Premium
A hop‐by‐hop delay‐constrained routing algorithm with explicit loop avoidance and backup routing information
Author(s) -
Zhang Baoxian,
Chen Changjia,
Mouftah Hussein T.
Publication year - 2004
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.657
Subject(s) - computer science , computer network , static routing , dynamic source routing , link state routing protocol , distributed computing , equal cost multi path routing , routing information protocol , destination sequenced distance vector routing , policy based routing , geographic routing , path vector protocol , routing table , multipath routing , backup , routing protocol , zone routing protocol , routing (electronic design automation) , database
QoS Routing is crucial for QoS provisioning in high‐speed networks. In general, QoS routing can be classified into two paradigms: source routing and hop‐by‐hop routing. In source routing, the entire path to the destination node of a communication request is locally computed at the source node based on the global state that it maintains, which does not scale well to large networks. In hop‐by‐hop routing, a path‐selecting process is shared among intermediate nodes between the source node and the destination node, which can largely improve the protocol scalability. In this paper, we present the design of hop‐by‐hop routing with backup route information such that each intermediate node can recursively update the best known feasible path, if possible, by collectively utilizing the routing information gathered thus far and the information that it locally stores. Such a route is kept as a backup route and its path cost is used as a reference to guide the subsequent routing process to search for a lower‐cost constrained path and avoid performance degradation. In this way, the information gathered is maximally utilized for improved performance. We prove the correctness of our presented algorithm and deduce its worst message complexity to be O (∣ V ∣ 2 ), where ∣ V ∣ is the number of network nodes. Simulation results indicate that, however, the designed algorithm requires much fewer messages on average. Therefore it scales well with respect to the network size. Moreover, simulation results demonstrate that the cost performance of our algorithm is near‐optimal. Copyright © 2004 John Wiley & Sons, Ltd.