Influence of Network Topology on Protocol Simulation
Damien Magoni, Jean-Jacques Pansiot
Corresponding author: Damien MAGONI Ph. D. student LSIIT / Université Louis Pasteur Bureau C446 Pôle API Boulevard Sébastien Brant 67400 ILLKIRCH, France E-mail:
[email protected] Phone: +33 (0)3.88.65.55.38 Fax: +33 (0)3.88.65.55.01 Topics: Performance Evaluation, Simulation Protocol Design and Evaluation Keywords: Network topology, Topology models, Graph generators, Protocol simulation
Influence of Network Topology on Protocol Simulation Damien Magoni, Jean-Jacques Pansiot
Abstract
influence that could bias results by 30% when comparing the traffic concentration in core-based multicast trees and in shortest path multicast trees [16]. Later in 1997, Zegura et al. showed that the average delay ratio of center to shortest path was increased by a factor that could go up to 100% when using transit-stub graphs rather than random graphs [17]. Recently Radoslavov et al. did a thorough study of the impact of topology on protocol design [13]. They studied three well known network topology models (i.e. Waxman, Tiers and ITM) and their influence on four multicast protocol paradigms (i.e. multicast trees, forwarding state aggregation, endsystem multicast and alternate path routing). They reported significant result differences depending on the topology model used. Also in a recent study Palmer et al. evaluated their STORM multicast algorithm with 4 topology models, including PLOD and Waxman [12]. Although the average packet overhead was roughly the same (for a 50-client or above topology) whatever the generator used, the plot of the distribution of the percent of protocol overhead per node was highly tied to the type of generator. This paper is an extension of these previous studies. We examine an oriented multicast routing protocol which is very dependent on the underlying topology and has not been already studied in this context. We also use, in addition to the others, a new topology generator that matches more closely the properties of the Internet and that has not been tested in the previous studies (i.e. BRITE).
Simulation is one of the most widely used techniques for designing network protocols. A simulation framework provides a sandbox where a harmful design flaw can easily be detected and removed. This is done prior to implementation and experimentation in an operational environment as it is easier and cheaper to carry out. However, simulation results can be distorted if the simulation model is unrealistic. In particular the topology model used by a protocol simulation can have a great impact on the results. In this paper we present a comparison of the results of a oriented multicast protocol simulation performed on some of the major topology models currently in use in the network research community.
1. Introduction The aim of this paper is to highlight the impact of network topology on network protocol simulation. The wide use of simulators such as ns [14] or GloMoSim [6] by the scientific commu nity for designing network protocols enforces the need of realistic modeling at all levels. The topology models used in simulators have been quite simple since the beginning of network simulation but today’s computing power makes simulation possible over larger topologies (i.e. graphs). That’s why the use of small graphs following grid-like or random models should be changed in favor of bigger and more realistic graphs.
Concerning network topology models, a well-known early model was defined by Waxman in 1988. This model places the nodes randomly on a plane and then creates links between nodes with a probability depending on the nodes’ euclidean distance [15]. The Waxman model belongs to what we call flat topology class. Circa 1996, two new generators were created, namely Tiers [3] and ITM [17]. Both are based on a structured network creation process designed to match the Internet architecture. The ITM topology model is called transit-stub because it is based on the Autonomous Systems’ structure. It has been widely used in network simulation tools (e.g. it is distributed in ns). Tiers is based on the LAN-MAN-WAN structure of the Internet. Tiers and ITM generators are both belonging to what we call the hierarchical topology class. Recently new generators were created to build graphs that follow the power-law properties of the Internet. Some of them are already available for testing, namely BRITE [11] and Inet2 [7]. These generators belong to the power-law topology class. In our paper we will only deal with the generators of the last two classes.
Section 2 gives an outline of previous studies on the influence of network topology on protocol simulation as well as an overview of the existing topology models. Section 3 presents some properties of the Internet topology and exhibits the characteristics of the topology generators that we will use. Section 4 briefly describes the oriented multicast routing protocol that we will evaluate by simulation. Section 5 shows the influence of the topology models on the protocol simulation results for a typical use of our oriented multicast protocol.
2. Related work The influence of topology on protocol simulation results was already noticed in 1993 by Doar et al. The efficiency of their multicasting algorithms was reduced by 50% when using random graphs rather than hierarchical graphs [4]. In 1994, Wei et al. found that the average node degree of the topology model had an
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3. Network topology
We run the simulation only on the graphs of the last two classes. The flat topology class and the Waxman model in particular has already been widely studied and its drawbacks are well-known. Furthermore, we don’t test Inet2 because this generator has been designed to create AS-level topology graphs.
A network is typically modeled as an undirected graph. The topology of the graph is a description of the way the nodes are connected together. Properties, such as the average node degree and the diameter, give information on a graph topology. For network protocols, the knowledge of the topology of the medium is very important as it directly translates into useful information such as path length and path redundancy.
