An optimal establishment of virtual path connections for ATM networks
INFOCOM '95 Proceedings of the Fourteenth Annual Joint Conference of the IEEE Computer and Communication Societies (Vol. 1)-Volume - Volume 1
Statistical multiplexing and mix-dependent alternative routing in multiservice VP networks
IEEE/ACM Transactions on Networking (TON)
Information Sciences—Informatics and Computer Science: An International Journal
Distributed cost-based update policies for QoS routing on hierarchical networks
Information Sciences—Informatics and Computer Science: An International Journal
Dynamic allocation of resources to virtual path agents
IEEE/ACM Transactions on Networking (TON)
IEEE/ACM Transactions on Networking (TON)
Dynamic bandwidth reservation for label switched paths: An on-line predictive approach
Computer Communications
MPLS automatic bandwidth allocation via adaptive hysteresis
Computer Networks: The International Journal of Computer and Telecommunications Networking
Efficient hierarchical QoS routing in ATM networks
Computer Communications
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We propose a new policy for virtual path bandwidth allocation in broadband networks. Based on a threshold scheme, our policy handles the inherent tradeoff between bandwidth utilization and processing costs. In each virtual path controller the thresholds are chosen so as to keep bandwidth utilization high, while obtaining a low rate of processing requests. Two novel ideas are used in our threshold scheme: adapt ivity, which results in a better prediction of future bandwidth requirements; and hysteresis, which prevents excessive processing of requests due to oscillations around thresholds. We tested the performance of our new bandwidth control scheme, and compared it with previously suggested schemes. The performance measures were the expected amount of unused bandwidth, the average signaling load and the blocking probability. Performance has been evaluated through numerical computations as well as by simulations. Our analysis is based on a time segmentation technique which allows us to reduce a Markov chain with N M states into M Markov chains with N states and a one-dimensional chain with M states. Our results show that our policy significantly improves upon previously suggested approaches.