive bitrate (ABR) algorithm, which considers the content-induced performance variability of SR-DNNs into the bitrate decision process. Due to complex network conditions and video content, SR-ABR adopts deep reinforcement learning (DRL) to select future bitrate for adapting to a wide range of environments. Moreover, to utilize the content-induced performance variability of SR-DNNs efficiently, we first define the performance variability of SR-DNNs over different video content, and then use a 2D convolution kernel to distill the features of the performance variability of the SR-DNNs to a short future video segment (several chunks) as part of the inputs. We compare SR-ABR with the related state-of-the-art works using trace-driven simulation under various real-world traces. The experiments show that SR-ABR outperforms the best state-of-the-art work NAS with the gain in average QoE of 4.3%–46.2% and 18.9%–42.1% under FCC and 3G/HSDPA network traces, respectively.

The modern smart grid is a vital component of national development and is a complex coupled network composed of power and communication networks. The faults or attacks of either network may cause the performance of a power grid to decline or result in a large-scale power outage, leading to significant economic losses. To assess the impact of grid faults or attacks, hardware-in-the-loop (HIL) simulation tools that integrate real grid networks and software virtual networks (SVNs) are used. However, scheduling faults and modifying model parameters using most existing simulators can be challenging, and traditional HIL interfaces only support a single device. To address these limitations, we designed and implemented a grid co-simulation platform that could dynamically simulate grid faults and evaluate grid sub-nets. This platform used RTDS and EXata as power and communication simulators, respectively, integrated using a protocol conversion module to synchronize and convert protocol formats. Additionally, the platform had a programmable fault configuration interface (PFCI) to modify model parameters and a real sub-net access interface (RSAI) to access physical grid devices or sub-nets in the SVN, improving simulation accuracy. We also conducted several tests to demonstrate the effectiveness of the proposed platform.

Erasure coding is widely used in distributed storage systems (DSSs) to efficiently achieve fault tolerance. However, when the original data need to be updated, erasure coding must update every encoded block, resulting in long update time and high bandwidth consumption. Exiting solutions are mainly focused on coding schemes to minimize the size of transmitted update information, while ignoring more efficient utilization of bandwidth among update racks. In this paper, we propose a parallel Cross-rack Pipelining Update scheme (CPU), which divides the update information into small-size units and transmits these units in parallel along with an update pipeline path among multiple racks. The performance of CPU is mainly determined by slice size and update path. We formulate the proposed pipelining update scheme as an optimization problem, based on a new theoretical pipelining update model. We prove the optimization problem is NP-hard and develop a heuristic algorithm to solve it based on the features of practical DSSs and our implementations, including Big chunk and Small overhead. We implement CPU and conduct experiments on Amazon EC2 under a variety of scenarios. The results show that CPU can reduce the average update time by 48.2%, compared with the state-of-the-art update schemes.

Flexible Ethernet (FlexE) performs as an advanced technology in metro networks, offering bandwidth sharing, high resilience, and resource isolation advantages. Featured as the new brand of FlexE, fine granularity unit (FGU) has been recently proposed, where the standard 5Gbps timeslot is further divided into 480 sub-ones for sub-1G service accommodation. However, this reduction in granularity will raise new issues on end-to-end (EtE) latency and jitter performance, which are crucial for emerging time-critical services, such as remote industrial control and virtual reality. With FGU, service data may experience extended wait time at forwarding sites due to the misalignment between data arrival and sub-timeslot readiness, thereby increasing the risk of violating latency and jitter requirements. Therefore, the FGU-enabled network cannot completely inherit the timeslot assignment scheme in traditional FlexE of solely bandwidth considerations. To address this issue, we propose a customized optical-electrical routing and sub-timeslot assignment scheme (CRTAS) for FGU, where latency and jitter requirements as well as service characteristics are considered. This scheme aims at minimizing the wait latency of sub-timeslot mapping and crossover in forwarding sites along the EtE path. Additionally, we also introduce the traditional sub-timeslot assignment scheme (TTAS) for comparison. Cases of virtual reality and industrial control are modelled and numerically evaluated, where CRTAS outperforms TTAS in terms of EtE latency and jitter guarantee. Our analyses can offer valuable insights for developing quality-of-service (QoS) committed transport solutions in future metro converged optical-electrical networks.