Process Pool Research Articles

Security-aware 5G RAN slice mapping with tiered isolation in physical-layer secured metro-aggregation elastic optical networks using heuristic-assisted DRL

The optical transport network (OTN) encryption technology is attractive to solve the physical-layer security in services for the light-path provision process. This paper mainly explores the security-aware 5G radio access network (RAN) slice mapping problem with the tiered isolation (TI) policy, which decides the solution for aggregating service into the physical-layer secured metro-aggregation elastic optical networks (MA-EONs). We first introduce the physical-layer secured OTNs and illustrate their differences from the traditional optical networks. Then, we formulate the 5G RAN slice mapping problem in physical-layer secured MA-EONs as an exact integer linear programming (ILP) model to minimize the average cost (AC), which consists of the number of utilized processing pools (PPs)/general-purpose processors (GPPs)/virtual machines (VMs), and maximum frequency slot index (MFSI) on the light-paths, meanwhile satisfying the given slice’s latency, isolation, and security requirements. After that, to overcome the non-scalability problem of the ILP model, a heuristic-assisted deep reinforcement learning (HA-DRL) algorithm is proposed to obtain a near-optimal solution for large-scale network scenarios, where the classical shortest path algorithm is employed in the DRL to shrink the size of the exploration space and accelerate the convergence process. Finally, we evaluate the proposed ILP model and HA-DRL algorithm through extensive simulations. Simulation results indicate that our proposed HA-DRL method can find approximate solutions to the ILP model in the small-scale network scenario. Furthermore, the HA-DRL method can also achieve higher resource efficiency compared with benchmark heuristic first-fit algorithms in the large-scale network scenario. In comparison to the first-fit algorithm benchmark, the proposed HA-DRL can achieve up to 9.4% AC reduction in large-scale network scenarios.

Resource-efficient and QoS guaranteed 5G RAN slice migration in elastic metro aggregation networks using heuristic-assisted deep reinforcement learning

To cope with the growing and diversifying 5G services, RAN slicing, an effective resource allocation mechanism, has been proposed. Each RAN slice serves varied service requirements, with baseband processing functions (BPFs), e.g., distributed units (DUs) and centralized units (CUs), implemented via virtual machines in a processing pool (PP). Co-locating the virtualized DU/CU (vDU/vCU) of multiple slices in a single PP enhances resource utilization and reduces power consumption. As mobile traffic and slice resource demands fluctuate over time, we face a trade-off: either migrate RAN slices to improve resource efficiency or avoid migration to prevent user service interruption, thereby ensuring users’ QoS. Additionally, an elastic optical network (EON) is employed as the substrate metro aggregation network for flexible and spectrum-efficient scheduling. In this context, the routing and spectrum allocation of optical paths connecting different BPFs should also be optimized to maximize spectral resource usage. To address the above RAN slice deployment and migration issue, in this paper, we propose a heuristic-assisted deep reinforcement learning (HA-DRL)-based algorithm to jointly optimize power consumption, slice migration, and spectrum resource consumption. Two heuristic algorithms, RAN slice reallocation (RSR) and RAN slice adjustment (RSA), are proposed. Using their results as a reference, the HA-DRL achieves a better trade-off among the triple optimization objectives. Simulations on a small-scale 9-node network and a large-scale 30-node network demonstrate the superiority of HA-DRL over baseline heuristic algorithms. We achieved significant reductions in migrated traffic and spectral resource saving at a minor power consumption cost.

Cost-Aware DU Placement and Flow Routing in 5G Packet xHaul Networks

Packet-switched xHaul networks based on Ethernet technology are considered a promising solution for assuring convergent, cost-effective transport of diverse radio data traffic flows in dense 5G radio access networks (RANs). A challenging optimization problem in such networks is the placement of distributed processing units (DUs), which realize a subset of virtualized baseband processing functions on general-purpose processors at selected processing pool (PP) facilities. The DU placement involves the problem of routing of related fronthaul and midhaul data flows between network nodes. In this work, we focus on developing optimization methods for joint placement of DUs and routing of flows with the goal to minimize the overall cost of PPs activation and processing in the network, which we refer to as the PPC-DUP-FR problem. We account for limited processing and transmission resources as well as for stringent latency requirements of data flows in 5G RAN. The latency constraint makes the problem particularly difficult in a packet-switched xHaul network since it involves the non-linear and dynamic estimation of the latencies caused by buffering of packets in the switches. The latency model that we apply in this work is based on worst-case calculations with improved latency estimations that skip from processing the co-routed, but non-affecting flows. We use a mixed-integer programming (MIP) approach to formulate and solve the PPC-DUP-FR optimization problem. Moreover, we develop a heuristic method that provides optimized solutions to larger PPC-DUP-FR problem instances, which are too complex for the MIP method. Numerical experiments performed in different network scenarios indicate on the effectiveness of the heuristic in solving the PPC-DUP-FR problem. In particular, the heuristic achieves up to 63% better results than MIP (at the MIP optimality gap equal to 76%) in a medium-size mesh network, in which the MIP problem is unsolvable for higher traffic demands within reasonable runtime limits. In larger networks, MIP is able to provide some results only for the PPC-DUP-FR problem instances with very low traffic demands, whereas the solutions generated by the heuristic are at least 83% better than the ones achieved with MIP. Also, the analysis performed shows a significant impact of the PP cost factors considered and of the level of cost differentiation of PP nodes on the overall PP cost in the network. Finally, simulation results of a case-study packet xHaul network confirm the correctness of the latency model used.

