Bo Li, Karthikeyan Karur Balu, and Kaiqi Xiong Rochester Institute of Technology

1. The Performance Evaluation of Intra-domain Bandwidth Allocation and Inter-domain Routing Algorithms for a QoS-guaranteed Routing Path Discovery 1st DFG/GENI Doctoral Consortium, San Juan, PR March 13th-15th, 2011 Bo Li, Karthikeyan Karur Balu, and Kaiqi Xiong Rochester Institute of Technology Abstract We study dynamic bandwidth allocation in multi-domain networks while minimizing the total cost of network bandwidth used by a network service provider who must meet the requirements of Quality of Service (QoS) predefined in the Service Level Agreement (SLA). The goal of this project is to evaluate the performance of intra-domain bandwidth allocation and inter-domain routing algorithms using GENI infrastructure. We have conducted research experiments on ProtoGENI for the validation of the calculation of a percentile delay and the evaluation of Additive Increase/Multiplicative Decrease (AIMD)-based bandwidth allocation algorithms. Furthermore, we have developed those related GENI educational experiments that are being used in networking courses • Bandwidth Allocation Scenarios • Use ProtoGENI to simulate a multi-domain network environment where the bandwidth of network nodes along a given service path is fixed. We determine bandwidth requirement using the AIMD-based technique and its variants to ensure QoS guarantees for customers from different client domains. The technique taken here is to gradually increase bandwidth through a probe of available bandwidth until a critical stage is reached where no more bandwidth is available. When the critical stage occurs, allocated bandwidth is reduced to half and the whole process is repeated Multi-Domain Networks • Motivation and Significance of Work • The “Triple” and “Quadruple play” are part of the cable and telecom industry’s strategy to offer new networking services, which will permit them to hold on to their customer base, and to increase market share • Dimensioning network gears have a significant impact towards the competitive pricing of bundled network services. The models that we develop will serve as a necessary tool for providing proper dimensioning guidelines The Queueing Network Model Experimental Results The experiment result reveals that network bandwidth at each node can be dynamically allocated to ensure QoS guarantees for customer services in both scenarios • Research Questions • For a given arrival rate of customer service requests and given bandwidth along an end-to-end network path, what level of QoS requirements can be guaranteed? • How to allocate bandwidth along a network path to ensure QoS guarantees for a given number of clients with varied service requests? • How to allocate bandwidth along a network path to ensure QoS guarantees for a dynamic number of clients with varied service requests? Optimization Approach Bandwidth Shared Among Clients Let ci be the cost for one unit of bandwidth at network node i modeled as queue i. The bandwidth bi that should be allocated on the output link of each queue i can be formulated as the following optimization problem: A Comparison of Experimental and Theoretical Results subject to the percentile constraint: the end-to-end delay is less than a pre-specified value TD, γ% of the time Note: In general, other QoS metrics can be included GENI Education Experiments By leveraging the above research experiments, we have designed and developed lab exercises for the graduate-level course: Network Design and Performance that has been offered in the winter quarter End-to-end delay: T = X1+X2+…+Xm Experimental Validation Using ProtoGENI A tandem network consists of eight network nodes, clients, and servers who offer services to clients. We consider the scenarios of campus network traffic and large-scale network traffic applications respectively. Our goal is to validate the calculation of a percentiledelay used as one of the QoS performance metrics Laplace Transform of T: LT(s) = LX1(s) LX2(s) …LXm(s) Bandwidth Analysis in Multi-domain Networks Percentile end-to-end delay: FT(t) = L-1(LT(s)/s), where F(t) is a cumulative distribution function of T • Conclusions and Future Work • It has been shown that GENI experimental results are consistent with theoretical ones in the calculation of a percentile end-to-end delay • Experiments have showed that the AIMD-based algorithms can be effectively used to allocate network bandwidth shared among client domains for QoS guarantees • Our preliminary studies have showed that the proposed bandwidth allocation approaches are promising • Extend the above experiments to consider a complex network environment and continue to conduct these experiments on other GENI resources such as OpenFlow, BGP-Mux, and PlanetLab • Conduct research experiments for the algorithms used in solving the problem of partitioning end-to-end QoS requirements in multi-domain networks At a critical stage when the cumulative bandwidth overshoots marked by red, network nodes are designed to control bandwidth allocation Time-based Bandwidth Sharing