A load-balancing spare capacity reallocation approach in service-rich SONET metro mesh networks

The next-generation SONET metro network is evolving into a service-rich infrastructure. At the edge of such a network, multi-service provisioning platforms (MSPPs) provide efficient data mapping enabled by Generic Framing Procedure (GFP) and Virtual Concatenation (VC). The core of the network tends to be a meshed architecture equipped with Multi-Service Switches (MSSs). In the context of these emerging technologies, we propose a load-balancing spare capacity reallocation approach to improve network utilization in the next-generation SONET metro networks. Using our approach, carriers can postpone network upgrades, resulting in increased revenue with reduced capital expenditures (CAPEX). For the first time, we consider the spare capacity reallocation problem from a capacity upgrade and network planning perspective. Our approach can operate in the context of shared-path protection (with backup multiplexing) because it reallocates spare capacity without disrupting working services. Unlike previous spare capacity reallocation approaches which aim at minimizing total spare capacity, our load-balancing approach minimizes the network load vector (NLV), which is a novel metric that reflects the network load distribution. Because NLV takes into consideration both uniform and non-uniform link capacity distribution, our approach can benefit both uniform and non-uniform networks. We develop a greedy loadbalancing spare capacity reallocation (GLB-SCR) heuristic algorithm to implement this approach. Our experimental results show that GLB-SCR outperforms a previously proposed algorithm (SSR) in terms of established connection capacity and total network capacity in both uniform and non-uniform networks.

A Load-Balancing Shared-Protection-Path Reconfiguration Approach in WDM Wavelength-Routed Networks

Wavelength-routed networks (WRN) are very promising candidates for next-generation Internet and telecommunication backbones. In such a network, optical-layer protection is of paramount importance due to the risk of losing large amounts of data under a failure. To protect the network against this risk, service providers usually provide a pair of risk-independent working and protection paths for each optical connection. However, the investment made for the optical-layer protection increases network cost. To reduce the capital expenditure, service providers need to efficiently utilize their network resources. Among all the existing approaches, shared-path protection has proven to be practical and cost-efficient [1]. In shared-path protection, several protection paths can share a wavelength on a fiber link if their working paths are risk-independent. In real-world networks, provisioning is usually implemented without the knowledge of future network resource utilization status. As the network changes with the addition and deletion of connections, the network utilization will become sub-optimal. Reconfiguration, which is referred to as the method of re-provisioning the existing connections, is an attractive solution to fill in the gap between the current network utilization and its optimal value [2]. In this paper, we propose a new shared-protection-path reconfiguration approach. Unlike some of previous reconfiguration approaches that alter the working paths, our approach only changes protection paths, and hence does not interfere with the ongoing services on the working paths, and is therefore risk-free. Previous studies have verified the benefits arising from the reconfiguration of existing connections [2] [3] [4]. Most of them are aimed at minimizing the total used wavelength-links or ports. However, this objective does not directly relate to cost saving because minimizing the total network resource consumption does not necessarily maximize the capability of accommodating future connections. As a result, service providers may still need to pay for early network upgrades. Alternatively, our proposed shared-protection-path reconfiguration approach is based on a load-balancing objective, which minimizes the network load distribution vector (LDV, see Section 2). This new objective is designed to postpone network upgrades, thus bringing extra cost savings to service providers. In other words, by using the new objective, service providers can establish as many connections as possible before network upgrades, resulting in increased revenue. We develop a heuristic load-balancing (LB) reconfiguration approach based on this new objective and compare its performance with an approach previously introduced in [2] and [4], whose objective is minimizing the total network resource consumption.

Balancing Exploration and Exploitation: A New Algorithm for Active Machine Learning

Active machine learning algorithms are used when large numbers of unlabeled examples are available and getting labels for them is costly (e.g. requiring consulting a human expert). Many conventional active learning algorithms focus on refining the decision boundary, at the expense of exploring new regions that the current hypothesis misclassifies. We propose a new active learning algorithm that balances such exploration with refining of the decision boundary by dynamically adjusting the probability to explore at each step. Our experimental results demonstrate improved performance on data sets that require extensive exploration while remaining competitive on data sets that do not. Our algorithm also shows significant tolerance of noise.