Energy-Efficient DU-CU Deployment and Lightpath Provisioning for Service-Oriented 5G Metro Access/Aggregation Networks

To accommodate ever-growing mobile data and heterogeneous 5G services, considerable challenges have emerged to stimulate new design principles on radio access networks (RAN). Advanced functional splits are then introduced to divide baseband unit (BBU) into two new entities, i.e., distributed unit (DU) and central unit (CU), that DU-CU can be co-located or separated into different processing pools (PP) honoring service requirements. Then, the optical transport network evolves towards a more flexible architecture to support agile DU-CU deployment (i.e., alterable DU-CU locations). However, this agile DU-CU deployment without an effective management strategy may lead to the activation of many unnecessary PPs and lightpaths in the network infrastructure that results in low energy efficiency. Moreover, this problem will be continuously amplified with network scale expansion, which becomes one of the operator's major concerns for OPEX reduction. Thus, in this paper, we are dedicated to solving this problem through turning network devices from “always on” to “on-demand on” pattern to support energy-efficient DU-CU deployment and lightpath provisioning. To this end, we propose a mixed-integer linear programming (MILP) model and auxiliary graph-based heuristic to minimize the power consumption of reconfigurable add/drop multiplexer, Ethernet switch, optical transponder, PP, and DU-CU processor. We analyze the power consumption under different 5G services (i.e., eMBB, URLLC, and mMTC) from a latency perspective in both small-scale and large-scale networks. Moreover, our strategies can also be applied with adjustable deployment that adapts to time-varying user traffics, which reduces power by over 14% compared to conventional static deployment. Our analyses can help provide insights into the modeling and designing of energy-efficient DU-CU deployment and lightpath provisioning in 5G.

Latency-Aware DU/CU Placement in Convergent Packet-Based 5G Fronthaul Transport Networks

The 5th generation mobile networks (5G) based on virtualized and centralized radio access networks will require cost-effective and flexible solutions for satisfying high-throughput and latency requirements. The next generation fronthaul interface (NGFI) architecture is one of the main candidates to achieve it. In the NGFI architecture, baseband processing is split and performed in radio (RU), distributed (DU), and central (CU) units. The mentioned entities are virtualized and performed on general-purpose processors forming a processing pool (PP) facility. Given that the location of PPs may be spread over the network and the PPs have limited capacity, it leads to the optimization problem concerning the placement of DUs and CUs. In the NGFI network scenario, the radio data between the RU, DU, CU, and a data center (DC)—in which the traffic is aggregated—are transmitted in the form of packets over a convergent packet-switched network. Because the packet transmission is nondeterministic, special attention should be put on ensuring the appropriate quality of service (QoS) levels for the latency-sensitive traffic flows. In this paper, we address the latency-aware DU and CU placement (LDCP) problem in NGFI. LDCP concerns the placement of DU/CU entities in PP nodes for a given set of demands assuming the QoS requirements of traffic flows that are related to their latency. To this end, we make use of mixed integer linear programming (MILP) in order to formulate the LDCP optimization problem and to solve it. To assure that the latency requirements are satisfied, we apply a reliable latency model, which is included in the MILP model as a set of constraints. To assess the effectiveness of the MILP method and analyze the network performance, we run a broad set of experiments in different network scenarios.

Can Fine-Grained Functional Split Benefit to the Converged Optical-Wireless Access Networks in 5G and Beyond?

The centralized radio access network (C-RAN) is an effective architecture to promote CAPEX/OPEX reduction and cell cooperation derived from its centralized baseband processing. However, there is a contradiction between centralization gain and transport resource saving, which hinders the vision of a resource-efficient and cost-effective RAN deployment. Advanced RAN architectures with functional splits are then introduced to cope with this challenge. Distinguished with other studies, we are intended to investigate whether a fine-grained functional split architecture could benefit to the RAN evolution, and how it impacts on the converged optical-wireless access networks. To this end, we establish a quantitative model to analyze the performance of this architecture. With the fine-grained split, baseband unit (BBU) is divided into a set of fine-grained units (FU) to be placed in desired processing pools (PP) as a service chain. To analyze the placement performance, we propose a mixed-integer linear programming model (MILP) considering the PP selection, routing, wavelength and bandwidth assignment, as well as latency control to minimize the number of PPs, bandwidth, latency, and functions deployment cost. We compare its performance with other two coarse-grained split architectures, i.e., SBBU (adopt low-PHY split like BBU in 4G) and recently emerged DU-CU in both small-scale and large-scale network scenarios. Our analyses provide insights into the modeling and design of efficient converged optical-wireless access networks in 5G and beyond.