4. Protocol simulation We want to evaluate the influence of the topology model used on the simulation results of our agent search protocol. As it is not fully defined yet, we will call it an algorithm rather than a protocol through the rest of this paper. This algorithm has been described in a previous study [10]. In short, our algorithm is an improvement over the expanding rings search mechanism. We want to find agents that are located between a given source and destination. We assume that the initiator of the search knows the address of the destination. Packets are multicasted in a controlled way, so that they do not go too far off the shortest path from the initiator to the destination. Each packet contains a range field that indicates how many hops the packet is allowed to do when it is out of the source-destination shortest path.
3.1 Internet topology An accurate knowledge of the topology of real networks is necessary to design graph generators. Recently the topology of Internet has been investigated a lot and new results have been discovered. In particular, some topological properties were found to comply with power-laws. For example, Faloutsos et al. discovered that the node degree distribution of the Internet topology complies with two power-laws [5] (both at the AS-level and at the router-level). The exponents of these powerlaws concisely describe their corresponding distributions. The trees’ part of Internet has also been studied by Magoni et al. who discovered three powerlaws that apply to the size and depth distributions of the trees [9] (at both AS and router levels).
Our agent search algorithm is based on an oriented multicast algorithm that is very sensitive to the underlying network topology. This oriented multicast algorithm has also been described in a previous study [8]. We compare our agent search algorithm to the expanding rings search (ERS) algorithm. The expanding rings search has been described in protocols such as YAM [2] and QoSMIC [1].
3.2. Topology models Table 1 shows the most common topology models currently in use by the research community. As software packages, they are all freely available except PLOD. Class Flat topology Hierarchical topology Power-law topology
Model(s) Waxman Tiers Transit-stub BRITE Inet2 PLOD
Date & references 1988 – [15] 1996 – [3] 1996 – [17] 1999 – [11] 2000 – [7] 2000 – [12]
5. Influence of topology In this section we give the results of the simu lation of the agent search and ERS algorithms for each of the topology model tested. We also explain how we got the results (i.e. how we set the parameters of the generators and the simulator).
Table 1. Topology models
5.1. Simulation parameters
In the flat topology models, the edges are created with a probability depending on the distance of the corresponding nodes. In the hierarchical topology models, subgraphs modeling network parts are first generated by using a flat topology method (as in Transit-Stub) or a spanning tree method (as in Tiers). Then the subgraphs are connected together in a way that enforces a multi-level tree-like structure. In the powerlaw topology models, the edges are distributed to the nodes in a way that matches the skewed node degree distribution of the Internet. This can be done by reverse engineering (as in Inet2 and PLOD) or by the use of preferential connectivity and incremental growth (as in BRITE).
Table 2 gives the parameter settings of the generators. 20 graphs by topology generator have been generated. Each graph has 2000 nodes and contains 1 % of agent nodes. Each algorithm (ours and ERS) has been tested on 500 different source-destination pairs, for four given source-destination distances, for each graph. So for a given source-destination distance, each algorithm has been tested 10000 times. The simulations have been carried out with the network manipulator software. It is a static network simulator that we have implemented in our laboratory. It is static because it does not take into account any temporal aspect of the communications. The results of these simulations have been merged to give average results.
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We made these simulations for source-destination distances of 4, 8, 12 and 16 hops. For the ERS, the TTL is increased by 2 while no optimal agent is found, starting at 1 up to a maximum value of 7 (i.e. 1, 3, 5, 7). For our algorithm, the range is increased by 1 from 1 to 4. It is possible for the algorithms not to find any optimal agent because they have to stop their search at some point. Generator Transit-Stub
Tiers
BRITE
Parameters #transits in top #nodes/transit #stubs/transit #nodes/stub Edge method Alpha Beta NW NM NL SW SM SL RW HS LS m Node placement PC IG
Value(s) 20 4 2 12 Waxman 0.5 0.5 1 220 0 680 6 0 6 1000 100 1, 2 Random Only Active
Figure 1. Bandwidth Ratio Figure 2 shows the number of optimal agent hit ratio. For example, the Transit-Stub has a value of 1.5. This means that our algorithm finds 50% more optimal agents than the ERS algorithm. The variations between the topology ratio values are of lesser importance than in the previous figure but they are still significant.
Table 2. Parameter Settings for the Generators
5.2. Simulation results In this section, we present the simulation results of four variables of interest. These variables are the bandwidth usage, the number of optimal agent found, the number of attempts to find an optimal agent and the efficiency. For each variable, we calculate its value by using our agent search algorithm and by using the ERS algorithm. We divide the former value by the latter to obtain a ratio that enables an easier comparison. Furthermore, as we carried out tests on four different distances, we calculate the average of the four ratio values. So we have one ratio value left for each of the generators used.
