Burstiness predictions based on rough network traffic measurements
نویسندگان
چکیده
To dimension network links, such that they will not become QoS bottlenecks, the peak rate on these links should be known. To measure these peaks on sufficiently small time scales, special measurement tools are needed. Such tools can be quite expensive and complex. Therefore network operators often rely on more cheap, standard tools, like MRTG, which were designed to measure average traffic rates (m) on time scales such as 5 minutes. For estimating the peak traffic rate (p), operators often use simple rules, such as p = α · m. In this paper we describe measurements that we have performed to investigate how well this rule describes the relation between peak and average traffic rate. In addition, we propose some more advanced rules, and compare these to the simple rule mentioned above. The analyses of our measurements, which have been performed on different kinds of networks, show that our advanced rules more adequately describe the relation between peak and average traffic rate.
منابع مشابه
Inferring Traffic Burstiness by Sampling the Buffer Occupancy
Common practice to determine the required bandwidth capacity for a network link is to measure the 5 minute average link load, and then add a safety margin to cater for the effect of burstiness on small time-scales. Because of the substantial measurement efforts required to determine the burstiness, network managers often rely on rules of thumb to find the safety margin, e.g. ‘mean plus 50%’. In...
متن کاملThe Effect of Flow Capacities on the Burstiness of Aggregated Traffic
Several research efforts have recently focused on the burstiness of Internet traffic in short, typically sub-second, time scales. Some traces reveal a rich correlation structure in those scales, while others indicate uncorrelated and almost exponential interarrivals [1]. What makes the Internet traffic bursty in some links and much smoother in others? The answer is probably long and complicated...
متن کاملA Feasibility Analysis of Network Traffic Forecast Based on Fractal Characteristics
Since fractal characteristics of network traffic were discovered by Leland in 1994, the literature is both well developed and skeptical about the value of traditional time series analysis on network data. In this paper, we investigate usability of traffic prediction based on fractal characteristics especially in the influence of assuming condition, model parameter with fractional predictors and...
متن کاملBurstiness Reduction of a Doubly Stochastic AR-Modeled Uniform Activity VBR Video
Stochastic modeling of network traffic is an area of significant research activity for current and future broadband communication networks. Multimedia traffic is statistically characterized by a bursty variable bit rate (VBR) profile. In this paper, we develop an improved model for uniform activity level video sources in ATM using a doubly stochastic autoregressive model driven by an underlying...
متن کاملConnection-Level Modeling of Network Traffic
Aggregate network traffic exhibits strong burstiness and non-Gaussian marginals, which popular models like fractional Gaussian noise (fGn) fail to capture. To better understand the cause of traffic burstiness, we look into connection-level information of traffic trace. A careful study reveals that traffic burstiness is directly related to the heterogeneity in connection bandwidths (and round-tr...
متن کامل