Quality of service enhancement in optical burst switching networks without full wavelength convension capability

Founded 1905 QUALITY OF SERVICE ENHANCEMENT IN OPTICAL BURST SWITCHING NETWORKS WITHOUT FULL WAVELENGTH CONVERSION CAPABILITY SHAN DONG MEI (M.Sc., M.Eng., B.Eng.) A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY DEPARTMENT OF ELECTRICAL & COMPUTER ENGINEERING NATIONAL UNIVERSITY OF SINGAPORE 2010 MAY Acknowledgments First and foremost, I am truly indebted to my supervisors, Professor Chua Kee Chaing and Professor Mohan Gurusamy for their continuous guidance and support during this work. I have benefited tremendously from the regular discussions with them. Without their help and patience, this work would not be possible. I would also like to thank all the researchers in the Optical Networks Lab, who greatly enriched both my knowledge and life with their intelligence and optimism. I am also grateful to the lab officer, Mr. Koh Eng Seng, for his endeavor to make the lab a neat and pleasant place to work. Last, but not least, I would like to thank all my family members, especially my parents, for their endless love and support. Shan Dong Mei December, 2008 i Contents Acknowledgments i List of Figures vii List of Tables xiii List of Abbreviations xiv Summary xvii Introduction 1.1 The Emergence Of OBS Technology . . . . . . . . . . . . . . . . . . 1.2 OBS Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Quality Of Service In OBS Networks . . . . . . . . . . . . . . . . . 1.4 Wavelength Conversion In OBS Networks . . . . . . . . . . . . . . . 11 1.5 Motivation And Contributions . . . . . . . . . . . . . . . . . . . . . 14 1.5.1 Wavelength Assignment . . . . . . . . . . . . . . . . . . . . 15 1.5.2 Wavelength Converter Allocation . . . . . . . . . . . . . . . 15 1.5.3 Burst Scheduling . . . . . . . . . . . . . . . . . . . . . . . . 16 ii Contents iii 1.6 16 Outline Of The Thesis . . . . . . . . . . . . . . . . . . . . . . . . . QoS In OBS Networks: An Overview 2.1 18 QoS Enhancement . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.1.1 Burst Scheduling . . . . . . . . . . . . . . . . . . . . . . . . 19 2.1.2 Wavelength Assignment . . . . . . . . . . . . . . . . . . . . 24 2.1.3 Traffic Engineering . . . . . . . . . . . . . . . . . . . . . . . 26 2.1.4 Deflection Routing . . . . . . . . . . . . . . . . . . . . . . . 27 2.1.5 Burst Overlap Reduction . . . . . . . . . . . . . . . . . . . . 28 Relative QoS Provisioning . . . . . . . . . . . . . . . . . . . . . . . 28 2.2.1 Intentional-Dropping Based Service Differentiation . . . . . 29 2.2.2 Preemption-Based Service Differentiation . . . . . . . . . . . 29 2.2.3 Header-Buffering-Based Service Differentiation . . . . . . . . 30 2.2.4 Contention-Ability-Based Service Differentiation . . . . . . . 30 2.3 Absolute QoS Provisioning . . . . . . . . . . . . . . . . . . . . . . . 31 2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.2 Priority-Based Offline Wavelength Assignment 34 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.2 Link Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3.2.1 Assumptions and Notations . . . . . . . . . . . . . . . . . . 36 3.2.2 Link Model Description . . . . . . . . . . . . . . . . . . . . 39 3.2.3 Iteration Method . . . . . . . . . . . . . . . . . . . . . . . . 44 Offline Wavelength Assignment Scheme . . . . . . . . . . . . . . . . 46 3.3 Contents 3.4 3.5 iv 3.3.1 Topology Approximation Algorithm . . . . . . . . . . . . . . 46 3.3.2 Priority-Based FFTE Algorithm . . . . . . . . . . . . . . . . 47 3.3.3 WSO Extending Algorithm In The Wavelength Domain . . . 49 3.3.4 Possible Link Models In The PFFTE Scheme . . . . . . . . 50 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 3.4.1 Effect Of Traffic Load . . . . . . . . . . . . . . . . . . . . . 54 3.4.2 Effect Of Delay Bound At Edge Nodes And Number Of Wavelengths Per Link . . . . . . . . . . . . . . . . . . . . . 55 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 Wavelength Converter Allocation 64 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 4.2 Wavelength Converter Allocation Problem . . . . . . . . . . . . . . 66 4.2.1 WC Allocation Problem Formulation . . . . . . . . . . . . . 66 4.2.2 WC Allocation Algorithm . . . . . . . . . . . . . . . . . . . 67 Link Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 4.3.1 Input Traffic Assumption . . . . . . . . . . . . . . . . . . . . 70 4.3.2 Burst Loss Estimation . . . . . . . . . . . . . . . . . . . . . 72 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 4.4.1 Simulations In NSFNET Network . . . . . . . . . . . . . . . 