Figure 2. Optimal Agent Hit Ratio Figure 3 shows the average number of attempts needed to find at least one optimal agent. The Transit-Stub value of 0.6 means that our algorithm needs on average 40% less attempts to find at least one optimal agent than the ERS algorithm. Here too, the values depend on the topology model.
Figure 1 shows the bandwidth ratio given by each of the topology models. For example, the Transit-Stub topology model has a value of 1.4. This means that our agent search algorithm creates on average 40% more packets than the ERS algorithm. We can clearly see that there are big differences between the results and that they depend on the type of graphs used for the simulation (i.e. the kind of topology model used). We can already say that the topology model used has a big influence on the results. The biggest gap is a -68% difference that can be found between the Tiers ratio and the BRITE 2 (i.e. with m = 2) ratio.
Figure 3. Attempt Number Ratio We have defined a ratio called efficiency to be able to assess the algorithms’ performances. The efficiency is equal to the number of optimal agents found divided by the number of packets emitted in the network. As usual we divide the efficiency of our algorithm by the efficiency of the ERS algorithm to obtain an efficiency
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[5] M. Faloutsos, P. Falouts os, C. Faloutsos. On power-law relationships of the Internet topology, in Proc. of ACM SIGCOMM’99, January 1999. [6] GloMoSim. http://pcl.cs.ucla.edu/projects/glomosim/. [7] C. Jin, Q. Chen, S. Jamin. Inet: Internet Topology Generator, Tech. Rep. CSE-TR-433-00, 2000. [8] D. Magoni, J.-J. Pansiot. Algorithm for an Oriented Multicast Routing Protocol, submitted for publication at IEEE ICN’01. [9] D. Magoni, J.-J. Pansiot. Analysis of the Autonomous System Network Topology, submitted for publication at ACM Computer Communication Review. [10] D. Magoni, J.-J. Pansiot, D. Pate, D. Grad. Agent Search by Oriented Multicast, in Proc. of ACIS SNPD’00, Reims, France, May 2000. [11] A. Medina, I. Matta, J. Byers. On the Origin of Power Laws in Internet Topologies, ACM Computer Communication Review, 2000. [12] C. Palmer, G. Steffan. Generating Network Topologies That Obey Power Laws, submitted at IEEE GlobeCom’01. [13] P. Radoslavov, H. Tangmunarunkit, H. Yu, R. Govindan, S. Shenker, D. Estrin. On Characterizing Network Topologies and Analyzing Their Impact on Protocol Design, Tech. Rep., USC, 2000 [14] The VINT projet. network simulator (ns-2), http://www.isi.edu/nsnam/vint/. [15] B. Waxman. Routing of multipoint connections, IEEE Journal on Selected Areas in Communications, 6(9):16171622, December 1988. [16] L. Wei, D. Estrin. A Comparison of Multicast Trees and Algorithms, in Proc. of IEEE Infocom’94, Toronto, Canada, June 1994. [17] E. W. Zegura, K. L. Calvert, M. J. Donahoo. A quantitative comparison of graph-based models for internetworks, IEEE/ACM Transactions on Networking, 5(6):770-783, December 1997.
ratio. Figure 4 shows the efficiency ratio of each topology model. They are all greatly different. Tiers favors the ERS over our algorithm, while the others favor our algorithm. The difference between the Tiers ratio and the BRITE 1 ratio reaches +198%. The performance of our algorithm over the ERS algorithm is heavily influenced by the topology model used.
Figure 4. Efficiency Ratio
6. Conclusion We showed on a particular multicast algorithm example that the kind of topology model used for protocol simulation has a crucial impact on the simulation results. A protocol performance could be favored by a topology model or disfavored by another. This situation can lead researchers to avoid using simulation for design protocol. Perhaps the best way to draw acceptable conclusions would be to use a topology model that is closest to the real network topology where the new protocol will be deployed. For IP protocols, and routing protocols in particular, the use of the most recent topology models (i.e. of the power-law topology class) should be recommended. However, there is still room for improvement in creating a topology model that would match the Internet topology. Indeed, even the most up-to-date generators do not take into account all of the Internet topology properties that have been newly discovered. Simulation is such an important tool in network research that it can not be neglected because of a lack of realistic topology generators. It is clear that new enhanced topology models will appear in the near future to reduce the bias owing to topology on protocol simulation results.
7. References [1] A. Banerjea, M. Faloutsos, R. Pankaj. QoSMIC : Quality of Service sensitive Multicast Internet protoCol, in Proc. of ACM SIGCOMM’98, Vancouver, BC, September 1998. [2] K. Carlberg, J. Crowcroft. Building Shared Trees Using a One-to-Many Joining Mechanism, ACM Computer Communication Review, pp. 5-11, January 1997. [3] M. Doar. A better model for generating test networks, in Proc. of IEEE GlobeCom’96, November 1996. [4] M. Doar, I. Leslie. How bad is naïve multicast routing?, in Proc. of IEEE Infocom’93, pp. 82-89, 1993.
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