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

Traffic Characteristics on 1Gbit/s Access Aggregation Links

Large network operators have thousands or tens of thousands of access aggregation links that they need to manage and dimension properly. Measuring and understanding the traffic characteristics on these type of links are therefore essential. What do the traffic intensity characteristics look like on different timescales from days down to milliseconds? How do the characteristics differ if we compare links with the same capacity but with different type of clients and access technologies? How do the traffic characteristics differ from that on core network links? These are the type of questions we set out to investigate in this paper. We present the results of packet level measurements on three different 1Gbit/s aggregation links in an operational IP network. We see large differences in traffic characteristics between the three links. We observe highly skewed link load probability densities on timescales relevant for buffering (i.e. 10-milliseconds). We demonstrate the existence of large traffic spikes on short timescales (10-100ms) and show their impact on link delay. We also found that these traffic bursts often are caused by only one or a few IP flows

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