Time Based Traffic Policing and Shaping Algorithms on Campus Network Internet Traffic

This paper presents the development of algorithm on Policing and Shaping Traffic for bandwidth management which serves as Quality of Services (QoS) in a Campus network. The Campus network is connected with a 16 Mbps Virtual Private Network line to the internet Wide Area Network. Both inbound and outbound real internet traffic were captured and analyzed. Goodness of Fit (GoF) test with Anderson-darling (AD) was fitted to real traffic to identify the best distribution. The Best-fitted Cumulative Distribution Function (CDF) model was used to analyze and characterized the data and the parameters. Based on the identified parameters, a new Time Based Policing and Shaping algorithm have been developed and simulated. The policing process drops the burst traffic, while the shaping process delays traffic to the next time transmissions. Mathematical model to formulate the controlled algorithm on burst traffic with selected time has been derived. Inbound traffic threshold control burst was policed at 1200 MByte (MB) while outbound traffic threshold was policed at 680 MB in the algorithms. The algorithms were varied in relation to the identified Weibull parameters to reduce the burst. The analysis shows that the higher shape parameter value that relates to the lower burst of network throughput can be controlled. This research presented a new method for time based bandwidth management and an enhanced network performance by identifying new traffic parameters for traffic modeling in Campus network

On the Distribution of Traffic Volumes in the Internet and its Implication

Getting good statistical models of traffic on network links is a well-known, often-studied problem. A lot of attention has been given to correlation patterns and flow duration. The distribution of the amount of traffic per unit time is an equally important but less studied problem. We study a large number of traffic traces from many different networks including academic, commercial and residential networks using state-of-the-art statistical techniques. We show that the log-normal distribution is a better fit than the Gaussian distribution commonly claimed in the literature. We also investigate a second heavy-tailed distribution (the Weibull) and show that its performance is better than Gaussian but worse than log-normal. We examine anomalous traces which are a poor fit for all distributions tried and show that this is often due to traffic outages or links that hit maximum capacity. We demonstrate the utility of the log-normal distribution in two contexts: predicting the proportion of time traffic will exceed a given level (for service level agreement or link capacity estimation) and predicting 95th percentile pricing. We also show the log-normal distribution is a better predictor than Gaussian or Weibull distributions

On the Distribution of Traffic Volumes in the Internet and its Implications

In this edition of the Voice, the College’s Career Planning Placement Service offers a variety or workshops include one on life planning. Wooster Chief of Security and Dr. Startzman of the campus wellness center, speak to students on the topic of rape and safety at the College. The Wooster Board of Trustees begins the process to select a new president of the College of Wooster. The Art Center offers classes on quilting, plants, printmaking, drawing, and other artistic mediums, to students for eight weeks. Additionally, an article discusses the, then up and coming, Bicentennial of the United States.https://openworks.wooster.edu/voice1971-1980/1131/thumbnail.jp

