Online Traffic Engineering using Least Interference Optimization

Transcription

1 Online Traffic Engineering using Least Interference Optimization A.B Bagula, M. Botha, and A.E Krzesinski Department of Computer Science University of Stellenbosch, 7 Stellenbosch, South Africa Tel: Fax: bagula@cs.sun.ac.za Abstract Flow-based routing algorithms have recently been proposed where the online routing process uses a priori knowledge of the ingress-egress pairs and an estimation of the traffic profile to reduce LSP rejection in MPLS networks. This paper presents a new routing scheme referred to as Least Interference Optimization (LIO) where the online routing process uses the current bandwidth availability and the traffic flow distribution to achieve traffic engineering in IP networks. A Least Interference Optimization Algorithm (LIOA) is presented which balances the number and quantity of flows carried by a link to achieve efficient routing of MPLS bandwidth-guaranteed label-switched paths (LSPs). Preliminary simulation results show that LIOA outperforms several widely known routing algorithms including the Minimum Hop Algorithm (MHA), Shortest Path First (SPF), Constraint Shortest Path First (CSPF) and Minimum Interference Routing Algorithm (MIRA) on several performance parameters including (1) the LSP rejection and (2) the ease of implementation. Index Terms Traffic engineering, online routing, MHA, SPF, CSPF, MIRA, LIOA. I. INTRODUCTION Traffic engineering (TE) is a network management technique which improves the use and efficiency of a network by explicitly steering the traffic through a routed or switched network to achieve performance goals such as delay minimization, throughput maximization, etc. The routing algorithms currently deployed in the Internet and the extensions proposed to adapt these algorithms for traffic engineering include the widely deployed Shortest Path First (SPF) and Constraint Shortest Path First (CSPF). SPF, the most widely used algorithm for intra-domain routing, uses by default a static model where traffic is routed based on link costs which are inversely proportional to the link capacity as proposed by Cisco. This static routing model may lead to unbalanced network loading by assigning bandwidth requests to overloaded portions of a network while leaving other parts of the network under-utilized. This work is supported by grant numbers and 2677 from the South African National Research Foundation, Siemens Telecommunications and Telkom SA Limited. One of the authors acknowledges a bursary from the Department of Labour (DoL). Opinions expressed and conclusions arrived at are those of the author and are not necessarily to be attributed to the DoL. CSPF was proposed as an algorithm which solves the problem of load balancing of SPF by using link costs which reflect the current resource availability. These include costs which are inversely proportional to the residual link capacities. However both SPF and CSPF are poorly equipped for traffic engineering support under heavy load conditions [1]. Multiprotocol Label Switching (MPLS) enables the construction of a connection-oriented network over the connectionless IP network using bandwidth guaranteed tunnels called Label Switched Paths (LSPs). The Minimum Interference Routing Algorithm (MIRA) [2] was proposed in the context of MPLS networks as a flow-based routing algorithm which sets up LSPs using knowledge of the ingress-egress pairs to prevent the creation of bottlenecks for flows in a network, thus reducing the rejection rate of LSP requests between a specified subset of ingress-egress pairs. However, MIRA causes additional routing complexity which does not necessarily translate into equivalent performance gains. This paper presents a Least Interference Optimization Algorithm (LIOA) for setting up bandwidth-guaranteed tunnels in MPLS networks. The main contributions of this paper are Cost-based route optimization. We present an online method for finding optimal routes using a link cost metric which reduces the interference among competing flows by reducing the numbers and magnitudes of the flows carried on the links. Our routing algorithm has the same complexity as classical routing algorithms such as Breadth First Search and Dijkstra s algorithm. Performance improvement. Our routing model is easy to implement (and thus easily deployed by ISPs) and offers substantial network performance improvement compared to the flow-based MIRA and the packet-based MHA, SPF, and CSPF algorithms. The remainder of this paper is organized as follows. Section II presents the least interference optimization model. Section III contains the performance evaluation. Our conclusions are presented in section IV.

