QoS-Aware Routing Schemes Based on Hierarchical Load-Balancing for Integrated Services Packet Networks

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1 QoS-Aware Routing Schemes Based on Hierarchical -Balancing for Integrated Services Packet Networks C. Casetti, R. Lo Cigno, M. Mellia Dipartimento di Elettronica, Politecnico di Torino, Italy Abstract This paper presents a load balancing method to improve network utilization when static routing algorithms are employed. Static routing algorithms can generally be reduced to a path assignment problem with the aim of minimizing a cost function: i.e., maximizing network revenues. Unfortunately the problem, when multiple constraints such as different QoS traffic classes are present, is computationally intractable. balancing methods are heuristics that allow the identification of local minima of the cost function that hopefully are close to the real optimum. The load balancing method we propose is based only on static information about the average traffic load of each traffic relation: a piece of information which can easily be attained in telecommunication networks. The path-selection strategy is defined in terms of Set Theory and its performance is evaluated via simulation by comparison against standard Minimum-Hop path selections. Simulation results show that low priority traffic benefits significantly from the more balanced load obtained with our solution. I. INTRODUCTION Integrated-Services Packet Networks (ISPN) are emerging as one of the new challenges in networking research. ISPN is not yet a completely defined term: the assumption in this work is that we deal with a packet-switched network supporting two or more traffic classes differentiated by their required Quality of Service (QoS). Next generation Internet will be (hopefully) an ISPN, but Asynchronous Transfer Mode (ATM) networks are ISPNs as well, with the additional constraint of handling only fixed-size packets named cells. Routing strategies play an important role in ISPNs, since it is expected that they have wide-area coverage and they are highly meshed. QoS-aware routing takes into account also the characteristics of the connection that is being routed through the network, in order to exploit network resources to their full extent. Traditionally, routing strategies have been divided into static and dynamic ones. Dynamic routing strategies [1], [2], [3] are very appealing for their capacity to deal with unpredictable changes in traffic pattern; however, in very large networks, they suffer from the stale information problem: routing decisions are taken on estimates of the network condition that may be either wrong or out-of-date and this may induce inefficient network exploitation and, ultimately, even network oscillation and collapse [4]. Static routing strategies are generally not based on the network load, but on topological properties of the network, such as the number of crossed nodes, the length and the capacity of the links, and so forth. A good example of different static rout- This work was supported in part by a research contract between CSELT and Politecnico di Torino and in part by the Italian Government through the Ministry for University and Research (MURST) and through the National Research Council (CNR). ing strategies can be found in [5]. In our work we focus on static routing strategies, leaving for future research a comparison with dynamic ones. The performance of static routing algorithms can be enhanced making use of some statistical knowledge of the traffic, such as the long-term average of the traffic offered to the network from node to node. In any case, the aim of any static routing algorithm is the identification of the set of routes, for any source destination pair µ, that optimize (or at least enhance) the network performance. The identification of the optimal set of routes is generally a NP-Complete problem when multiple constraints are present [6], [7], hence approximations or heuristics are needed to obtain routing algorithms with good performance in ISPN networks, especially if multiple service classes are present. Our work introduces a load-balancing heuristics that can be hierarchically applied to different traffic classes for the definition of the route sets to be used by alternate-path (primary-path only can be seen as a special case) routing schemes. Alternate routes are sequentially probed as the CAC (Connection Admission Control) function denies allocation on the chosen path. We point out that routing and CAC functions are not tightly coupled, so that, for instance, a static routing algorithm can be coupled with either a static CAC (e.g., peak or equivalent bandwidth) or with a dynamic CAC based on traffic measures. The remaining part of the paper is organized as follows: Section II gives a formal definition of the problem, in order to allow a general and theoretical analysis; Section III describes the paths selection algorithms using the formalism defined in Section II; Section IV describes the scenario chosen for results comparison, and Section V presents the numerical results obtained by simulating the above scenario; Section VI, finally, closes the paper with hints on the prosecution of the work. II. SET THEORY PROBLEM FORMULATION AND ALGORITHM DESCRIPTION Let us consider a network Æ µ, where is the set of nodes and is the set of links, and let Å and Ä. We denote by the set of all possible 1 loop-free paths, and by µ the set of all possible 1 loop-free paths between the source node and the destination node. Obviously ¾ µ.let be a generic path, and µ be the i-th path from source to destination. Let also Ð be the set of all possible paths sharing the link Ð,and Ð µ the set of all possible

