A STUDY ON QOS ROUTING FOR PROVIDING GUARANTEED SERVICES UNDER MULTI-CLASS TRAFFIC LOADS JIA LEI NATIONAL UNIVERSITY OF SINGAPORE 2005 A STUDY ON QOS ROUTING FOR PROVIDING GUARANTEED SERVICES UNDER MULTI-CLASS TRAFFIC LOADS JIA LEI (B.Eng., NANJING UNIVERSITY OF SCIENCE & TECHNOLOGY) A THESIS SUBMITTED FOR THE DEGREE OF MASTER OF ENGINEERING DEPARTMENT OF ELECTRICAL & COMPUTER ENGINEERING NATIONAL UNIVERSITY OF SINGAPORE 2005 Acknowledgements Many thanks are given to my supervisor, Dr. Yin Qinghe, who sparks this research and leads me into the networking research world. His valuable guidance and kindly help in all aspects of my work and life here was really appreciated. Special thanks are given to my wife, who supports me with her love and many encouragements, which accompany me on this long journey. In addition, many of my lab mates and friends had contribution to this work. Especially I would like to thank Huang Qijie and Li Dan for their valuable suggestions and assistance. Also, I want to thank Li Jianfeng, Wu Zheng, Xiao Haiming and Er Inn Inn for those helpful discussions. Thanks also go to National University of Singapore and Institute of Infocomm Research for providing me this great opportunity and all the facilities to carry out my research. This thesis is dedicated to my parents. It is their love and dedication that made everything I have possible. i Contents Acknowledgement i Contents ii List of Figures v List of Tables vii List of Abbreviations viii Summary ix Chapter 1. Introduction 1.1 A Brief Introduction to Next Generation Network 1.2 Research Motivation 1.3 Assumptions .5 1.4 Thesis Contribution………….………………………………………………6 1.5 Thesis Organization .8 Chapter 2. Preliminary and Related Work 2.1 Basic Concepts 2.1.1 Weighted Graph Model .9 2.1.2 QoS Metrics and Constraints 10 2.2 QoS Routing Problems . 13 2.2.1 Routing Techniques 14 ii 2.2.2 QoS Unicast Routing 16 2.2.3 Multi-class QoS Routing .25 2.2.4 QoS Routing and Other QoS-provision Components .27 2.3 Token Bucket Traffic Model .29 2.4 Weighted Fair Queuing 30 Chapter 3. A Multi-class Adaptive QoS Routing Algorithm 34 3.1 Network Model 35 3.2 ULARAC Path Algorithm for DBCLC Path Problem .37 3.2.1 DBCLC Path Problem .37 3.2.2 ULARAC Cost Function .40 3.2.3 ULARAC Path Algorithm 42 3.2.4 Loop-free Property and Complexity Analysis 45 3.3 A Multi-class Adaptive QoS Routing Algorithm 46 3.3.1 Routing Principle 47 3.3.2 VRB Calculating Method .48 3.3.3 A Multi-class Adaptive QoS Routing (MAQR) algorithm .52 Chapter 4. Multi-class Adaptive QoS Routing Algorithm: Simulation Study 55 4.1 Network Topology .56 4.2 Traffic Load .58 4.3 Performance Metrics 59 4.4 Performance Evaluation .60 4.4.1 Impact of Best-effort Traffic Distribution 61 4.4.1.1 Uneven Distribution of Best-effort Traffic 61 iii 4.4.1.2 Even Distribution of Best-effort Traffic 67 4.4.2 Impact of Delay Constraint .68 4.4.3 Impact of the Characteristics of the Links 73 4.5 Summary 74 Chapter 5. Conclusions 76 Bibliography 79 iv List of Figures Figure 2.1: Path selection from s to d with (cost, delay) values indicated on each link and routing objective is cost minimization and path delay no more than 40 ms……. 17 Figure 2.2: Token bucket traffic model .30 Figure 2.3: GPS server model 31 Figure 3.1: ULARAC path algorithm. .44 Figure 3.2: A constructed loop path .45 Figure 3.3: Representation of calculating credit function 52 Figure 4.1: Mesh (3*3) topology. 57 Figure 4.2: vBNS topology. .57 Figure 4.3: Average loss rate of best-effort traffic as a function of QoS traffic load: flow 1, Delay bound=0.025s, vBNS topology. .64 Figure 4.4: Average loss rate of best-effort traffic as a function of QoS traffic load: flow 2, Delay bound=0.025s, vBNS topology. .65 Figure 4.5: Average loss rate of best-effort traffic as a function of QoS traffic load: flow 2, Delay bound=0.048s, cluster topology. 65 Figure 4.6: Average loss rate of best-effort traffic as a function of QoS traffic load: flow 1, Delay bound=0.048s, cluster topology. 66 Figure 4.7: Average loss rate of best-effort traffic as a function of QoS traffic load: even distribution, cluster topology 66 Figure 4.8: Average loss rate of best-effort traffic as a function of QoS traffic load: flow 1, Delay bound=0.04s, vBNS topology. .71 v Figure 4.9: Average loss rate of best-effort traffic as a function of QoS traffic load: flow 2, Delay bound=0.04s, vBNS topology. 