2 II. THE LEAST INTERFERENCE OPTIMIZATION MODEL Consider a network represented by a directed acyclic graph G(N, L) where N is the set of nodes and L is the set of links. Let C l denote the capacity of link l and let P i,e denote the set of feasible paths connecting the ingress-egress pair (i, e). Assume that a request to establish an LSP tunnel of bandwidth d i,e units between an ingress-egress pair (i, e) is received and that future demands concerning LSP requests are not known. The routing problem consists of finding the best feasible path p P i,e such that 0 < f l < C l (1) d i,e min l p (C l f l ) (2) L p = l p L l (I l, f l ) (3) L p = min p P i,e L p (4) where L l (I l, f l ) is the cost of link l when carrying I l flows, f l is the total flows on link l and L p is the cost of path p. Equations (1), (2), (3) and (4) express respectively the feasibility of the flows, the feasibility of the path, the additivity of the path cost function and the optimality of the routing process. A. The Minimum Interference Routing Algorithm The objective of MIRA is to find a path between a pair of nodes that blocks the least amount of available flow between all other source-destination nodes. The link penalty function MIRA uses the link penalty function expressing costs which determine the relative importance of the ingress-egress pairs. In MIRA, computing the link costs reduces to finding the set of critical links for all ingress-egress pairs where the set of critical links belong to min-cut sets computed using the current residual link capacity. The link penalty function is L l = 1/θ s,d (5) (s,d ):l C s,d where θ s,d is the max-flow between pair (s, d ) and C s,d is the set of critical links between the pairs (s, d ) as determined by the max-flow algorithm. The MIRA routing algorithm Consider a demand for d i,e bandwidth units between two nodes i and e. MIRA uses the link penalty function defined in equation (5) above and executes seven steps in routing this demand 1) Compute the max-flow values θ s,d for all (s, d ) pairs except (i, e). 2) Compute the set of critical links for all (s, d ) pairs except (i, e). A link is critical for a given ingress-egress pair if that link belongs to any min-cut for that ingressegress pair. 3) Compute the link costs L l. 4) Compute the reduced network. Eliminate all links with residual capacities less than d i,e to form a reduced network whose links have sufficient spare capacity to carry the demand d i,e. 5) Find the new least cost path. Use Dijkstra s algorithm to find the new least cost path p from i to e in the reduced network using the link costs L l. 6) Route the traffic demand. Assign the traffic demand d i,e to the path p. 7) Update the link flows. For each link l p : f l := f l + d i,e. B. The Least Interference Optimization Algorithm The objective of LIOA is to find a path between a pair of nodes that minimizes the number and the magnitude of the flows carried by the links on the path. A power-based penalty function The link penalty function is L l (I l, f l ) = I α l /S 1 α l (6) where the link interference I l is the number of flows carried on the link, the link slack S l = C l f l where C l is the link capacity, f l is the traffic carried by a link l and 0 α 1 is a parameter representing the trade-off between LIO and CSPF routing. The link penalty function (6) is minimized by minimizing the the link interference I l and maximizing the link slack S l. By dispersing traffic flows over the network through interference minimization, this link cost will minimize the number of LSPs blocked under congestion. Note that L l (I l, f l ) = 1/(C l f l ) α = 0 CSPF routing 1/C l α = f l = 0 SPF routing I l α = 1 LIO routing When 0 < α < 1 the link penalty function yields a mix of LIO and CSPF routing. The impact of the parameter α on the link penalty function is discussed further in Section III. A log-based penalty function A link penalty function based on the logarithm of the powerbased penalty function (6) is given by L l (I l, f l ) = α log(i l + 1) + (1 α) log(k/(c l f l )) where 0 < α 1, 1 k/(c l f l ) (I l + 1) α/(1 α) and k = max l C l avoids negative link costs. The LIO routing algorithm Consider a demand for d i,e bandwidth units between two nodes i and e. LIOA executes four steps in routing this demand 1) Compute the link costs { Cl f L(I l, f l ) = l d i,e Il α/(c l f l ) 1 α C l f l > d i,e.

