# Syslog Performance: Data Modeling and Transport

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4 Content length [bytes] Fig. 2: The normal and content length cumulative distributions Time [s] Time [s] Fig. 3: The inter-arrival time density distribution for the whole sample (big graph) and the Wireshark sample (small graph). exponential distribution. The exponential distribution is given by the probability density function f(x) = λe λx (2) where λ > is the rate parameter of the distribution. Using maximum likelihood estimation, the rate parameter of (2) can be estimated with the inverse of the mean of the interarrival time distribution. Thus, for the Wireshark sample, having the finest time granularity, the mean of the inter-arrival time distribution is.73 s, giving λ =.36 s. To show how the exponential distribution fits to the inter-arrival time distribution, the corresponding two cumulative distributions are plotted in Fig. 4. The maximum error between the two cumulative distributions is 9.7 % and the mean error is. % Time [s] Fig. 4: The exponential and interarrival time cumulative distributions. C. Important Message Distribution The number of important and normal messages per day for our syslog database sample is plotted in Fig. 5. From this sample, the fraction of important messages is 7.6 %. In [2], the authors used two syslog database samples. The first sample was captured during heavy loads and contained % of important messages, while the second sample was captured during light loads and contained.4 %. Hence, the authors used in their experiments the assumption that during heavy loads the fraction of important messages increases. We examine the spikes in our database sample for similar trends. During heavy loads, the largest load spike in Fig. 5 has a fraction of 5.3 %, at day 255, while the second largest load spike has a fraction of 2.9 %, at day 87. Thus, in our system we cannot draw the conclusion that heavier loads increase the fraction of important messages, but consistent with [2] we can see that the fraction of important messages may vary greatly. IV. EXPERIMENTAL EVALUATION In this section, we first describe the experiment setup that we use to evaluate the performance of PR- for transporting syslog messages, followed by our experiment results. A. Experiment Setup We adopt a single bottleneck, emulation based experiment for the performance evaluations. We use three computers. All of these three machines have the same hardware configuration of 4 GB RAM and an Intel Core 2 duo processor (2.6 GHz). The Dummynet traffic shaper [9] is set up in the middle machine to introduce physical delays and bandwidth limitations in the network. Both end machines are configured with FreeBSD 8.. Based on the findings in Section III, we have created a syslog message generator application. The lengths of the generated messages are drawn from a normal

5 865 normal messages. Additionally, we perform each experiment with 3 repetitions to allow for 95 % confidence intervals. The network related parameters are summarized in Table. 855 TABLE I: Network related parameters. Number of messages Normal messages Important messages Parameter Value(s) One way Delay: ms Queue size: 38 KB Bandwidth: Mbps (Up and Down) # of background flows: 5 and Time [day] Fig. 5: Fraction of important and normal messages per day. distribution of mean 85 and standard deviation 3. Besides, since we see an exponential distribution of the inter-arrival times of syslog messages in the traces, our application creates messages according to a Poisson arrival process. We do not, however, use the obtained mean for inter-arrival times in the experiment, since we are in this case interested to see how the evaluated protocols behave during congestion. To avoid having to use an excessive amount of background flows or a very small bandwidth to create congestion, we instead use a shorter mean inter-arrival time of 2 ms. Our application generates two main types of messages: important and normal. According to our analysis in section III, we use several distributions of important messages starting from % to 25 %. In this experiment, we use a timed reliability based PR- policy. In such a policy, a certain lifetime is given for every application message. Upon expiration of this lifetime, PR- does not consider any (re)transmission of this message. Our expectation is that even under heavy congestion in the network, important messages should reach the receiver whereas normal messages have a smaller or no chance. We use a time-to-live (TTL) value of 5, ms for important messages and a TTL of 2 ms for normal messages. We use the same application settings on top of various transport services such as TCP,, and PR-. Both and PR- use unordered delivery, since syslog generates semantically independent messages. Additionally, in each run, syslog messages are sent from the server to the client. In the experiment, we have several competing background flows in the network. These are greedy TCP flows. In consequence, a foreground flow being any of the evaluated protocols, TCP,, or PR-, must compete with the background flows for network resources. We vary the number of background flows to differ the congestion level in the network. We measure average message delay for both important and B. Experiment Results Fig. 6 shows the performance of PR- along with and TCP for different number of background flows. Since the PR- graphs for important and normal messages visually overlap, we only put the graphs for important messages. In both cases, although PR- performs better than TCP for smaller fractions of important messages, it surprisingly performs worse than. PR- is expected to outperform the fully reliable, since it can ignore retransmission of normal messages if they are lost. Two factors have been changed from our previous work; the network settings and the distribution of message sizes. Hence, we perform another experiment using the same synthetic workload that we used before in our present network settings to isolate the phenomena that is influencing the PR- results most. We use a fixed message size of 25 bytes and keep the same average send rate as we used in the first experiment. In addition, we keep the number of background flows to 2. The results from this experiment are given in Fig. 7. In this figure, a subgraph is included to distinguish the results of PR- from. Here we see that PR- outperforms both TCP and by prioritizing important messages over normal messages during (re)transmission. This shows that the message size distribution has a major influence on the PR- results. Based on a detailed analysis of the trace files from our experiments, we have identified that during loss recovery, the existing forward tsn mechanism becomes inefficient. For our scenario where the message size distribution is normal and the mean is not very large, we identify two cases. Firstly, as each message can be of different size and can be placed alone in a packet, the messages have different loss probabilities when the byte based queue at the network is full; in consequence, this can produce a non consecutive loss pattern. In this case, the forward tsn mechanism cannot tell a receiver to forward the cumulative TSN across several TSNs or messages. Instead, it does it separately for individual lost messages, since the delivered message(s) must be cumulatively acknowledged before the forward tsn for the next lost message can be transmitted. On the contrary, fully reliable can retransmit multiple messages together, and becomes faster. Secondly, if there is a backlog created in the send buffer at the transport layer, many messages can be bundled into a single packet, since the mean of the message size distribution