71 Figure 4.10:Average loss rate of best-effort traffic as a function of QoS traffic load: flow 1, Delay bound=0.02s, vBNS topology. .72 Figure 4.11:Average loss rate of best-effort traffic as a function of QoS traffic load: flow 2, Delay bound=0.02s, vBNS topology. .72 vi List of Tables Table 4.1: Blocking rate for QoS connections: Delay bound=0.025s, vBNS topology .64 Table 4.2: Performance gains for best-effort traffic under different delay requirementin vBNS topology. .70 vii List of Abbreviations AODV AS BGP CBR DBCLC FRED FEC GPS IS-IS LARAC LBAP MCI MPLS MAQR MPCPO MBGP NGN OSPF PGPS PSTN QoS RED RIP SLS SFQ ULARAC vBNS VRB WFQ Ad-hoc On-demand Distance Vector Autonomous System Border Gateway Protocol Constraint-Based Routing Delay and Bandwidth Constrained Least Cost Flow Random Early Detection Forwarding Equivalence Class Generalized Processor Sharing Intermediate System-Intermediate System Lagrange Relaxation based Aggregated Cost Linear Bounded Arrival Processes Microwave Communications Inc. Multi-Protocol Label Switching Multi-class Adaptive QoS Routing Multi-Path-Constrained Path-Optimization routing Multi-protocol BGP Next Generation Network Open Shortest Path First Packet-by-packet Generalized Processor Sharing Public Switched Telephone Network Quality of Services Random Early Detection Routing Information Protocol Service Level Specification Stochastic Fair Queuing Utilization based and Lagrange Relaxation based Aggregated Cost very high-speed Backbone Network Services Virtual Residual Bandwidth Weighted Fair Queuing viii routing algorithm has to route the QoS traffic along the links that are already loaded by large amount of best-effort traffic, which certainly impact the performance of besteffort traffic. Furthermore, if the delay requirement is too stringent to some extent, the worst case is that there possibly exists just one feasible path between a node-pair, which subsequently causes the inter-class resource sharing strategy to take no effect on QoS routing, which may yield the results as in Figure 4.10. In summary, Figures 4.8 - 4.11 combined with Table 4.2 demonstrate that the proposed routing algorithm works well when the delay requirement is not too stringent and the performance improvement decreases as the delay requirement becomes more and more stringent. Table 4.2: Performance gains for best-effort traffic under different delay requirements in vBNS topology. 70 Figure 4.8: Average loss rate of best-effort traffic as a function of QoS traffic load: flow 1, Delay bound=0.04s, vBNS topology. Figure 4.9: Average loss rate of best-effort traffic as a function of QoS traffic load: flow 2, Delay bound=0.04s, vBNS topology. 71 Figure 4.10: Average loss rate of best-effort traffic as a function of QoS traffic load: flow 1, Delay bound=0.02s, vBNS topology. Figure 4.11: Average loss rate of best-effort traffic as a function of QoS traffic load: flow 2, Delay bound=0.02s, vBNS topology. 72 4.4.3 Impact of the characteristics of the links Figure 4.5 and Figure 4.6 show the average loss rate of best-effort traffic as a function of the QoS traffic loads under one same delay constraint, i.e. 48 milliseconds for the cluster topology. By comparing these two figures, we find easily that the performance gains for best-effort flow and flow are not similar. Specifically, the performance gain for best-effort flow in Figure 4.6 is almost unnoticeable. On the contrary, a clear performance improvement for best-effort flow in Figure 4.5 is observed, where in fact the average performance gain is 1.175% and even higher up to 2.327% when the QoS traffic load goes beyond 400 MB/s. Also, similar observations can be obtained in the experiments for the vBNS topology. From Table 4.2, as we compare the average performance gains between best-effort flow (left column) and flow (right column), we observe that the data in the right column are always larger than that of the same row in the left column. This is not unexpected and can be explained as different links in the network have different characteristics. These characteristics include the propagation delay and physical (or geographic) position of a link in the network topology. First, since QoS traffic in the network is delay sensitive, the first objective of the proposed routing algorithm is to search for a feasible path that suits the delay constraint. A link with shorter propagation delay is then more desired to constitute a feasible path. In fact, we found in our experiments that in order for a path to meet the end-to-end delay requirement, those links that are of short propagation delay are usually heavily used to deliver QoS traffic by the routing algorithm. Second, the other kind of links that are more desired by routing are those that are situated in the ‘key’ positions in the network, which can be likened to the transportation 73 junctions. The ‘key’ positions are usually in the center of the network topology or the places through which traffic most possibly traverses from one part of the network to another part of the network. For example, the link from node to node in the cluster topology is just situated in the center; and in the vBNS topology, both best-effort traffic and QoS traffic either traverses the link from Denver to Chicago or the link from Los Angeles to Houston if they are originated from the west coast and destined for the east coast of the U.S. The above-mentioned links were all observed to deliver large amount of both types of traffic in the experiments. As a result, this kind of links that are more favored by the QoS routing, due to their characteristics of short propagation delay and ‘key’ positions in the network, usually continue to be routed with QoS traffic when they are already congested by best-effort traffic, which subsequently gives rise to the ineffectiveness of the proposed routing algorithm. In conclusion, for a proper delay constraint, the proposed routing algorithm works most effectively under the circumstance that the final result path consists of other than this kind of links. 