Scalable QoS routing in MPLS networks using mobile code

In a continually evolving Internet, tools such as Q u a lity o f Service ro u tin g must be used in order to accommodate user demands. However, deploying and developing QoS routing in the legacy Internet is difficult. Multiprotocol Label Switching (MPLS) facilitates the deployment of QoS routing, due to its separation of functions between the control and forwarding plane. Developing QoS routing raises scalability issues within very large networks. I propose overcoming these issues by using topology aggregation and distributed routing based on modem techniques such as active networks and mobile agents. However, topology aggregation introduces inaccuracy, which has a negative impact on QoS routing performance. To avoid such problems I propose a hierarchical routing protocol, called Macro-routing, which by using distributed route computation is able to process more detailed information and thus to use the most accurate aggregation technique, i.e. Full-Mesh. Therefore, the protocol is more likely to find the best path between source and destination, and can also find more than one available path. QoS routing, which is used for finding feasible paths that simultaneously satisfy multiple constraints, is also called multiple-constrained routing and is an NP-complete problem. The difficulty of solving such problems increases in a hierarchical context, where aggregation techniques influence the path computation process. I propose a new aggregation technique which allows the selection of multiple paths that satisfy multiple QoS constraints. This reduces the probability of a false negative, i.e., of the routing algorithm incorrectly reporting that no path satisfying the constraints exists. This aggregation technique is called extended full-mesh (EFM) and is intended for use with the Macro-routing protocol. Deploying these protocols in the Internet will allow multi-constrained routing to be practically implemented on large networks

Routing with QoS Information Aggregation in Hierarchical Networks

Abstract-In this paper, we consider the problem of routing with two additive constraints in the hierarchical networks, such as the Internet. In order for scalability, the supported QoS information in the hierarchical networks has to be aggregated. We propose a novel method for aggregating the QoS information. To the best of our knowledge, our approach is the first study to use the area-minimization optimization, the de facto optimization problem of the QoS information aggregation. We use a set of real numbers to approximate the supported QoS between different domains. The size of the set is predefined so that advertisement overhead and the space requirement will not grow exponentially as the network size grows. The simulation results show that the proposed method outperforms the existing methods

Macro-routing: a new hierarchical routing protocol

In a continually evolving Internet, tools such as quality of service routing must be used in order to accommodate user demands. QoS routing raises scalability issues within very large networks, which can he avoided by using hierarchical routing strategies. However, such strategies can lead to inaccurate path selection due to the aggregation process. To avoid such problems, we propose a hierarchical routing protocol, called macro-routing, which can distribute the route computation more efficiently throughout the network using mobile agents. It processes more detailed information than conventional hierarchical routing protocols, so is more likely to find the best path between source and destination. Also, by using mobile agents, more than one available path can be found. This provides a fast recovery mechanism, where no protocol restart is needed in a failure situation

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

A QoS-Driven ISP Selection Mechanism for IPv6 Multi-homed Sites

A global solution for the provision of QoS in IPng sites must include ISP selection based on per-application requirements. In this article we present a new site-local architecture for QoS-driven ISP selection in multi-homed domains, performed in a per application basis. This architecture proposes the novel use of existent network services, a new type of routing header, and the modification of address selection mechanisms to take into account QoS requirements. This proposal is an evolution of current technology, and therefore precludes the addition of new protocols, enabling fast deployment. The sitelocal scope of the proposed solution results in ISP transparency and thus in ISP independency.This research was supported by the LONG (Laboratories Over the Next Generation Networks) project IST-1999-20393.Publicad