6 Average delay [s] TCP Average delay [s].5.5 TCP Average delay [sec] (a) 5 background flows Fig. 7: Average delay performance using a fixed message size of 25 bytes with 2 background flows. Average delay [s] TCP (b) 2 background flows Fig. 6: Average delay performance for different important message distributions. is quite small. As a result, when even a single packet is lost, many consecutive messages are lost. In such a case, when a single packet is lost, and if many messages are bundled into that packet, we loose many messages with several importance levels. Thus, if all the messages are normal, then a forward tsn chunk can tell a receiver to forward the cumulative ACK point across all these messages. As a result, PR- can be faster than by ignoring to retransmit messages. However, when we have a mixture of reliable and unreliable messages, the forward tsn mechanism becomes slow. This is due to the fact that it only sends a forward tsn chunk to the receiver for an unreliable lost message when the preceding reliable message is confirmed to be delivered. Hence, PR- suffers from a considerable delay. This is also true when we have a fixed message size of 25 bytes; however, the resulting delay is limited, since at most five messages can be bundled into a single packet. V. CONCLUSION In this paper, we first model the characteristics of syslog data using real traces from an operational network. Then, we investigate the performance of PR- for transporting syslog messages using the derived model in a network scenario where multiple flows compete for network resources. However, unlike what we have shown in our previous work [8], PR- noticeably performs worse than. After a detailed analysis of the traces from our experiments, we have identified that the existing forward tsn mechanism in PR- becomes inefficient if an application generates message sizes with small mean according to a normal distribution. We are currently working on improving the forward tsn mechanism and aim to implement and evaluate our solution. Besides, we are planning to obtain syslog traces from a larger network to further understand the characteristics of syslog data. ACKNOWLEDGMENT The work was carried out within the Compare Business Innovation Centre phase 2 project, funded partly by the European Regional Development Fund. REFERENCES [] J. Postel, RFC 364: The BSD Syslog protocol, August, 2. [2] J. Postel, RFC 768: User Datagram Protocol (UDP), August, 98. [3] D. New and M. Rose, RFC 395:Reliable Delivery for syslog, November, 2. [4] Syslog New Generation (Syslog-ng). network-security/syslog-ng/, visited February 27, 2. [5] J. Postel, RFC 793: Transmission control protocol, September, 98. [6] F. Miao et al., RFC 5425: Transport Layer Security (TLS) Transport Mapping for Syslog, March 29. [7] J. Salowey et al., RFC 62: Datagram Transport Layer Security (DTLS) Transport Mapping for Syslog, October, 2.

7 [8] M. Rajiullah, A. Brunstrom, and S. Lindskog, Priority Based Delivery of PR- Messages in a Syslog Context, in International Workshop on Autonomic Networking and Self-Management in the Access Networks (SELFMAGICNETS 2), 2. [9] R. Stewart, Q. Xie, and K. Morneault, RFC 496: Stream control transmission protocol, September, 27. [] R. Stewart, M. Ramalho, Q. Xie, and M. Tuexen, RFC 3758: Stream Control Transmission Protocol () Partial Reliability Extension, may, 24. [] R. Gerhards et al., RFC 5424: The syslog Protocol, March, 29. [2] G. D. Marco et al., as a transport for SIP: a case study, In 7th World Multiconference on Systemics, Cybernetics and Informatics (SCI), pp , Orlando, FL, USA, July, 23. [3] T. Maeda, M. Kozuka, and Y. Okabe, Reliable Streaming Transmission Using PR-, in Ninth Annual International Symposium on Applications and the Internet, SAINT 9, pp , IEEE, 29. [4] H. Sanson, A. Neira, L. Loyola, and M. Matsumoto, PR- for real time H. 264/AVC video streaming, in The 2th International Conference on Advanced Communication Technology (ICACT), vol., pp , IEEE, 2. [5] H. Wang, Y. Jin, W. Wang, J. Ma, and D. Zhang, The performance comparison of PR-, TCP and UDP for MPEG-4 multimedia traffic in mobile network, in International Conference on Communication Technology Proceedings, ICCT, vol., pp , IEEE, 23. [6] M. Molteni and M. Villari, Using with partial reliability for MPEG-4 multimedia streaming, in European BSD Conference, 22. [7] S. Kim, S. Koh, and Y. Kim, Performance of for IPTV Applications, in The 9th International Conference on Advanced Communication Technology, vol. 3, pp , IEEE, 27. [8] X. Wang and V. Leung, Applying PR- to transport SIP traffic, in Global Telecommunications Conference, GLOBECOM 5, vol. 2, pp. 5 78, IEEE, 26. [9] L. Rizzo, Dummynet: a simple approach to the evaluation of network protocols, ACM SIGCOMM Computer Communication Review, vol. 27, no., pp. 3 4, 997. [2] H. Tsunoda et el., A Prioritized Retransmission Mechanism for Reliable and Efficient Delivery of Syslog Messages, in Proceedings of Seventh Annual Communication and Services Research Conference, pp , Washington, DC, USA, 29.

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