From Data Processing to Distributional Modelling of Traffic Measurements

This thesis is motivated by the need to analyse measured traffic data from networks. It develops and applies statistical methods to characterize and to model such data. The application areas are related to teletraffic and telecommunication networks, vehicular traffic and road/street networks, and Internet of Things applications. The research is based on four scientific publications, augmented with the statistical framework and theoretical development included in this summary. From the applications' point of view, the addressed research problems diverge on the types of the engineering problems, while from the statistical point of view, they share common theoretical methods. The application problems are: i) to study whether a Gaussian process is a feasible model for aggregated Internet traffic, ii) to obtain aggregated flow level models for flow sizes, flow durations and their bivariate joint distribution, iii) to deduce vehicular traffic routes from correlated counts of vehicles that are observed at different locations of a street network, and iv) to develop a data reduction algorithm that works with limited computational capacity and can be deployed by Internet of Things applications. This summary provides the statistical framework that combines the developed and applied methodologies and emphasizes their common features. Rigorous mathematical proofs are given for certain less-known, possibly novel, results about mutual information of pairs of order statistics, and a convergence result related to simultaneous estimation of several quantiles. These were used in the publications or, alternatively, bring new statistical insight to the methods that were used in the publications.Tutkimuksen motiivina on ollut löytää tilastollisia menetelmiä liikennedatan analysointiin. Liikennedata käsittää tässä sekä tieto- että ajoneuvoliikenteestä mitattua, liikenteen määrää kuvaavaa dataa. Menetelmiä kehitetään liikenteen ominaisuuksien karakterisointiin ja jakaumien mallintamiseen. Tutkimus sisältää neljä julkaisua sekä yhteenveto-osuuden. Julkaisuissa käsitellyt sovellukset ovat kaikki hyvin erilaisia, mutta niiden tilastollisessa lähestymistavassa on paljon yhteisiä piirteitä. Julkaisuissa käsitellyt tilastolliset ongelmat ovat seuraavat. Ensimmäisessä julkaisussa tutkitaan, soveltuuko Gaussinen prosessi aggregoidun eli monesta yksittäisestä liikennevirrasta muodostetun yhdistetyn liikennevirran malliksi. Jo usean vuosikymmenen ajan on tiedetty, että tietoliikenteen yhdistäminen ei käyttäydy tilastollisessa mielessä yhtä hyvin kuin esimerkiksi puheliikenteen yhdistäminen. Yhdistetyn tietoliikenteen purskeisuus ei lievene yksittäisten liikennevirtojen määrän kasvaessa vaan purskeisuus näkyy useissa eri aikaskaaloissa ja aikasarjana siinä esiintyy pitkän aikavälin riippuvuuksia. Julkaisun menetelmät sisältävät keinoja aikasarjan stationaarisuuden ja normaalijakaumaoletuksen tutkimiseen tällaisissa tilanteissa. Toisessa julkaisussa tutkitaan mobiilidatayhteyksien ominaisuuksia, joita voidaan mitata aggregoidusta liikennevirrasta rekonstruoimalla yksittäisiä yhteyksiä. Niitä ovat verkosta ladattujen tiedostojen kokojakauma, latauksiin kuluneen ajan jakauma ja näiden kahden suureen yhteisjakauma. Lisäksi mallinnetaan näiden kahden suureen osamäärän jakaumaa, joka kuvastaa yksittäisen mobiilidatakäyttäjän kokemaa keskimääräistä tiedonsiirtonopeutta. Erona tavanomaisempiin menetelmiin mitata käyttäjän saamaa tiedonsiirtonopeutta on se, että julkaisun menetelmillä sitä voidaan mitata aggregoidusta liikennevirrasta eli verkon sisältä. Julkaisussa myös tutkitaan kokojakauman paksuhäntäisyyttä. Kolmas julkaisu käsittelee autoliikenteen mallintamista. Esimerkiksi liikennevalojen yhteydessä on usein sensoreita, jotka havaitsevat ohi ajavat kulkuneuvot. Sensoreiden tuottamaa dataa voidaan jalostaa mittaamaan kulkuneuvojen lukumääriä esimerkiksi 15 minuutin mittaisilla aikaväleillä. Julkaisun menetelmillä tällaista dataa voidaan hyödyntää reaaliajassa esimerkiksi laskemalla ennuste luottamusväleineen meneillään olevan tai tulevan 15 minuutin aikavälin liikennemäärälle. Neljäs julkaisu liittyy esineiden ja asioiden Internetiin. Siinä esitellään algoritmi, jolla voidaan tiivistää esimerkiksi sensorin tuottamaa numeerista mittausdataa jakaumamuotoon. Algoritmin tarvitsemat laskennalliset resurssit, prosessoriaika ja muistin tarve ovat hyvin pienet. Tiivistäminen on mielekästä esimerkiksi tilanteissa, joissa mittauksien tekeminen on käytettävissä olevien resurssien kannalta halpaa, mutta tiedonsiirto kallista. Algoritmia voidaan soveltaa myös liikennemäärien mittaamiseen. Yhteenveto-osuudessa käsitellään edellä mainituissa julkaisuissa kehitettyjen tilastollisten menetelmien yhteistä teoriapohjaa. Teoriapohjaan kuuluvat järjestystunnusluvut ja kvantiilit, moniulotteiset jakaumat sekä aikasarjan stationaarisuuteen ja autokorrelaatioihin liittyvä teoria. Yhteenveto-osuus sisältää myös vähemmän tunnettuja, mahdollisesti uusia matemaattisia tuloksia jotka liittyvät järjestystunnuslukujen sisältämän keskinäisen informaation määrään sekä usean kvantiilin yhtäaikaisen estimoinnin ongelmiin, kun estimointi tapahtuu samasta numeerisesta datavirrasta

