Improved learning automata applied to routing in multi-service networks

Multi-service communications networks are generally designed, provisioned and configured, based on source-destination user demands expected to occur over a recurring time period. However due to network users' actions being non-deterministic, actual user demands will vary from those expected, potentially causing some network resources to be under- provisioned, with others possibly over-provisioned. As actual user demands vary over the recurring time period from those expected, so the status of the various shared network resources may also vary. This high degree of uncertainty necessitates using adaptive resource allocation mechanisms to share the finite network resources more efficiently so that more of actual user demands may be accommodated onto the network. The overhead for these adaptive resource allocation mechanisms must be low in order to scale for use in large networks carrying many source-destination user demands. This thesis examines the use of stochastic learning automata for the adaptive routing problem (these being adaptive, distributed and simple in implementation and operation) and seeks to improve their weakness of slow convergence whilst maintaining their strength of subsequent near optimal performance. Firstly, current reinforcement algorithms (the part causing the automaton to learn) are examined for applicability, and contrary to the literature the discretised schemes are found in general to be unsuitable. Two algorithms are chosen (one with fast convergence, the other with good subsequent performance) and are improved through automatically adapting the learning rates and automatically switching between the two algorithms. Both novel methods use local entropy of action probabilities for determining convergence state. However when the convergence speed and blocking probability is compared to a bandwidth-based dynamic link-state shortest-path algorithm, the latter is found to be superior. A novel re-application of learning automata to the routing problem is therefore proposed: using link utilisation levels instead of call acceptance or packet delay. Learning automata now return a lower blocking probability than the dynamic shortest-path based scheme under realistic loading levels, but still suffer from a significant number of convergence iterations. Therefore the final improvement is to combine both learning automata and shortest-path concepts to form a hybrid algorithm. The resulting blocking probability of this novel routing algorithm is superior to either algorithm, even when using trend user demands

RSVP- A Fault Tolerant Mechanism in MPLS Networks

The data transmission over the Internet (IP) takes an increasingly central role in our communications infrastructure. Here there may be the slow convergence of routing protocols after a network failure becomes a growing problem. To minimize this problem in this paper I am introducing a concept called Multi-protocol label switching (MPLS) will reduce the time convergence and network recovery is limited process when compared to IP. In this paper presents a new efficient fault-tolerance approach for MPLS. And we used a protocol in MPLS based networks to evaluate fast reroute maintain which is RSVP (Resource reservation protocol). And it uses RIP (Routing Information Protocol) algorithm based on distance vector routing and is supported on a wide variety of systems to overcome all the defects in IP. In this paper we simulated the scenarios by using GNS3 (Graphical Network Simulator 3)

Tuning a Campus Network

The foremost purpose for carrying out this project was to have an understandable conception of what a campus network is and the development it has followed through time due to the various demands from enterprises. The goal was to analyze the different components that compose an enterprise campus network along with a range of practicable routing protocols and Layer 2 switching technologies. The practical part of the project was carried out taking into consideration the requirements of a typical enterprise campus network today due to the services it provides and the demands from its clients. By this, both the clients and the enterprise providing the services need the campus network to perform to its full capacity with a guaranteed security, Quality of Service, and high availability. It was found that using a routing protocol end-to-end was a more efficient way of deploying a highly available enterprise campus network compared to Layer 2 access design. A fully routed enterprise campus network not only triumphs all the bottleneck of Layer 2 designs but as well achieves the needs and demands of clients today without them noticing the downtime. It is fundamental to construct the campus network in a structured manner and considering of the future developments without affecting the current structural design. The designed campus network should be able to easily scale, be manageable, redundant, and with Quality of Service to achieve all the intensifying demands of today’s user