An algorithm for join/prune mechanisms for improving handoff using mobility prediction in wireless networks

In this paper, we provide a detailed description of an algorithm that implements join and prune mechanisms, which will help to build an optimal multicast tree with QoS requirements during handoff. An analysis is presented to show how mobility prediction can help in the selection of potential access routers (AR) with QoS requirements that affect multicast group size and bandwidth cost in the multicast tree. The proposed technique tries to minimise the number of multicast tree join and prune operations. We have examined the performance of this algorithm using simulations in various environments and obtained good performance results. Our results show that the expected multicast group increases linearly with the increase in the number of selected destination access routers (AR) for multicast during handoff. We observe that the expected number of joins and prunes from the multicast tree increases with group size. Thus, for an increased number of destinations, the estimated cost of the multicast tree in a cellular network also increases

Congestion removal in the next generation internet

The ongoing development of new and demanding Internet applications requires the Internet to deliver better service levels that are significantly better than the best effort service that the Internet currently provides and was built for. These improved service levels include guaranteed delays, jitter and bandwidth. Through extensive research into Quality of Service and Differentiated Service (DiffServ) it has become possible to provide guaranteed services, however this turns out to be inadequate without the application of Traffic Engineering methodologies and principles. Traffic Engineering is an integral part of network operation. Its major goal is to deliver the best performance from an existing service provider's network resources and, at the same time, to enhance a customers' view of network performance. In this thesis, several different traffic engineering methods for optimising the operation of native IP and IP networks employing MPLS are proposed. A feature of these new methods is their fast run times and this opens the way to making them suitable for application in an online traffic engineering environment. For native IP networks running shortest path based routing protocols, we show that an LP-based optimisation based on the well known multi-commodity flow problem can be effective in removing network congestion. Having realised that Internet service providers are now moving towards migrating their networks to the use of MPLS, we have also formulated optimisation methods to traffic engineer MPLS networks by selecting suitable routing paths and utilising the feature of explicit routing contained in MPLS. Although MPLS is capable of delivering traffic engineering across different classes of traffic, network operators still prefer to rely on the proven and simple IP based routing protocols for best effort traffic and only use MPLS to route traffic requiring special forwarding treatment. Based on this fact, we propose a method that optimises the routing patterns applicable to different classes of traffic based on their bandwidth requirements. A traffic engineering comparison study that evaluates the performance of a neural network-based method for MPLS networks and LP-based weight setting approach for shortest path based networks has been performed using a well-known open source network simulator, called ns2. The comparative evaluation is based upon the packet loss probability. The final chapter of the thesis describes the software development of a network management application called OptiFlow which integrates techniques described in earlier chapters including the LP-based weight setting optimisation methodology; it also uses traffic matrix estimation techniques that are required as input to the weight setting models that have been devised. The motivation for developing OptiFlow was to provide a prototype set of tools that meet the congestion management needs of networking industries (ISPs and telecommunications companies - telcos)

Comparison of intra-domain traffic engineering methods

This paper presents a comparison study of two Traffic Engineering (TE) methods in an autonomous system (AS). These two methods are novel compared to the existing methods for they are able to provide TE solutions in order of seconds. One could always argue that better solutions can always be found given more time. However, the emphasis here is both methods are made suitable for online TE and it is shown here that they greatly outperform the common IP routing. Herein, we also discuss the consequence of carrying weight changes during the network operation. We believe that weight changes still outweigh the disturbance caused

Improvement of handoff in wireless networks using mobility prediction and multicasting techniques

Achieving seamless mobility is a significant challenge for wireless networking today. This paper illustrates the use of multicasting techniques aided by mobility prediction to improve handoff performance in wireless networks. Handoff holds the key to defining the performance of wireless networks since there could be packet losses during handoff as the mobile node moves from one point of attachment to another. A new method of determining a multicast tree routing scheme with specific performance objectives is presented in this paper. The Grey model has been used as the prediction methodology as it has been shown to provide good prediction accuracy[1]. A situation is modelled where a multicast tree is defined covering multiple access routers (AR) to maintain connectivity with the mobile node using mobility prediction (by selecting the least number of access routers) whilst ensuring guarantees of bandwidth and minimum hop count such that packet loss can be avoided. To simultaneously solve the above two problem formulations gives rise to a multi-objective optimisation problem. Discovering the optimal routing is an NP hard problem where network state information is not accurate, which is a common feature in wireless networks. After describing the problem, an algorithm that satisfies the constraints and objectives with a near optimal cost is presented

OptiFlow - A Capacity Management Tool

Intra-domain traffic engineering for routing protocols such as OSPF (open shortest path first) or intermediate system-intermediate system (IS-IS) can be performed by finding a judicious set of weights to allocate to the links of the network. Unfortunately, methods proposed in the literature for finding those weights may require a significant computational effort. By way of contrast, weight setting approaches based on linear programming can be shown in this paper to find suitable weights in the order of seconds. Prior to this time, it was necessary to determine appropriate weight settings in an off-line mode. From the results presented in this paper it can be demonstrated that solutions can be located for this problem in a matter of seconds. This makes it possible to perform traffic engineering for short term link overloads in real-time mode. The performance of this methodology has been verified by using simulation based on the well-known performance tool, ns-2. The technique described in this paper is being integrated into an optimisation module of a network capacity management tool called OptiFlow

COMPARISON OF INTRA-DOMAIN TRAFFIC ENGINEERING METHODS

This paper presents a comparison study of two Traffic Engineering (TE) methods in an autonomous system (AS). These two methods are novel compared to the existing methods for they are able to provide TE solutions in order of seconds. One could always argue that better solutions can always be found given more time. However, the emphasis here is both methods are made suitable for online TE and it is shown here that they greatly outperform the common IP routing. Herein, we also discuss the consequence of carrying weight changes during the network operation. We believe that weight changes still outweigh the disturbance caused.