4.5 Summary We studied the proposed multi-class adaptive QoS routing algorithm by simulations. Our experiments reveal three main results, of which result and were found for the first time to our knowledge: 1. We observed obvious performance improvements for best-effort traffic when the traffic load is heavy, unevenly distributed and delay constraints for QoS traffic 74 are not too stringent (beyond 25 milliseconds in our simulations) while not deteriorating the performance of QoS traffic. Moreover, our approach resulted in just little performance degradation for best-effort traffic when the traffic load is evenly distributed. 2. We observed that the performance improvements for best-effort traffic are compromised by the delay constraints. As delay constraints become more and more stringent, the performance improvements decrease. 3. We observed that the performance improvements vary as different kinds of links constitute a result path by the routing algorithm. The performance improvements are more significant on the paths that consist of those links that are not favored by QoS traffic under same delay constraints and same traffic loads. 75 Chapter Conclusions Evolutions in network services, technology and regulation are creating a golden era of network innovation. Much is certain for this evolution. For instance, Next Generation Network (NGN) will support proliferate services that a service provider can offer or facilitate to its customers. Four classes of services are elaborated on in [25] including multi-parties real-time interactive communications that have strict QoS requirements for the underlying transport network; and rather traditional information services that are more data-oriented and not ask for much QoS guarantee. Facing new challenges imposed on the underlying transport network in NGN, innovative QoS routing, especially those taking multiple classes of services into account, are definitely needed. In this thesis, we presented routing algorithms not only to provide end-to-end delay guarantee for QoS traffic, but also at the same time to improve the performance of best-effort traffic. 76 We first presented an applicable algorithm to solve the DBCLC path problem. We use the reciprocal of link’s residual bandwidth as a link’s original cost. Then we introduce WFQ delay bound into the original LARAC cost function and obtain an extended aggregated cost for each link. As WFQ scheduler can be commercially applied on each router, our technique provides a practical way of solving the DBCLC path problem for the network operators. Next, we further evolved the algorithm to a multiclass adaptive routing algorithm by adopting VRB concept. We presented our own mathematical computation method to obtain VRB for each link. The proposed multiclass QoS routing algorithm was verified by simulation experiments. Our simulation results showed that the routing algorithm taking multi-class traffic loads into account can lead to significant performance improvement for best-effort traffic when best-effort traffic load is heavy, unevenly distributed and delay constraints for QoS traffic are not too stringent. The routing algorithm will not degrade the performance of QoS traffic in most scenarios. However, for the reason that our multi-class adaptive QoS routing algorithm is to provide end-to-end delay guarantee in addition to bandwidth guarantee, the efficiency of the algorithm is compromised by stringent delay constraints. Moreover, an interesting behavior is observed that performance improvements vary as a best-effort session routes its traffic on different kinds of links. If the session routes its traffic on one kind of links that are mostly favored by QoS traffic, little performance improvement can be seen. Note that the inefficiency of the proposed routing algorithm adopting VRB concept is not observed in the multi-class QoS routing algorithms only for bandwidth-guaranteed traffic and best-effort traffic. 77 Throughout our study, we assume that network state and topology information is accurate and up-to-date when the node is making a path selection decision. 