2 2 paths from to sharing the link Ð. Let µ be a generic cost function associated to the path, related to the network topology; for example, µ can be a function of the physical length of, or of the number of hops Ò µ, or a function of both. Given two paths ½ µ ¾ µ connecting the same source and destination, they are considered -equivalent if ½ µ ¾ µ. We shall also denote by µ the number of -equivalent paths of cost from to. Given a network Æ µ, where a static routing algorithm is employed, let us consider the problem of how to order the set of all possible paths to better exploit the network capacity. Whenever µ ½, the sorting algorithm has to choose between two or more equivalent paths: a supplementary metric has to be defined. To this purpose, let us define some new parameters. Suppose that we know the traffic matrix Ì Ø,where Ø is the probability that a source tries to open a call toward a destination. Let be the offered load of source. Weshall denote by µ another cost function, derived from the bandwidth requirement of the connection originated at the source and routed to the destination ; wethenhave µ Ø. This is called Static Offered, representing the load that a connection offers to the network, evaluated upon the static parameters of the traffic matrix. With the above notation, we can compute a Static Link Cost function е for each link Ð of the network, defined as follows: е ½ Ð µ¾ Ð µ where Ð is the available bandwidth on the link Ð. We deliberately did not specify further the nature of Ð, since this quantity can be used to keep into account higher-priority traffic that effectively shrinks the link s capacity as far as the the lower-priority traffic is concerned (this is the case of best-effort vs. guaranteed traffic). е represents the normalized load that the link Ð would carry if we took into account the traffic resulting from all source-destination pairs that route calls over the set of paths Ð. Again, the term static means that this cost is evaluated on the knowledge of the traffic matrix only, and it is not a real measure of the carried load. However, it should be pointed out that е is updated throught the route computation phase as the staticload algorithm keeps adding new paths. Note also that е can be greater than one. By maximizing over all links of a path, we can derive a Static Cost µ for that path: µ Ñ Ü Ð¾ е We can thus choose between two (or more) -equivalent paths ¾ µ, sorting them according to the cost. III. ALGORITHM DESCRIPTION Because the target of the load balancing algorithms is to order a set of path, we chose to describe these algorithms in a set theory manner, where ordering functions can be applied. ½ or a subset of them, as limited by the route selection algorithm For the sake of simplicity, we first define a set of simple procedures that are later on used to obtain the algorithm we are interested in. Procedure Hop First: HF( ). Given a set of paths, find a subset containing all the paths with topological cost ¾ µ. 1. for all ¾ do 2. if µ then return ; Procedure Min Hop: MH( ). Given a set of paths, find the minimum cost Ñ Ò for each source-destination pair, and return the subset containing all the minimum cost paths. 1. for ½ Å,for ½ Å do 2. µ µ Ñ Ò Ñ Ò µµ; ¾ µ 3. HF µ Ñ Ò µ 4. return ; Procedure Equivalent Path: EP( ). Given a set of paths, find a subset containing all the paths with a number of -equivalent paths equal to. 1. for ½ Å,for ½ Å do 2. µ µ 3. if µ then 4. µ ; 5. return The following procedures rely on vector Ä, which is the accumulator for the Static Link Cost. Ä is initialized to zero and is updated whenever the SCU procedure (see below) is executed. Procedure Static Cost Update: SCU( ). Given a path, update the Static Link Cost е for its links. 1. let and be the source and destination nodes linked by ; 2. for all link Ð ¾ do 3. е е ½ Ð µ Procedure Static Sorting: SLS( ). Given an unordered path set, find an ordered subset of paths with the minimum Static Cost for each pair µ. Note that this is the only procedure that provides an ordering of the input set of paths. 1. ¼ 2. for ½ Å ½ Å do 3. µ µ ; 4. find ¾ µ such that µ Ñ Ò ¾ µ µµ 2 5. ; 6. SCU( ); 7. ½ 8. return ; A. Hierarchical Balancing Algorithm When multiple traffic classes are present, with different QoS requirements as well as different priorities, the path assignment ¾ If there is more than one minimum cost path, one can recursively choose between them again recalculating µ on all the links Ð ¾ except for the one for which µ е. At last, one can choose between them randomly.