78 4.4.2 Simulations In GEANT Network . . . . . . . . . . . . . . . 80 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 4.3 4.4 4.5 Burst Rescheduling Algorithms 90 Contents v 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 5.2 Burst Rescheduling At A Single Node . . . . . . . . . . . . . . . . . 91 5.2.1 Implementation And Benefit . . . . . . . . . . . . . . . . . . 92 5.2.2 Theoretical Analysis . . . . . . . . . . . . . . . . . . . . . . 94 Burst Rescheduling In A Network . . . . . . . . . . . . . . . . . . . 98 5.3.1 Two Related Phenomena In A Network . . . . . . . . . . . . 98 5.3.2 Rescheduling Algorithms . . . . . . . . . . . . . . . . . . . . 101 5.3.3 Signalling Overheads . . . . . . . . . . . . . . . . . . . . . . 103 5.3.4 Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 5.3 5.4 5.5 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 5.4.1 Simulation Study At A Single Node . . . . . . . . . . . . . . 105 5.4.2 Simulation Study In The Network . . . . . . . . . . . . . . . 106 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 Conclusion 119 6.1 Research Contribution . . . . . . . . . . . . . . . . . . . . . . . . . 119 6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 6.2.1 More Accurate Link Model . . . . . . . . . . . . . . . . . . . 121 6.2.2 WC Allocation under Reduced Load Assumption . . . . . . 122 6.2.3 Optimal Burst Scheduling . . . . . . . . . . . . . . . . . . . 122 6.2.4 Non-Poisson Assumption in Wavelength Assignment . . . . . 122 6.2.5 Performance Benchmarking in Wavelength Assignment . . . 123 Publication 124 Contents Bibliography vi 125 List of Figures 1.1 An OBS network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 OBS core node architecture . . . . . . . . . . . . . . . . . . . . . . . 1.3 The use of offset time . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Reservations for a burst with JIT, the Horizon scheme and JET . . . . . 1.5 Burst allocations using FDLs and wavelength converters . . . . . . . . . 10 1.6 Dedicated wavelength converter deployment structure . . . . . . . . . . 11 1.7 Share-per-node wavelength converter deployment structure . . . . . . . 12 1.8 Share-per-link wavelength converter deployment structure . . . . . . . . 13 2.1 Burst scheduling using LUAC and LAUC-VF . . . . . . . . . . . . . . 20 2.2 Burst scheduling using segmentation and burst rescheduling . . . . . . . 20 3.1 Target link . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.2 One-wavelength contention model . . . . . . . . . . . . . . . . . . . 41 3.3 NSFNET network . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 3.4 Torus network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 vii List of Figures 3.5 viii Performance of FFTE schemes in NSFNET vs. the traffic load under identical traffic demand (14 wavelengths per link and zero delay bound at the edge nodes) . . . . . . . . . . . . . . . . . . . . . . . . 3.6 55 Performance of FFTE schemes in the torus network vs. the traffic load under identical traffic demand (16 wavelengths per link and zero delay bound at the edge nodes) . . . . . . . . . . . . . . . . . . 3.7 56 Performance of FFTE schemes in NSFNET vs. the traffic load under non-identical traffic demand (14 wavelengths per link and zero delay bound at the edge nodes) . . . . . . . . . . . . . . . . . . . . . . . . 3.8 57 Performance of FFTE schemes in the torus network vs. the traffic load under non-identical traffic demand (16 wavelengths per link and zero delay bound at the edge nodes) . . . . . . . . . . . . . . . 3.9 58 Performance advantage of the PFFTE scheme over the NFFTE scheme vs. the traffic load under identical traffic demand (W wavelengths per link and zero delay bound at the edge nodes) . . . . . . 59 3.10 Performance advantage of the PFFTE scheme over the NFFTE scheme vs. the traffic load under non-identical traffic demand (W wavelengths per link and zero delay bound at the edge nodes) . . . 60 3.11 Effect of the delay bound at the edge nodes in integrated networks under non-identical traffic demand (W wavelengths per link) . . . . 61 3.12 Effect of the delay bound at the edge nodes in edge/core networks under non-identical traffic demand (W wavelengths per link) . . . . 61 3.13 Effect of the the number of wavelengths per link in integrated networks under non-identical traffic demand (zero delay bound at the edge nodes) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 List of Figures ix 3.14 Effect of the the number of wavelengths per link in edge/core networks under non-identical traffic demand (zero delay bound at the edge nodes) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 4.1 Output link l . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 4.2 GEANT network . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 4.3 Burst loss probability vs. the average number of FWCs per link when the traffic load is 0.5 in the NSFNET network under identical traffic demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 78 Burst loss probability vs. the average number of FWCs per link when the traffic load is 0.5 in the NSFNET network under nonidentical traffic demand . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Burst loss probability vs. conversion ratio in the NSFNET network when the traffic load is 0.5 under identical traffic demand . . . . . . 4.6 82 Burst loss probability vs. conversion ratio in the NSFNET network when the traffic load is 0.8 under non-identical traffic demand . . . 4.9 81 Burst loss probability vs. conversion ratio in the NSFNET network when the traffic load is 0.5 under non-identical traffic demand . . . 4.8 80 Burst loss probability vs. conversion ratio in the NSFNET network when the traffic load is 0.8 under identical traffic demand . . . . . . 4.7 79 83 Performance improvement after the streamline effect is modelled when the traffic load is 0.5 under the identical traffic demand in the NSFNET network . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 4.10 Performance improvement after the streamline effect is modelled when the traffic load is 0.8 under the identical traffic demand in the NSFNET network . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 Chapter 5. Burst Rescheduling Algorithms 118 Figure 5.14: Increased signalling overhead vs. average traffic load per flow under identical traffic demand Figure 5.15: Increased signalling overhead vs. average traffic load per flow under non-identical traffic demand Chapter Conclusion 6.1 Research Contribution The assumption of non-full wavelength conversion capability in OBS networks is more reasonable compared with the full wavelength conversion assumption in light of the fact the WCs are expensive and still immature technologically in the foreseeable future. However, the non-full wavelength-conversion assumption indicates a lower capability of using WCs to resolve contentions among bursts for output wavelengths. As a result, QoS enhancement becomes more important. In this thesis, we have proposed several QoS enhancement approaches in non-fully wavelengthconvertible OBS networks. In Chapter 3, we have proposed an offline wavelength assignment scheme in non-wavelength-convertible OBS networks. In this approach, the WSO of a connection is formed by sorting wavelengths in decreasing order of their priorities, which are determined based on calculated burst loss probabilities on the different wavelengths along a connection’s route: the lower the calculated burst loss probability on a wavelength, the higher the priority of the wavelength. This way, more traffic is transmitted to wavelengths with fewer contentions among bursts. The burst loss probability on a wavelength along a connection’s route is calculated based on a 119 Chapter 6. Conclusion 120 link model considering the streamline effect, i.e., the impact of the relative traffic load difference among the input streams on the burst loss performance on output wavelength channels. Simulation results have shown that the proposed scheme can improve the burst loss performance in the network dramatically compared with existing schemes due to both its adopted link model and its capability of using the complete set of WSOs. Simulation results also have illustrated that the performance of our scheme can be enhanced when the number of wavelengths per link increases and the allowed delay bound for the scheduling of bursts is prolonged. In Chapter 4, we have studied the WC allocation problem in OBS networks. The aim of WC allocation is to minimize the burst loss probability given the overall number of WCs and the WC deployment structure within the core nodes. We have formulated the WC allocation problem in an OBS network as an integer programming problem and proposed an algorithm accordingly. The key idea of the algorithm is to remove the WCs one by one from an OBS network with full wavelength conversion capability, each time causing the least increase in networkwide burst loss. The burst loss change in the network after a WC is removed is estimated using a link model. We have proven that this scheme is optimal in reducing network-wide burst loss under the constant-load assumption. Simulation results have shown our proposed algorithm can significantly reduce the burst loss probability in