Routing Algorithms Evaluation for Elastic Traffic C. Casetti, R. Lo Cigno, M. Mellia, M. Munaf`o Politecnico di Torino – Dipartimento di Elettronica Corso Duca degli Abruzzi, 24 I-10129 Torino, Italy e-mail: fcasetti,locigno,mellia,
[email protected] Z. Zsoka Technical University of Budapest – Hungary e-mail:
[email protected]
Abstract— An innovative simulation technique for the performance of routing algorithms in presence of elastic traffic is proposed in this paper. Traditional simulation tools for the evaluation of routing strategies are based on the generation of calls whose length is determined when the connection enters the network. But the real duration of connections in the Internet depends on the amount of information to be transferred and by the (time varying) congestion level of the network. The performance method proposed here takes into account the nature of Internet traffic, while maintaining the efficiency of call based event-driven simulations. Results show that conclusion based on traditional methods can be very far from those obtained with the more accurate method proposed here.
I. I NTRODUCTION & W ORK R ATIONALE Research on Quality of Service (QoS) routing in the Internet was extremely active in recent years. [1], [2], [3], [4], [5] are only a few examples of works in this area. Some research focused on algorithmic aspects, trying to find suitable metrics to exploit dynamic traffic conditions to improve performance with respect to the simple Shortest Path (SP ) algorithm, which is used in present day Internet [6], [7]. Other works studied the problem of information distribution, exploring the influence of stale information on the performance of several algorithms [8], [9]. Either a packet based network simulator or a call level simulator is generally used to provide simulation results. All these works assumed a synthetic load of the network where connections duration is determined “a priori,” obtained extracting a random variable, like in traditional circuit switched networks. It is well known, however, that the duration of connections in the Internet is dependent from the network congestion, since is is determined by the amount of time needed to successfully The work was done while Z. Zsoka was a visiting student at the Dipartimento di Elettronica of Politecnico di Torino. This work was partially supported by a contract between CSELT (TELECOM Italia research center) and Politecnico di Torino and by the Italian Ministry for University and Scientific Research (MURST).
transmit a given amount of information. This is true for best effort traffic, but also some connection oriented applications, like audio and video retrieval, are indeed based on file transfers, leading to connection durations that are functions of the network load. On the one hand, it is quite clear that evaluation approaches based on fixed connection duration may lead to incorrect conclusions, while, on the other hand, evaluation approaches based on packet level simulations are computationally too heavy to allow the exploration of suitably large networks for reasonably long amounts of time. Both the above traditional evaluation approaches have serious drawbacks. The simulation of “connections,” that are implicitly assumed to be constant bit rate (CBR), fails to model the elastic nature of the Internet traffic, that leads to spread congestion in time when information transfers are throttled in the network. On the other hand, packet based simulations often fails to recognize the implicit presence of information flows, that may profit more than randomly distributed traffic from QoS routing. The contribution of the work we present here is the development of a routing evaluation tool that, while being based on a connection level approach and being computationally efficient, allows for the simulation of elastic traffic, where connections open up with a given amount of information to transfer, and close only when they have transmitted it. We model also the behavior of the application (or user) that interrupts a data transfer if he perceives a large degradation of the network performance (starved situation). This features allows us to derive other performance index, such the starvation probability. The remaining part of the paper is organized as follows. Section II gives a brief presentation of the simulation tool, specifically pointing out innovations for elastic traffic management. Section III introduces the routing algorithms used for testing the new evaluation method. Section IV presents some results highlighting the novelty of the method. Section V closes the paper discussing the paper contribution.
