Characteristics of WAP traffic

This paper considers the characteristics of Wireless Application Protocol (WAP) traffic. We start by constructing a WAP traffic model by analysing the behaviour of users accessing public WAP sites via a monitoring system. A wide range of different traffic scenarios were considered, but most of these scenarios resolve to one of two basic types. The paper then uses this traffic model to consider the effects of large quantities of WAP traffic on the core network. One traffic characteristic which is of particular interest in network dimensioning is the degree of self-similarity, so the paper looks at the characteristics of aggregated traffic with WAP, Web and packet speech components to estimate its self-similarity. The results indicate that, while WAP traffic alone does not exhibit a significant degree of self-similarity, a combined load from various traffic sources retains almost the same degree of self-similarity as the most self-similar individual source

Characterization of Web server workload

Realistic and formal mathematical description of web-server workload forms a fundamental step in the design of synthetic workload generators, capacity planning and accurate predictions of performance measures. In this thesis we perform detailed empirical analysis of the web workload by analyzing access logs of nine web-servers. Unlike most previous work that focused on request-based workload characterization, we analyze both request and session characteristics. We perform rigorous statistical analysis to determine the self-similarity of web traffic and heavy-tailedness of the distribution of different session parameters. Our analysis shows that web traffic is self-similar and the degree of self-similarity is proportional to the workload intensity. To increase the confidence in our analysis we use several methods for estimating the degree of self-similarity and heavy-tailedness. Additionally we point out specific problems associated with these methods. Finally, we analyze the impact of robots sessions on the heavy-tailedness of the distribution

Reasoning about Social Semantic Web Applications using String Similarity and Frame Logic

Social semantic Web or Web 3.0 application gained major attention from academia and industry in recent times. Such applications try to take advantage of user supplied meta data, using ideas from the semantic Web initiative, in order to provide better services. An open problem is the formalization of such meta data, due to its complex and often inconsistent nature. A possible solution to inconsistencies are string similarity metrics which are explained and analyzed. A study of performance and applicability in a frame logic environment is conducted on the case of agent reasoning about multiple domains in TaOPis - a social semantic Web application for self-organizing communities. Results show that the NYSIIS metric yields surprisingly good results on Croatian words and phrases

Investigating self-similarity and heavy tailed distributions on a large scale experimental facility

After seminal work by Taqqu et al. relating self-similarity to heavy tail distributions, a number of research articles verified that aggregated Internet traffic time series show self-similarity and that Internet attributes, like WEB file sizes and flow lengths, were heavy tailed. However, the validation of the theoretical prediction relating self-similarity and heavy tails remains unsatisfactorily addressed, being investigated either using numerical or network simulations, or from uncontrolled web traffic data. Notably, this prediction has never been conclusively verified on real networks using controlled and stationary scenarii, prescribing specific heavy-tail distributions, and estimating confidence intervals. In the present work, we use the potential and facilities offered by the large-scale, deeply reconfigurable and fully controllable experimental Grid5000 instrument, to investigate the prediction observability on real networks. To this end we organize a large number of controlled traffic circulation sessions on a nation-wide real network involving two hundred independent hosts. We use a FPGA-based measurement system, to collect the corresponding traffic at packet level. We then estimate both the self-similarity exponent of the aggregated time series and the heavy-tail index of flow size distributions, independently. Comparison of these two estimated parameters, enables us to discuss the practical applicability conditions of the theoretical prediction

Fitting World-Wide Web Request Traces with the EM-Algorithm

In recent years, several studies have shown that network traffic exhibits the property of self-similarity. Traditional (Poissonian) modelling approaches have been shown not to be able to describe this property and generally lead to the underestimation of interesting performance measures. Crovella and Bestavros have shown that network traffic that is due to World Wide Web transfers shows characteristics of self-similarity and they argue that this can be explained by the heavy-tailedness of many of the involved distributions. Considering these facts, developing methods which are able to handle self-similarity and heavy-tailedness is of great importance for network capacity planing purposes. In this paper we discuss two methods to fit hyper-exponential distributions to data sets which exhibit heavy-tails. One method is taken from the literature and shown to fall short. The other, new method, is shown to perform well in a number of case studies

In Defense of Wishful Thinking: James, Quine, Emotions, and the Web of Belief

What is W. V. O. Quine’s relationship to classical pragmatism? Although he resists the comparison to William James in particular, commentators have seen an affinity between his “web of belief” model of theory confirmation and James’s claim that our beliefs form a “stock” that faces new experience as a corporate body. I argue that the similarity is only superficial. James thinks our web of beliefs should be responsive not just to perceptual but also to emotional experiences in some cases; Quine denies this. I motivate James’s controversial view by appealing to an episode in the history of medicine when a researcher self-experimented by swallowing a vial of bacteria that at the time had not been studied in much detail. The researcher’s commitment to his own as-yet untested hypothesis was based in part on emotional considerations. Finally, I argue that Quine’s insistence that emotions can never be relevant to adjusting our web of belief reflects a tacit holdover of one of logical positivism’s crucially anti-pragmatist commitments—that philosophy of science should focus exclusively on the context of justification, not the context of discovery. James’s emphasis on discovery as a (perhaps the) crucial locus for epistemological inquiry is characteristic of pragmatism in general. Since Quinean epistemology is always an epistemology of justification, he is not happily viewed as a member of the pragmatist tradition

Network TraÆc behaviour in switched ethernet systems

Measurements on a high-performance switched Ethernet system are presented that reveal new insights into the statistical nature of le server and web server traÆc. Both le sizes and data requested from the web server are shown to match well a truncated Cauchy distribution. This is a distribution with heavy tails similar in nature to the commonly used Pareto distribution but with a much better t over smaller le/request sizes. We observe self similar characteristics in the traÆc at both servers and also at a CPU server elsewhere on the network. TraÆc from this server is predominantly targeted at the le and web servers, suggesting that self-similar properties at one point on a network are being propagated to other points. A simple simulation model of an isolated server is presented with Poisson arrivals and service (packet transmission) demands with the same Cauchy distribution as we observed. The departure process is shown to follow a power law and the corresponding power spectrum is shown to match extremely well that of the observed traÆc. This supports the suggested link between le/request size distribution and self-similarity. The resulting implication that self similarity and heavy tails are primarily due to server-nodes, rather than being inherent in o ered traÆc, leads to the possibility of using conventional queueing network models of performance. This idea is further supported by an additional simulation experiment and suitable models are proposed