IEEE/ACM Transactions on Networking (TON)
Random early detection gateways for congestion avoidance
IEEE/ACM Transactions on Networking (TON)
On the self-similar nature of Ethernet traffic (extended version)
IEEE/ACM Transactions on Networking (TON)
IEEE/ACM Transactions on Networking (TON)
Self-similarity in World Wide Web traffic: evidence and possible causes
IEEE/ACM Transactions on Networking (TON)
Generating representative Web workloads for network and server performance evaluation
SIGMETRICS '98/PERFORMANCE '98 Proceedings of the 1998 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
Explicit allocation of best-effort packet delivery service
IEEE/ACM Transactions on Networking (TON)
Use of &agr;-stable self-similar stochastic processes for modeling traffic in broadband networks
Performance Evaluation - Special issue on performance and control of network systems
WF2Q: worst-case fair weighted fair queueing
INFOCOM'96 Proceedings of the Fifteenth annual joint conference of the IEEE computer and communications societies conference on The conference on computer communications - Volume 1
Dynamic bandwidth allocation policies
INFOCOM'96 Proceedings of the Fifteenth annual joint conference of the IEEE computer and communications societies conference on The conference on computer communications - Volume 2
Adaptive bandwidth provisioning with explicit respect to QoS requirements
Computer Communications
Inter-domain SLS negotiation for end-to-end UMTS/IMS QoS
Journal of Mobile Multimedia
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Recent works propose the use of fractional stable noise (FSN) to capture the statistical properties of an arrival process over time intervals. This process can reproduce the properties of long-range dependence and high variability exhibited by traffic in real-life networks. However, when modeling network traffic with this @a-stable long-range dependent stochastic process, some analytical difficulties arise. For instance, the value of its index of stability @a conditions the existence of some moments, which in turn limits the applicability of traditional statistical procedures. Therefore, alternative procedures and methods have to be used. In this work we claim that in spite of the increased complexity, there is much to gain by considering this modeling approach in the context of traffic control. We focus our attention in the prediction of FSN processes and we argue that it can potentially help improving currently existing resource management mechanisms. We support this claim by introducing the Dynamic Predictive Weighted Fair Queueing; a novel algorithm for the dynamic allocation of resources. Our simulation results and consequent performance comparisons with other schemes suggest that the performance of some scheduling algorithms can be highly improved in terms of packet losses and delays by incorporating prediction techniques that take into account the relevant properties of the network traffic.