On Delay Performance and Burst Assembly for Wireless Mesh and Optical Burst Switching Converged Metro Area Network

Wireless Mesh Networks (WMN) have attracted increasing attention from the research community as a high-performance and low-cost solution to last-mile broadband Internet access. On the other side, Optical Burst Switching (OBS) is a promising access technology that uses optical fiber with burst switching paradigm. In this paper, we propose a novel Metropolitan Area Network (MAN) architecture, called Optical Burst Wireless Mesh Architecture (OBWMA) which integrates WMN at the user access side and OBS at the core of the MAN. OBWMA aims to combine advantages of both WMNs and OBS networks, such as large coverage at low cost and bandwidth availability. We specify the details of the interconnection and the internetworking of WMNs and the OBS network in OBWMA. Moreover, we develop an analytical model to compute the end-to-end delay in OBWMA in order to support flow requests with delay constraints. Furthermore, we propose a Control Bridge (CB) that ensures Quality of Service (QoS) mapping at the border between the WMN and the OBS parts. Also, we propose a burst assembly scheme, called Adaptive Hybrid Burst Assembly scheme (AHBA). Simulation results using ns-2 demonstrate the feasibility of OBWMA and the validity of our analytical model.     

Novel reinforcement learning-based approaches to reduce loss probability in buffer-less OBS networks

Optical Burst Switching (OBS) is a promising switching paradigm for the next generation Internet. A buffer-less OBS network can be implemented simply and cost-effectively without the need for either wavelength converters or optical buffers which are, currently, neither cost-effective nor technologically mature. However, this type of OBS networks suffers from relatively high loss probability caused by wavelength contentions at core nodes. This could prevent or, at least, delay the adoption of OBS networks as a solution for the next generation optical Internet. To enhance the performance of buffer-less OBS networks, we propose three approaches: (a) a reactive approach, called Reinforcement Learning-Based Deflection Routing Scheme (RLDRS) that aims to resolve wavelength contentions, after they occur, using deflection routing; (b) a proactive multi-path approach, called Reinforcement Learning-Based Alternative Routing (RLAR), that aims to reduce wavelength contentions; and (c) an approach, called Integrated Reinforcement Learning-based Routing and Contention Resolution (IRLRCR), that combines RLAR and RLDRS to conjointly deal with wavelength contentions proactively and reactively. Simulation results show that both RLAR and RLDRS reduce, effectively, loss probability in buffer-less OBS networks and outperform the existing multi-path and deflection routing approaches, respectively. Moreover, simulation results show that a substantial performance improvement, in terms of loss probability, is obtained using IRLRCR.     

Reinforcement learning based routing in wireless mesh networks

This paper addresses the problem of efficient routing in backbone wireless mesh networks (WMNs) where each mesh router is equipped with multiple radio interfaces and a subset of nodes serve as gateways to the Internet. Most routing schemes have been designed to reduce routing costs by optimizing one metric, e.g., hop count and interference ratio. However, when considering these metrics together, the complexity of the routing problem increases drastically. Thus, an efficient and adaptive routing scheme that takes into account several metrics simultaneously and considers traffic congestion around the gateways is needed. In this paper, we propose an adaptive scheme for routing traffic in WMNs, called Reinforcement Learning-based Distributed Routing (RLBDR), that (1) considers the critical areas around the gateways where mesh routers are much more likely to become congested and (2) adaptively learns an optimal routing policy taking into account multiple metrics, such as loss ratio, interference ratio, load at the gateways and end-to end delay. Simulation results show that RLBDR can significantly improve the overall network performance compared to schemes using either Metric of Interference and Channel switching, Best Path to Best Gateway, Expected Transmission count, nearest gateway (i.e., shortest path to gateway) or load at gateways as a metric for path selection.     

Topology-aware wavelength partitioning for DWDM OBS networks: A novel approach for absolute QoS provisioning

Optical Burst Switching (OBS) is a promising switching technology for the next generation all-optical networks. An OBS network without wavelength converters and fiber delay lines can be implemented simply and cost-effectively using the existing technology. However, this kind of networks suffers from a relatively high burst loss probability at the OBS core nodes. To overcome this issue and consolidate OBS networks with QoS provisioning capabilities, we propose a wavelength partitioning approach, called Optimization-based Topology-aware Wavelength Partitioning approach (OTWP). OTWP formulates the wavelength partitioning problem, based on the topology of the network, as an Integer Linear Programming (ILP) model and uses a tabu search algorithm (TS) to resolve large instances efficiently. We use OTWP to develop an absolute QoS differentiation scheme, called Absolute Fair Quality of service Differentiation scheme (AFQD). AFQD is the first absolute QoS provisioning scheme that guarantees loss-free transmission for high priority traffic, inside the OBS network, regardless of its topology. Also, we use OTWP to develop a wavelength assignment scheme, called Best Effort Traffic Wavelength Assignment scheme (BETWA). BETWA aims to reduce loss probability for best effort traffic. To make AFQD adaptive to non-uniform traffic, we develop a wavelength borrowing protocol, called Wavelength Borrowing Protocol (WBP). Numerical results show the effectiveness of the proposed tabu search algorithm to resolve large instances of the partitioning problem. Also, simulation results, using ns-2, show that: (a) AFQD provides an excellent quality of service differentiation; (b) BETWA substantially decreases the loss probability of best effort traffic to a remarkably low level for the OBS network under study; and (c) WBP makes AFQD adaptive to non-uniform traffic by reducing efficiently blocking probability for high priority traffic.