3 2) Find the new least cost path. Apply Dijkstra s algorithm to find the least cost path p P i,e. 3) Route the traffic demand. Assign the traffic demand d i,e to path p. 4) Update the link flows and interference. For each link l p : I l := I l + 1; f l := f l + d i,e. The original MIRA algorithm [2] required as many maxflow computations as the number of ingress-egress pairs where each max-flow computation has O(N 3 ) complexity for a network of N nodes. Improved versions of MIRA known as S-MIRA and L-MIRA use approximations of the link interference [4]. These improvements result in a lower LSP rejection compared to the original MIRA algorithm but result in additional complexity which can outweigh the advantages offered by the approximations. The complexity of LIOA is determined by the shortest path computation which has complexity O(N + L) or O(N 2 ) depending on whether shortest paths are found using a Breadth First Search or Dijkstra s algorithm. III. PERFORMANCE EVALUATION We conducted simulation experiments using a fictitious representation of a South African network illustrated in figure 1 to compare the performance of five routing algorithms (1) Minimum Hop Algorithm (MHA) (2) Least Interference Optimization Algorithm (LIOA) (3) Shortest Path (SPF) (4) Constraint Shortest Path (CSPF) and (5) Minimum Interference Routing (MIRA). The test network has 23 nodes with 88 unidirectional links. The dark thick links have capacity 48 units and the light thin links have capacity 12 units corresponding respectively to OC-48 and OC-12 links. In our simulation experiments, each node is used as an ingress and egress node, leading to 506 ingress-egress pairs. 22 Springbok Klerksdorp 15 8 Kroonstad Upington 18 Kimberley 17 Bloemfontein 16 De Aar 19 Mafikeng 14 Pretoria 12 Messina 10 Pietersburg/Polokwane Johannesburg Nelspruit 9 Richards Bay 23 Pietermaritzburg 7 6 Durban Hop Algorithm (MHA), Shortest Path first (SPF) and Constraint Shortest Path First (CSPF) compare versus the MIRA algorithm. The second metric reveals the impact of bandwidth allocation on future admission control. LSP rejection refers to LSP requests which were not accepted due to random fluctuation of the bandwidth availability. A. LSP rejection We conducted simulation experiments to analyze (1) the behavior of the different algorithms (2) the impact of the LIOA calibration parameter α on the network performance and (3) the performance difference resulting from using the multiplicative and additive versions of the LIOA cost function. Different algorithms We simulated two LSP setup modes: static and dynamic. In the static mode, LSP requests were long-lived while the dynamic mode assumed that LSP requests are offered randomly, are kept for a random time period and finally torn down. Each simulation experiment consists of 20 trials. In the static case, the link capacities were scaled by LSP setups each requesting between 1 to 6 units of bandwidth were simulated. In the dynamic case, the link capacities were scaled by long-lived LSPs were first loaded. Thereafter 2000 more short-lived LSP requests were simulated. The LSP requests were for 1-6 units of bandwidth. Figure 2 compares the performance achieved by MIRA, SPF, MHA, CSPF and LIOA in dynamic mode. Figure 2 reveals that LIOA rejects fewer LSP requests compared to the three other algorithms Fig. 2. MHA CSPF SPF MIRA LIOA Network optimality: dynamic mode Cape Town Saldanha 21 1 Beaufort West Fig Mosselbaai 2 Bisho 5 4 East London 3 Port Elizabeth A South African network The relevant performance metrics are LSP rejection under congestion (heavy load conditions) and the quality of the paths found by the different algorithms. The first parameter was chosen to compare how algorithms such as LIOA, Minimum The results illustrated in figure 3 are found when MIRA, SPF, MHA, CSPF and LIOA are compared in static mode. These results reveal that LIOA rejects fewer requests compared to the three other algorithms. The calibration parameter α We conducted simulation experiments in dynamic mode to evaluate the impact of α as a parameter expressing the tradeoff between LIO and CSPF routing. Figure 4 reveals the lowest LSP rejection is obtained for α = 0.5, a value balancing LIO and CSPF routing.