3 3 procedure must be aware that connections will be accepted by CAC functions following different criteria. CAC functions and packet schedulers, generally assume hard class separation with acceptance preemption, i.e., regardless of the network being already saturated by low priority calls, a higher priority one will be accepted, and its packets will be served with priority, that is in non FIFO order, once they are in the network. As fas as CAC and scheduling are concerned, a hierarchical path selection technique based on load balancing should be an appropriate solution to help low-priority traffic to avoid routes heavily used by higher priority traffic. Let µ be the set of available paths in the network Æ. By dividing it into different subsets and ordering them according to the Static Cost function, we obtain a path assignment from the sources to the destinations, achieving, hopefully, a better exploitation of the network capacity than by simple Minimum-Hop ordering. Notice that Minimum-Hop ordering yields exactly the same path selection, regardless of it being run on all traffic classes together or hierarchically class by class. We describe a single sorting criteria, although more heuristics were studied in [8], and obviously many more can be defined. The algorithm works in cycles: in every cycle, it assign one and only one path per source-destination pair, if it exists, starting from the paths with the minimum number of hops. For the sake of clarity the algorithm is described for a single traffic class, avoiding an additional loop on traffic classes and the consequent complication in the notation. Hierarchically moving from one class to the next, the static link cost е is reset to the static load е of higher-qos-classes sources whose primary path Ô belongs to Ð. We chose not to take into account the load due to secondary paths from higher classes, since secondary paths are less likely to carry newly activated calls than primary paths. B. Algorithm: Balanced Primary First BPF( ) The following algorithm assigns at every loop a single path ¾ (if it exists) from the source to the destination,resorting the remaining path set, in order to find the best path to assign in the next cycle. This is obtained by applying, in sequence, the Min Hop algorithm to set aside all the shortest paths for all source-destination pairs, then the Equivalent Path algorithm to identify all the subsets with -equivalent paths. Each subset is processed by the Hop First algorithm which, in turn, yields a sub-subset of shortest paths. Eventually, the Static Sorting algorithm is used over all sub-subsets identified above to obtain the final order, as shown in Fig. 1. The result is a reordered paths set where, for each traffic relation µ, there is an ordered paths sub-set  µ in which the first one is termed primary and all the others are termed secondary. The routing function probes all the paths in order and the connection is refused only if the CAC functions can not allocate it on any paths of the sub-set µ, as is done in the Uncontrolled Alternate Routing [5]. 1. while do 2.  =MH( ); 3. ¼; 4. while  do 5. ½; 6.  =EP( ); 7.    ; 8. Ñ Ü ; 9. for ½ Ñ Ü do 10. ½; 11. while  do 12.  =HF  µ; 13.  =SLS( ); 14.   ½; 15. return ; J 1 Equivalent Path J e,1 Hop First J e J e,2 J e,c Static Sorting Min Hop Π ~ ~ ~ ~ ~ ~ J 1,1 J 1,2 J e,1 J e,2 J h,k Fig. 1. Algorithm Balanced Primary First (BPF) IV. SIMULATION SETUP The effectiveness of the algorithm presented in the previous sections was studied by means of a call-level simulation tool for high-speed networks, called ANCLES [9][10][11], assuming two traffic classes: a high priority, guaranteed-qos class and a low priority, best-effort class. The network topology of choice is shown in Fig. 2; it combines a rather tight mesh with hundreds of loop-free paths and a localized bottleneck. The link speed, which is the same for all links, is equal to 500 Mb/s. A mix of constant- and variable-bit rate sources, located in every node, are assumed to generate traffic in the highest QoS class: the constant-bit rate sources try to open calls at Ô ¾Mb/s rate toward each other, (call holding times are determined by i.i.d. exponential random variables averaging at 500 seconds); the variable-bit rate sources activate calls at Ô ½¼Mb/s which, once activated and admitted to the network, hold for an exponentially distributed period, with average 1000 seconds; variable-bit rate calls exhibit a burstiness degree (peak-to-mean rate ratio) equal to 5, thus resulting in an average rate ¾Mb/s. The call admission control scheme to which the above calls are subject is rather simple; for each link, new calls are accepted if: Ñ «(1) ¾ ½ Ô where ½ and ¾ are the sets of constant- and variable-bit rate calls, respectively, is the link capacity, and «is a protection coefficient («½) that can be set so as to avoid that the whole J Π