an OBS network compared to the uniform WC allocation scheme. Meanwhile, it is shown that modelling the streamline effect can improve the performance of the proposed converter allocation algorithm. We also have observed that the full wavelength conversion performance can be approached only when the conversion degree of WCs is close to the number of wavelengths per link. In chapter 5, we have proposed using burst rescheduling to reduce WC-induced burst loss and hence overall burst loss in partially wavelength-convertible OBS networks with non-zero WC-induced burst loss. We have illustrated that WC-induced burst loss can be minimized at a single node using burst rescheduling. We also have introduced a link model to show theoretically the benefit of burst rescheduling in reducing the WC-induced and the overall burst loss probabilities at a node. In a Chapter 6. Conclusion 121 network however, a rescheduled burst may be dropped at the next node on its route due to its changed wavelength, thus increasing the overall burst loss probability. To reduce the loss of rescheduled bursts, two burst rescheduling algorithms, viz., the CR algorithm and the AR algorithm, have been proposed. In the CR algorithm, the loss of rescheduled bursts is avoided completely by only rescheduling bursts which will not be dropped at subsequent nodes. In the AR algorithm, bursts are also rescheduled if otherwise they will be dropped due to the unavailability of free WCs. The performance of these two rescheduling algorithms is compared with that of the CAS algorithm. Simulation results have shown that both algorithms can significantly outperform the CAS algorithm before the full wavelength conversion performance is approached. Meanwhile, simulation results have also shown that the two burst rescheduling algorithms and the CAS algorithm have similar overheads and average computational complexity when burst rescheduling can significantly reduce the overall burst loss probability. 6.2 6.2.1 Future Work More Accurate Link Model Our proposed offline wavelength assignment scheme and WC allocation algorithm are both based on a link model for estimating the burst loss performance on an output link. Simulation results have shown that the accuracy of a link model can help improve the effectiveness of these schemes in reducing burst loss in the network. However, it is difficult to accurate burst loss performance estimation in OBS networks with non-zero WC-induced burst loss. As a result, the existing link models (including the ones proposed in this thesis) are introduced under certain assumptions that can simply the performance analysis on a link. Such assumptions include, for instance, the same offset time for bursts and the absence of FDLs. As future work, more accurate link models can be introduced without these assumptions. Chapter 6. Conclusion 6.2.2 122 WC Allocation under Reduced Load Assumption In this thesis, we have proven our proposed WC allocation algorithm is optimal under the assumption that the traffic loads of connections remain the same along their routes. As future work, we can study the WC allocation problem considering the reduced load of connections along their paths. This will improve the efficiency of the algorithm in reducing the burst loss probability. 6.2.3 Optimal Burst Scheduling In burst scheduling in partially wavelength convertible OBS networks with non-zero WC-induced burst loss, minimizing WC-induced burst loss is in contradiction with minimizing non-WC-induced burst loss. The reason is that the former generally requires a burst to be allocated to its original wavelength, which may not be the optimal one to reduce non-WC-induced burst loss. Therefore, theoretically, there exists an optimal burst scheduling algorithm to minimize the overall burst loss rather than only one type of burst loss. To design an optimal burst scheduling algorithm, factors that can affect WC-induced burst loss and non-WC-induced burst loss should be considered. Such factors include, for example, the offset time distribution at a node and wavelength conversion capability in the network. 6.2.4 Non-Poisson Assumption in Wavelength Assignment In the work of wavelength assignment, we assume that the locally generated traffic follows Poisson process and carry out the simulation under the same assumption. As future work, the wavelength assignment problem can be studied with nonPoisson assumption. Chapter 6. 