4 Fig. 3. MHA CSPF SPF MIRA LIOA Network optimality: static mode α = 0.8 α = 0.2 α = 0.5 B. The quality of the paths We conducted simulation experiments in dynamic mode to compare the quality of the LSP pathsets found by LIOA and MIRA. A trial of 5500 events was used for each experiment. Let P L and P M denote the sets of paths found by LIOA and MIRA respectively. The pie chart in Figure 6 presents a graphical view of the correlation between the pathsets P L and P M. The slice labelled P L P M (strong) represents the LSPs common to both P L and P M whose LIOA and MIRA flow values differ by less than 5%. The slice labelled P L P M (weak) represents the LSPs common to both P L and P M whose LIOA and MIRA flow values differ by more than 5%. The slice labelled P L \ P M represents the LSPs discovered only by LIOA and the slice labelled P M \ P L represents the LSPs discovered only by MIRA. With reference to Figure 6 we see that 55% of the paths in LIOA and MIRA are strongly correlated (L L L M (strong)), 29% are weakly correlated (L L L M (weak)), 7% of the LSPs were found only by MIRA (L M \ L L ), and 9% of the LSPs were found only by LIOA (L L \ L M ). L L L M (strong) PSfrag replacements Fig. 4. The calibration parameter α L M \L L PSfrag replacements Power-based versus log-based metrics Figure 5 compares the two cost metrics (power- and logbased) using different values of the α parameter for the two metrics in dynamic mode. The figure shows that the two cost metrics achieve approximately the same performance when α = 0.5 for the power-based metric and α = 0.2 for the logbased metric. This reveals for the test network being simulated, our routing scheme is relatively insensitive to the composition rule of the link penalty function: an additive composition rule performs as well as a multiplicative composition rule Fig. 5. Log Power Power- versus log-based metrics Fig. 6. L L L M (weak) L L \L M LSP correlation between MIRA and LIOA In general we observe that LIOA achieves lower rejection compared to MIRA. This relative efficiency results from the set of weakly correlated LSPs where flows are more efficiently distributed by LIOA. IV. CONCLUSION This paper presents an efficient routing algorithm using the least interference optimization (LIO) concept to achieve online traffic engineering in IP networks. A new cost metric which balances the number and intensity of flows offered to a network is presented. We show that this metric achieves network optimization under heavy traffic load profiles. This cost metric may be implemented using either a power version based on a multiplicative composition rule or a logarithmic version based on an additive composition rule with the same efficiency. For the network model under consideration, simulation results reveal that the Least Interference Optimization Algorithm (LIOA) requiring neither a priori knowledge of the ingressegress pair nor the estimation of the traffic profile outperforms the widely known Minimum Hop Algorithm (MHA), Shortest

5 Path First (SPF), Constraint Shortest Path First (CSPF) and Minimum Interference Routing Algorithm (MIRA) algorithms on several performance aspects including the simplicity of the implementation and the optimization of the network. REFERENCES [1] I.W. Widjaja, I. Saniee, A. Elwalid, D. Mitra, Online Traffic Engineering with Design-Based Routing, 15th ITC Specialist Seminar on Internet Traffic Engineering and Traffic Management, Würzburg, Germany, July [2] K. Kar, M. Kodialam, T.V. Lakshman. Minimum Interference Routing with Application to MPLS Traffic Engineering, Proc. IEEE INFOCOM, Tel-Aviv, Israel, March [3] J. Stevens. Spatial Reuse through Dynamic Power and Routing Control in Common-Channel Random-Access Packet Radio Networks, SURAN Program Technical Note (SRNTN) 59, Richardson, TX:Rockwell Inc., August Available from Defense Technical Information Center (DTIC). [4] M. Kodialam, T. V. Lakshman. MPLS Traffic Engineering Using Enhanced Minimum Interference Routing: An Approach Based On Lexicographic Max-Flow, Proceedings of Eighth International Workshop on Quality of Service (IWQoS), Pittsburgh, USA, June [5] S. Suri, M. Waldvogel, P. Warkhede Profile-based Routing: A new Framework for MPLS Traffic Engineering, Quality of Future Internet Services, Lecture Notes in Computer Science, Vol 2156, September Antoine Bigomokero Bagula obtained the MEng in Computer Engineering from the Université Catholique de Louvain in Belgium and the MSc in Computer Science from the University of Stellenbosch. He is currently a lecturer and a PhD student in the Department of Computer Science at the University of Stellenbosch.