4 4 1e+00 N9 1e-01 N8 N7 N6 N5 N4 N1 N3 N2 N10 N11 Call Blocking Probability 1e-02 1e-03 1e-04 γ = 0.4 γ = 1 N12 1e-05 Fig. 2. The examined topology link capacity is used up. Ñ indicates a parameter characterizing connections with a cell rate value midway between Ô and, according to a factor ; thatis: Ñ Ô µ (2) A discussion of the CAC scheme just described and its relation to some equivalent bandwidth expressions can be found in [12]. In our simulations, we chose «¼ and ¼ (and, for some results, ½, yielding peak allocation). For what concerns best-effort, lower-priority users, nodes N1, N8 and N12 are requested to activate connections with a peak rate Ô Å (but we also report results with Ô ½¼Å ) and average holding time equal to 60 seconds. Each best-effort source equally directs its traffic toward nodes N4, N6 and N10. The actual bandwidth, possibly smaller than the peak, exploited by users of this class is determined by a maxmin fairness allocation scheme that, for each call, accounts for the number of calls on the bottleneck link and for the residual capacity available to the best-effort class 1. No CAC is implemented for best-effort calls. The network traffic was split among guaranteed (40% of the overall traffic) and best-effort (60%) calls. Also, calls are routed according to the Hierarchical Balancing Algorithm detailed in Subsection III-A; note that, since there is no CAC for best-effort traffic, all calls in this class are routed over their respective primary paths. So as to provide an insight into the complexity of the load balancing algorithms we are proposing, we estimated that sorting all possible paths of the network in Figure 2 (approx. 40,000 paths) required just 2 seconds on a Pentium II-266MHz workstation. V. NUMERICAL RESULTS Simulations were run to obtain an estimate of how much bandwidth can be spared when the proposed load-balancing algorithm is used, with respect to a situation where the same rout- ½ Of course, this is only an estimate of what the best-effort connections get, depending on the actual policy used by the network to allocate resources to connections; however regardless of the policies and the performance they attain, the relative merits of routing schemes should not be affected. Fig. 3. Call blocking probabilities as a function of the overall guaranteed-class network load in Gb/s ing and CAC algorithms were deployed, without preventively balancing the load (i.e. sorting the set of the available paths only by topological cost ). Figure 3 shows the call blocking probabilities for guaranteed traffic which were estimated as a function of the overall guaranteed-class load offered to the network (in Gb/s), when different values of, ranging from a peak-rate characterization of variable-bit rate sources ( ½) toamore relaxed one ( ¼ ). Solid lines refer to curves obtained without load balancing, while dashed lines refer to the load balancing algorithm. It can be observed that balancing guaranteed traffic does not improve calls chances of being admitted to the network, nor does it lead to higher blocking probabilities. If we now turn our attention onto the lower QoS traffic class, we can try and gauge how the combination of load balancing applied to both traffic classes can, on the one hand, rationalize the distribution of guaranteed traffic, thus freeing up resources, and, on the other, efficiently allocate the leftover bandwidth to best-effort traffic. To this purpose, we defined a users reward as the ratio between the peak bandwidth that a best-effort call requests and the actual average bandwidth that the call has been able to exploit throughout its life. Figures 4 and 5 report such rewards, averaged over all calls that were activated during the simulation, for different values of the admission parameter. It can be seen that a reward increment as high as 5% can be achieved when the load balancing algorithm is applied. A closer inspection of the results allows us to link the rewards to the actual paths choice as determined by the load balancing algorithm. Table I details the effectiveness of the load balancing algorithm at limiting the number of times non-primary (and thus more resources consuming) paths are checked for admission of the higher-qos calls before a suitable path is found and the call is routed over it. Expressed through percentages, these quantities also provide us with a general indication of the call setup time and of how it can be considerably shortened when using a load-balancing algorithm.