II. ANCLES: T HE S IMULATION T OOL ANCLES [10], [11] is a call-based simulator of computer networks. Originally conceived for ATM networks, it has gradually evolved to allow the simulation of connections over IP networks, with a call-level granularity. ANCLES manages the opening and closing of different class of connections, their routing over a specified topology, and, if required, admission control procedures in both a parameter- and measurement-based fashion. ANCLES allows the user to compute a large number of performance indexes, and to present them with different granularity: for each source-destination relation, as well as aggregated for each source, and in network aggregate form. Among the performance that ANCLES is able to obtain, there are call blocking probability, average link utilization, average source throughput, average completion time for best-effort calls. The reader is suggested to refer to [11] for a more detailed description. A. Traffic Generation and CAC The present release of ANCLES provides five different types of call generators, all devoted to the generation of connection oriented traffic. Every request of access to the network is supplied with the identifier of the user selected as destination and with the parameters allowing the call management to be executed. On the basis of this information the routing functions can start looking for a suitable path for the incoming call and the CAC functions can decide about the call acceptance on each hop composing the examined paths. Calls can be associated to individual traffic flows or to MPLS tunnels as desired. The selection of the user to be reached depends on the traffic relations defined in a file, which provide a description of the traffic pattern, i.e., the user probability distribution of requesting a connection to other users. The main source models are: Constant Bit Rate (CBR) sources: characterized by their bit rate and duration; ON-OFF CBR sources: used to model sources that can either be transmitting at their maximum rate, or be idle; Continuously Variable BR sources:that continuously change their bit rate between two fixed bounds; Video sources: whose bit rate can assume only 3 distinct values, according to a suitable probability distribution and simulates the periodic variation due to MPEG encoding; Best Effort (BE): that adapt their transmission bit rate to the current network congestion level as described below. These source models can be grouped into classes, that are characterized by a different set of QoS parameters, can adopt a particular routing, and can be subjected to a given CAC algorithm. The description of routing and CAC algorithm goes beyond the scope of this paper and can be found on ANCLES manual [11].
B. Elastic Traffic Handling Features While the call duration of first four source models is determined a priori according to a probabilistic description, the duration of Best Effort connections can be limited by the amount of data to be transfered, thus modeling real elastic traffic sources. In more detail, a best-effort connection is characterized by a maximum bandwidth (to emulate the limitations introduced by the application, the transport protocol, or the access network) and by the amount of data to transfer. Once the connection is set-up in the network, ANCLES computes the amount of data transferred during the connection lifetime, using a runtime identification of bottleneck links and keeping track of the bandwidth variations, as each connection is affected by others on the path to its destination. Since the complexity of the process of identification of bottleneck links increases with the frequency of new connections’ activation and release, ANCLES implements both the full maxmin fair share algorithm, and a simplified algorithm that that give a good approximation of the maximum fair bandwidth allocation algorithm, while keeping the complexity low. The full algorithm, each time a connection is opened/closed, recomputes the current BE bit rate, using a max-min fair share allocation algorithm. This requires a full recalculation of the bit rate of all calls currently routed in the network. By applying the max-min fair algorithm, the tool provides for an upper bound to the performance, since the max-min fair share represents an ideal working situation for any congestion control protocol that aims at equally dividing resources among users. Instead, whenever a new connection is routed across the network, the simplified algorithm recomputes only the bottlenecks of connections already routed on links of the path crossed by the new connection. If the bottleneck of an existing connection is shifted onto a different link, more bandwidth should become available on the old bottleneck, thus possibly affecting the bottlenecks of other connections, and so on, until the bottlenecks of all connections are recomputed over and over. To avoid such a snowballing effect, the bottleneck recomputation is restricted to the connections routed over the path of the connection that has entered or left the network, and does not propagate to other connection’ bottlenecks. The approximation thus introduced depends on the granularity of connections’ bandwidth with respect to the link capacity (the smaller the granularity, the smaller the perturbation introduced by shifting the bottleneck). It is not possible to state whether this approximation leads to under- or over- estimate performances; however, comparisons with the exact max-min fair algorithm yield results that are almost indistinguishable. Thus, the connection duration depends on the amount of data transferred (which, instead, is an input parameter of the connection). When a new connection starts, ANCLES computes the theoretical finish time by estimating the time it would take to transfer the data if the current available bandwidth (determined by the connection bottleneck) were to be sustained throughout the connection lifetime. Each time the bottleneck of that
connection is shifted elsewhere, the finish time is recomputed, based on the data still to be transferred and on the updated available bandwidth. An additional feature implemented in ANCLES models the closing of starved connections. A connection is defined as ”starved” when the available bandwidth for that connection becomes too small (i.e., smaller than a fraction of the maximum required connection bandwidth). If more than one connection suddenly becomes starved, due to the activation of a new connection, only one is terminated. The closing of that single connection releases some resources that can be exploited by other connections, that can then exit the starved status.
routers of the QoS performances, such as the currently available bandwidth, is modeled in ANCLES using different information propagation models: Perfectly known: each router has the knowledge of the exact QoS parameters at every point in time. Delayed knowledge: each router propopagates to other routers in the network the current value of its QoS parameters. The propagation of the new values can be periodic or artificially triggered by, for example, crossing a threshold. Measured based: as in the delayed approach, but an error can be introduce to model a measure error.