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[42] Lei Jia and Qinghe Yin, “A Study of QoS Routing Problem for Providing End-toEnd Guaranteed Services under Multi-class Traffic Loads”, submitted to a conference, Dec. 2005. 85 [...]... requirement of a specific application is given as a set of constraints, which can be link constraints, path constraints and/or tree constraints A link constraint specifies the restriction on the use of links For example, a bandwidth constraint of a unicast connection requires that the links constituting the path must have certain amount of residual bandwidth available for the connection A link constraint is... Delay and Bandwidth Constrained Least Cost (DBCLC) path problem We define a link’s cost to represent the link utilization ratio and construct an aggregated cost in order for the adoption of Lagrange Relaxation technique Then, we propose the Utilization and Lagrange Relaxation based Aggregated Cost path selection algorithm Next, we extend the algorithm to a multi- class adaptive routing algorithm addressing... Therefore, QoS routing under multi- class traffic loads is studied in this thesis More precisely, we study the routing problems based on bandwidth-delay -guaranteed QoS traffic and traditional best-effort traffic The objective of our algorithms is to provide the end-to-end QoS guarantee for QoS traffic and achieve high resource utilization for whole network First, we present an applicable QoS routing algorithm... bandwidthguaranteed traffic and best-effort traffic, which might not be adequate To our understanding, most real-time services in NGN would have strict requirement on the end-to-end network delay On account of that, a study on the multi- class routing is definitely needed based on the coexistence of best-effort traffic and such kind of QoS traffic that not only asks for bandwidth guarantee, but also asks for delay guarantee... with certain bandwidth guarantee take minimizing call blocking rate as the unique objective while not considering the performance of other classes of traffic loads, especially the traditional best-effort traffic in the network As far as we know, not much effort was devoted to path selection decision policy based on multi- class traffic loads in the network Note that routing in such an integrated service... presented a multi- class routing algorithm addressing dynamic resource sharing between different classes of traffic [23] They initially put forward the idea of ‘virtual residual bandwidth’, which is created to adjust each link’s actual residual bandwidth to reflect the best-effort traffic load on the link Similarly, Y Chen and R Hwang presented QoS routing algorithms [3] for multiple classes of traffic by applying... selection approach for delay-constrained routing The approach floods routing messages from source towards destination node Each message accumulates the total delay of the path it has traversed so far When reaching an intermediate node, the message continues to be forwarded only if at least one of the two conditions is satisfied: 1 First such message is received by the node 2 The message holds a better accumulated... strategies can be classified in terms of either the mechanisms for triggering a search for feasible paths (satisfying constraints), or the amount of state maintained [4, 18] at each node In [40], routing strategies are classified as follows according to different mechanism for triggering a search for feasible paths 14 Pro-active (Pre-Computation) Routing: This routing approach stores the routes to all... and best-effort traffic, two classes of traffic compete for the network resources while best-effort traffic can only share the bandwidth left unused by QoS traffic Thus, an “optimal” path selected for a QoS flow may worsen the congestion condition or even create starvation of best-effort flows In order to address this issue, several studies were carried out in the literature For example, Q Ma and P Steenkiste... particular interest as well First of all, we give the formal definition of the equivalent problem, the Delay Constrained Least Cost path problem (referred hereafter simply as DCLC) as follows [15]: Given a directed, connected graph G(V,E), a non-negative total cost of a path c(p) and a total delay of a path d(p), a source node s, a destination node t, and a positive delay constraint ∆delay The constrained . utilization ratio and construct an aggregated cost in order for the adoption of Lagrange Relaxation technique. Then, we propose the Utilization and Lagrange Relaxation based Aggregated Cost path. problems based on bandwidth-delay -guaranteed QoS traffic and traditional best-effort traffic. The objective of our algorithms is to provide the end-to-end QoS guarantee for QoS traffic and achieve. best-effort traffic and such kind of QoS traffic that not only asks for bandwidth guarantee, but also asks for delay guarantee. Therefore, we perform such a study in this thesis and propose an approach