Linking network usage patterns to traffic Gaussianity fit

Gaussian traffic models are widely used in the domain of network traffic modeling. The central assumption is that traffic aggregates are Gaussian distributed. Due to its importance, the Gaussian character of network traffic has been extensively assessed by researchers in the past years. In 2001, researchers showed that the property of Gaussianity can be disturbed by traffic bursts. However, assumptions on network infrastructure and traffic composition made by the authors back in 2001 are not consistent with those of today's networks. The goal of this paper is to study the impact of traffic bursts on the degree of Gaussianity of network traffic. We identify traffic bursts, uncover applications and hosts that generate them and, ultimately, relate these findings to the Gaussianity degree of the traffic expressed by a goodness-of-fit factor. In our analysis we use recent traffic captures from 2011 and 2012. Our results show that Gaussianity can be directly linked to the presence or absence of extreme traffic bursts. In addition, we also show that even in a more homogeneous network, where hosts have similar access speeds to the Internet, we can identify extreme traffic bursts that might compromise Gaussianity fit

Understanding the characteristics of Internet traffic and designing an efficient RaptorQ-based data transport protocol for modern data centres

This thesis is the amalgamation of research on efficient data transport protocols for data centres and a comprehensive and systematic study of Internet traffic, which came as a result of the need to understand traffic patterns and workloads in modern computer networks. The first part of the thesis is on the development of efficient data transport pro- tocols for data centres. We study modern data transport protocols for data centres through large scale simulations using the OMNeT++ simulator. We developed and experimented with an OMNeT++ model of NDP. This has led to the identification of limitations of the state of the art and the formulation of research questions with respect to data transport protocols for modern data centres. The developed model includes an implementation of a Fat-tree topology and per-packet ECMP load bal- ancing. We discuss how we integrated the model with the INET Framework and validated it by running various experiments that test different model parameters and components. This work revealed limitations of NDP with respect to efficient one-to-many and many-to-one communication in data centres, which led to the de- velopment of SCDP, a novel and general-purpose data transport protocol for data centres that, in contrast to all other protocols proposed to date, natively supports one-to-many and many-to-one data communication, which is extremely common in modern data centres. SCDP does so without compromising on efficiency for short and long unicast flows. SCDP achieves this by integrating RaptorQ codes with receiver-driven data transport, in-network packet trimming and Multi-Level Feed- back Queuing (MLFQ); (1) RaptorQ codes enable efficient one-to-many and many- to-one data transport; (2) on top of RaptorQ codes, receiver- driven flow control, in combination with in-network packet trimming, enable efficient usage of network re- sources as well as multi-path transport and packet spraying for all transport modes. Incast and Outcast are eliminated; (3) the systematic nature of RaptorQ codes, in combination with MLFQ, enable fast, decoding-free completion of short flows. We extensively evaluated SCDP in a wide range of simulated scenarios with realistic data centre workloads. For one-to-many and many-to-one transport sessions, SCDP performs significantly better than NDP. For short and long unicast flows, SCDP performs equally well or better compared to NDP. In the second part of the thesis, we extensively study Internet traffic. Getting good statistical models of traffic on network links is a well-known, often-studied problem. A lot of attention has been given to correlation patterns and flow duration. The distribution of the amount of traffic per unit time is an equally important but less studied problem. We study a large number of traffic traces from many different networks including academic, commercial and residential networks using state-of-the-art statistical techniques. We show that the log-normal distribution is a better fit than the Gaussian distribution. We also investigate a second, heavy- tailed distribution and show that its performance is better than Gaussian but worse than log-normal. We examine anomalous traces which are a poor fit for all tested distributions and show that this is often due to traffic outages or links that hit maximum capacity. Stationarity tests showed that the traffic is stationary at some range of aggregation times. We demonstrate the utility of the log-normal distribution in two contexts: predicting the proportion of time traffic will exceed a given level (for link capacity estimation) and predicting 95th percentile pricing. We also show the log-normal distribution is a better predictor than Gaussian orWeibull distributions