III. ROUTING A LGORITHMS
IV. R ESULTS AND M ETHOD C OMPARISON
The aim of the tool is the evaluation of QoS routing, hence different routing algorithms are implemented in the simulator. Beside the static, hop-count based algorithms, that are not able to cope with the variation of available bandwidth in the network, ANCLES implements several of the recently proposed dynamic, traffic-based routing algorithms. Considering the available bandwidth in each link during the evaluation of the path, these algorithms are able to offer a better choice for the routing of calls with QoS requirements. Here we briefly describe the algorithms that will be used in Section IV to assess the difference between the traditional evaluation method and the one proposed in this paper. There are several other routing algorithms available in ANCLES. We chose to use only three, because the focus of the paper is not on routing algorithms, but on the novel performance evaluation technique for routing strategies in the Internet. Shortest-Path (SP ): for each source-destination pair, the algorithm determines the path with the minimum hop count and routes flows over that path. If two or more “shortest” paths exist, the algorithm would choose one at random and, once and for all, would route all flows over it. The path can be determined using standard path-finding algorithms (i.e. Bellman-Ford’s or Dijkstra’s). This is the routing algorithm commonly used in the current Internet. Minimum-Distance (M D): for each source-destination pair, the path P is chosen which minimizes the following quantity [2]: D(P ) =
X
1
b l2P l
(1)
where bl is the max-min fair bandwidth that is available to a new connection over link l belonging to path P . Widest-Shortest (W S ): for each source-destination pair, the algorithm determines the path with the minimum hop count, similarly to the algorithm, but if there are several paths at the same minimum hop count it breaks ties by choosing, among them, the one with the largest available bandwidth [1]. To test the robustness of the QoS routing algorithm to the information availability, the distribution among the network
In this section we briefly report the simulation results obtained running ANCLES. The network topology was randomly generated using the GT-ITM software [12]1 ; the resulting topology comprises 32 nodes, with an average connectivity degree of 4. Every link has the same capacity (10Mb/s), and there is a best-effort traffic source generator connected to each node, that requests connection with a maximum bandwidth of 1Mb/s; each call requires a bulk data transfer whose size is randomly chosen from an exponential distribution with average 20KB. A uniform traffic pattern is simulated, i.e., when a new call request is generated, the source and the destination are randomly chosen with the same probability. To get accurate results, each simulation was ended when the performance indices were such that the 95% confidence interval was within 5% of the point estimate. The results presented here are obtained using the full algorithm for the identification of the max-min fair share bandwidth. First of all we present a comparison of the results obtained with the classic time-limited best-effort connections with the results obtained with the novel data-limited connection model. Generally, when comparing routing algorithm performance, we are interested in the relative merit with respect to well established algorithm, such as the Shortest Path. Thus as performance indexes, we selected the relative throughput gain obtained using a QoS-aware routing (either the M D or the W S ) algorithm with respect to the SP , i.e., (algo) = (algo)= (SP ), where (algo) is the average throughput measured using the algo routing algorithm evaluated averaging over all the simulated connections. Figure 1 presents the plot of the above defined routing gain obtained with the classical time-based model on the top, and with the new data-based model on the bottom. Results are reported versus the network offered load (measured as the number of calls per second generated by each source). In order to allow a good readability of the plots, both the x and y axes scales are different in the two plots. Obviously the SP curve is constant to 1 in both plots, but this does not mean 1 A companion tool automatically translates the output of GT-ITM to the network description format used by ANCLES
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that the absolute performance of the SP algorithm is the same on the two scenarios. As it can be seen, both plots show that the throughput results obtained are comparable to previous studies [2], [5]; it was already shown that the M D and W S algorithms are able to outperform the SP routing on network with relatively low load, as they are able to exploit the spare bandwidth that is present on the lightly loaded links; they instead provide worse performances when the network becomes overloaded, because of the waste of bandwidth that occurs when the a longer path is selected. Indeed, it must be noted that the performance obtained with the two approaches are quite different. In particular, on the one hand, the classical approach shows a much wider ranges of offered load where the dynamic routing algorithms outperforms the SP algorithm, although the gain is never larger than 15%. On the other hand, the novel and more realistic approach shows that the load range where the dynamic algorithms perform better than the SP is much smaller. Moreover, in this range, the maximum obtained gain is much larger (about 50%), but the transition to the overloaded region where the SP can not be outperformed is much sharp. This might be surprising, but has it rationale in the elastic nature of the traffic model: when