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Filling LSP Label Switching Path LWC Limited-Range Wavelength Converter MPLS Multi-Protocol Label Switching NFFTE Node-Based First Fit Based On Trac Engineering NP Nondeterministic Polynomial NSFNET National Science Foundation Network OBS Optical Burst Switching OCS Optical Circuit Switching ODBR On-Demand Burst Rescheduling OM Output Module OPS Optical Packet Switching OS Ordered Scheduling OXC Optical. .. length of bre to delay bursts An all -optical WC is used to change an input wavelength to another wavelength without O/E/O conversion, thus not increasing the burden of electronic processing and not decreasing the data transmission rate in OBS networks There are multiple ways of achieving alloptical wavelength conversion [8] An example of using FDLs and WCs to reduce the burst loss probability is shown in. .. the WDM links 1 Chapter 1 Introduction 1.1 2 The Emergence Of OBS Technology Optical burst switching (OBS) was rst proposed in 1997 as a candidate all -optical switching technology to support the direct transport of IP trac in the optical layer in WDM networks [3] Its details are presented in [4][5][6][7] The motivation of OBS is to combine the advantages of two counterpart technologies, viz., optical. .. same time At the core nodes, bursts are switched in the optical domain from input wavelengths to output wavelengths A core node with two input links and two output links1 is depicted in Fig 1.2 [2] Each link carries three wavelengths w0 , w1 and w2 , with wavelength w0 being a control wavelength dedicated for headers The remaining wavelengths are data wavelengths used for burst communication2 An aggregate... nextgeneration of wavelength division multiplexed backbone transport networks It is reasonable to assume non -full wavelength conversion capability in practical OBS networks since all -optical tunable wavelength converters (TWCs/WCs) are expensive and still immature technologically Without full wavelength conversion capability to resolve contentions among bursts for output wavelengths, quality of service (QoS) enhancement. .. technology which is adopted in current backbone networks In point-to-point switching networks, optical signals carrying IP trac undergo O/E/O conversion at every node between source and destination nodes In OCS, however, O/E/O conversion is replaced by all -optical switching at the intermediate nodes of lightpaths As a result, the burden of electronic processing is reduced, thus leading to a higher data transmission... two-way reservation for each burst introduces long delay and the centralized nature of this scheme does not scale well in long-haul backbone networks 1.3 Quality Of Service In OBS Networks Quality of service (QoS) in OBS networks is mainly evaluated on the burst loss probability due to the buerless nature of OBS Although FDLs can be deployed at the core nodes, they are not fully functional buers and... structure The capability of wavelength conversion in OBS networks depends on the type and deployment structure of WCs within the core nodes WCs can be either limited-ranged (LWCs) or full- ranged (FWCs) An LWC can convert the input wavelength to a subset range of wavelengths in the vicinity of the input wavelength, while an FWC can convert to any wavelength [2] WCs can be deployed at a core Chapter 1 Introduction... issues related to QoS enhancement in OBS networks with non -full wavelength conversion capability These issues include 1) wavelength assignment in non -wavelength- convertible OBS networks, 2) allocation optimization of a given number of WCs at the core nodes to form a par- Chapter 1 Introduction 15 tially wavelength- convertible OBS network, and 3) burst scheduling in a partially wavelength- convertible... chapter has introduced OBS and the motivation of our works in this thesis Chapter 2 gives a survey of the existing QoS mechanisms in OBS, including dierent algorithms for QoS enhancement and various approaches for QoS provisioning Since most of these methods are proposed under full wavelength conversion assumption, their eectiveness in non-fully wavelength- Chapter 1 Introduction 17 convertible OBS networks . 1905 QUALITY OF SERVICE ENHANCEMENT IN OPTICAL BURST SWITCHING NETWORKS WITHOUT FULL WAVELENGTH CONVERSION CAPABILITY SHAN DONG MEI (M.Sc., M.Eng., B.Eng.) A THESIS SUBMITTED FOR THE DEGREE OF. Foundation Network OBS Optical Burst Switching OCS Optical Circuit Switching ODBR On-Demand Burst Rescheduling OM Output Module OPS Optical Packet Switching OS Ordered Scheduling OXC Optical Cross-Connect PFFTE Priority-Based. reschedul- ing (a) burst allocation without burst rescheduling when W = 3 and C = 1; (b) burst allocation with burst rescheduling when W = 3 and C = 1; (c) burst allocation without burst rescheduling when W