5 5 Users reward Users reward Mb/s 10 Mb/s 0.6 Fig. 4. Average best-effort users reward as a function of guaranteed-class network load in Gb/s - = Mb/s 10 Mb/s 0.6 Fig. 5. Average best-effort users reward as a function of guaranteed-class network load in Gb/s - =1 Path = 1.5 = 1.6 # Unbal. Bal. Unbal. Bal Path = 1.8 = 2.0 # Unbal. Bal. Unbal. Bal TABLE I PERCENTAGE OF TIMES A GUARANTEED-CLASS CALL IS ROUTED OVER FIRST-, SECOND-, THIRD- AND GREATER-THAN-THIRD-CHOICE PATHS, ¾ VI. CONCLUSIONS This paper has presented a load balancing path selection algorithm for static routing strategies, that is suited for hierarchical implementation in networks that support multiple QoS traffic. The load balancing metric was based only on the minimum available bandwidth on the path; however metrics that are based also on the expected delay and/or delay variations are not difficult to be defined and will be studied in the future. The proposed sorting algorithm was implemented and a simulation study was carried out on a complex, meshed topology, with two traffic classes, in order to compare the proposed scheme with a standard Minimum-Hop path selection algorithm, applying an uncontrolled alternate routing strategy to the paths selected. Results show that the proposed strategy allows the lower traffic class to obtain an increased share of resources, while obviously not affecting the higher class traffic. The total traffic admitted within the network was not observed to change significantly; however it must be noted that only a single topology with uniform traffic distribution was studied in this early stage of the research and the situation might change significantly if the traffic load is not uniform. Future research will cover this point. ACKNOWLEDGEMENTS The authors wish to thank Gabriele Favalessa and Luca Medico for their contributions to the simulator code. REFERENCES [1] E. Crawley, R. Nair, B. Rajagopalan, H. Sandick, A Framework for QoSbased Routing in the Internet, Internet-Draft, July 1998, work in progress [2] Q. Ma, P. Steenkiste. Routing Traffic with Quality-of-Service Guarantees in Integrated Services Networks, In Proceedings of the 8th IEEE/ACM International Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV 98), pages , England, July 1998 [3] R. Guerin, S. Kamat, A. Orda, and T. Przygienda, QoS Routing Mechanisms and OSPF Extensions, Internet-Draft, Mar. 1998, work in progress [4] C. Huitema, Routing in the Internet, Prentice Hall, Englewood Cliffs, NJ, USA, 1995 [5] S. Sibal, A. DeSimone, Controlling Alternate Routing in General-Mesh Packet Flow Networks, SIGCOMM 94, London, England, Aug [6] Z. Wang, J. Crowcroft, QoS Routing for Supporting Resource Reservation, IEEE Journal on Selected Areas in Communication, Sept [7] M. Marathe, R. Sundaram, S. Ravi, D. Rosenkrantz, H. Hunt, Bicriteria Network Design Problems, Proceedings of the 22nd International Colloquium on Automata Languages and Programming, LNCS 944, pp , 1995 [8] C. Casetti, G. Favalessa, R. Lo Cigno, L. Medico, M. Mellia, - Balancing Solutions for Static Routing Schemes in ATM Networks, Sixth IFIP Workshop on Performance Modelling and Evaluation of ATM Networks, Ilkley, U.K., July, 1998 [9] M. Ajmone Marsan, A. Bianco, C. Casetti, C. F. Chiasserini, A. Francini, R. Lo Cigno, M. Mellia, M. Munafò, An Integrated Software Environment for the Simulation of ATM Networks, SCSC 97, Arlington, VA, USA, July 1997 [10] M. Ajmone Marsan, A. Bianco, C. Casetti, C. F. Chiasserini, A. Francini, R. Lo cigno, M. Munafò, An Integrated Simulation Environment for the Analysis of ATM Networks at Multiple Time Scales, Computer Networks and ISDN Systems, Special Issue on Modeling of Wired - and Wireless - ATM Networks, Vol. 29, no , pp , Feb [11] ANCLES ATM Networks Call-LEvel Simulator URL: [12] M. Ajmone Marsan, A. Bianco, C. Casetti, P. Castelli, R. Lo Cigno, M. Mellia, M. Munafò, A CAC Algorithm Supporting Different QoS Classes, D. Kouvatsos (editor), Performance Modelling and Evaluation of ATM Networks Vol. 4, Chapman and Hall, London, July 1998