the network is congested, connections are throttled, thus they remain for a longer time in the network, spreading the congestion in time. This phenomenon creates a sort of positive feedback that explains the sharper and earlier transition. These so different behaviors might have a deep impact on the network dimensioning and planning. The new proposed model allows also the evaluation of new performance indices, such as the probability that a call that enters the network is not successfully completed, i.e., the probability that a source aborts the transfer due to starvation (in the results presented here, the starvation level is set to 1/10 of the maximum requested bandwidth), or the “Dilatation Factor”. This latter is defined as the ratio between the average completion time of a connection and it minimum completion time (computed using the source maximum requested bandwidth). Figure 2 plots on the top the starvation probability and on the bottom the average dilatation factor versus the network offered load for the different routing algorithms. The starvation probability measured give us more insight about the performance of different routing algorithms. Indeed, we can see that the starvation probability is smaller when both the W S and M D algorithm are used, suggesting that the network bears a larger number of simultaneous connections, each one obtaining a smaller throughput, but still higher that its starvation limit. Observing the dilatation factor on the lower plot, it can be noted that the W S algorithm degrades its performance very quickly as soon as the offered load is higher than about 4 calls per second, while the M D algorithm performs better than the SP algorithm up to 8 calls per second. This last plot shows that a routing algorithm like the W S one exhibits a sudden transition from the normal operation region where connections end in reasonable time to the congestion region, identified by connection duration nearly ten times larger (the dilatation factor can not be larger than 10, given the starvation threshold we used). V. C ONCLUSION In the paper we briefly described ANCLES, a call level network simulator that allows the user to evaluate network performance efficiently, while using at the same time a complex call model, such the one the is derived from elastic traffic. The focus of the paper is indeed on a new connection model that allows the definition of innovative performance indices for the evaluation of routing algorithm. The new model allows the evaluation of networks where connections have to transfer a given amount of data, and dynamically adapt their sending rate to network congestion, as the Internet, and where users abort a transfer when the performance is perceived to be too low. The presented results show that the new performance evaluation methodology can help the understanding of different QoS routing algorithms. Besides, comparison with traditional evaluation methods (where connection are time limited) highlights significant differences, hinting that tra-
[10] M. Ajmone Marsan, A. Bianco, C. Casetti, C. F. Chiasserini, A. Francini, R. Lo Cigno, M. Munaf`o, “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. 17-18, Feb. 1998, pp. 2165–2185 [11] ANCLES - A Network Call-Level Simulator. URL: http://www1.tlc.polito.it/ancles [12] GT-ITM Georgia Teach-Internetwork Topology Models URL: http://www.cc.gatech.edu/project/gtitm
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ditional models might fail to grab the correct characteristic of the current data networks. R EFERENCES [1] Z. Wang, J. Crowcroft, “QoS Routing for Supporting Resource Reservation”, IEEE JSAC 14(7):1288–1294, Sept. 1996. [2] Q. Ma, P. Steenkiste, and H. Zhang. “Routing High-Bandwidth Traffic in Max-Min Fair Share Networks”, in Proceedings of the ACM SIGCOMM’96, pages 206–217, Stanford, CA, USA, Aug. 1996. [3] D. Cavendish, M. Gerla, “Internet QoS Routing Using the Bellman-Ford Algorithm”, In Proceedings of the Conference on High Performance Networking (HPN98), IFIP, Vienna, Austria, 1998. [4] G. Apostolopoulos, R. Gu´erin, S. Kamat, S. K. Tripathi, “Quality of Service Based Routing: A Performance Perspective”, ACM SIGCOMM’98, Vancouver, Canada, Sept. 1998. [5] C. Casetti, G. Favalessa, M. Mellia, M. Munaf`o, “An Adaptive Routing Algorithm for Best-effort Traffic in Integrated-Services Networks”, 16th International Teletraffic Congress (ITC-16), Edinburgh, UK, June 1999 [6] J.Moy, “Open Shortest Path First Version 2”, RFC 2178, July 1997. [7] Y.Rekhter, T.Li, “A Border Gateway Protocol 4 (BGP-4)”, RFC 1771, March 1995. [8] R. Gu’erin, A. Orda, “QoS-based Routing in Networks with Inaccurate Information”, Proceedings of the IEEE INFOCOM ’97, Kobe, Japan, April 1997 [9] G. Apostolopoulos, R. Gu´erin, S. Kamat, S. K. Tripathi, “Improving QoS Routing Performance Under Inaccurate Link State Information”, 16th International Teletraffic Congress (ITC-16), Edinburgh, UK, June 1999
