Ten fallacies and pitfalls on end-to-end available bandwidth estimation

The area of available bandwidth (avail-bw) estimation has attracted significant interest recently, with several estimation techniques and tools developed during the last 2-3 years. Unfortunately, some key issues regarding the avail-bw definition, estimation, and validation remain vague or misinterpreted. In this note, we first review the previous work in the area and classify the existing techniques in two classes: direct probing and iterative probing. We then identify ten misconceptions, in the form of fallacies or pitfalls, that we consider as most important. Some misconceptions relate to basic statistics, such as the impact of the population variance on the sample mean, the variability of the avail-bw in different time scales, and the effect of the probing duration. Other misconceptions relate to the queueing model underlying these estimation techniques. For instance, ignoring that traffic burstiness or the presence of multiple bottlenecks can cause significant underestimation errors. Our objective is not to debunk previous work or to claim that some estimation techniques are better than others, but to clarify a number of important issues that cover the entire area of avail-bw estimation so that this important metric can be better understood and put in practical use

The Quest for Bandwidth Estimation Techniques for large-scale Distributed Systems

In recent years the research community has developed many techniques to estimate the end-to-end available bandwidth of an Internet path. This important metric has been proposed for use in several distributed systems and, more recently, has even been considered to improve the congestion control mechanism of TCP. Thus, it has been suggested that some existing estimation techniques could be used for this purpose. However, existing tools were not designed for large-scale deployments and were mostly validated in controlled settings, considering only one measurement running at a time. In this paper, we argue that current tools, while offering good estimates when used alone, might not work in large-scale systems where several estimations severely interfere with each other. We analyze the properties of the measurement paradigms employed today and discuss their functioning, study their overhead and analyze their interference. Our testbed results show that current techniques are insufficient as they are. Finally, we will discuss and propose some principles that should be taken into account for including available bandwidth measurements in large-scale distributed systems. 1

Multi-path Probabilistic Available Bandwidth Estimation through Bayesian Active Learning

Knowing the largest rate at which data can be sent on an end-to-end path such that the egress rate is equal to the ingress rate with high probability can be very practical when choosing transmission rates in video streaming or selecting peers in peer-to-peer applications. We introduce probabilistic available bandwidth, which is defined in terms of ingress rates and egress rates of traffic on a path, rather than in terms of capacity and utilization of the constituent links of the path like the standard available bandwidth metric. In this paper, we describe a distributed algorithm, based on a probabilistic graphical model and Bayesian active learning, for simultaneously estimating the probabilistic available bandwidth of multiple paths through a network. Our procedure exploits the fact that each packet train provides information not only about the path it traverses, but also about any path that shares a link with the monitored path. Simulations and PlanetLab experiments indicate that this process can dramatically reduce the number of probes required to generate accurate estimates

Available bandwidth estimation metrics as tools to evaluate network trunk links

Nowadays the platform par excellence for the development of all telecommunication activities is the Internet; and its infrastructure is facing new challenges every day due to the growth in demand for more content, such as streaming video, storage, and cloud processing. Also, to maintain optimal levels of service quality, network applications demand more telecommunication resources. Similarly, the network infrastructures that support these applications have evolved, and demand greater and more efficient management of the trunk links, which play a primary role in sustaining services. Therefore, this paper presents the performance evaluation of trunk, wired and wireless links in a heterogeneous computer network infrastructure, using available bandwidth estimation tools such as IGI, Pathload, and Traceband. Thus, for the experimental evaluation of the trunk links, two real network scenarios were implemented, where crosstraffic was generated in a synthetic way using the Mgen tool. Consequently, this study allowed verifying in other aspects; that the metrics of the estimation tools can be used to evaluate and know the performance of wired and wireless trunk links, which can be reliable up to 96% for network administrative tasks

Overhead in Available Bandwidth Estimation Tools: Evaluation and Analysis

The current Available Bandwidth Estimation Tools (ABET's) to perform an estimation, using probes packets are inserted into the network. The utilization These packages, makes ABET's are intrusive and consumes part of which is measuring bandwidth to noise known as "Overhead Estimation Tools" (OET); it’s can produce negative effects on measurements performed by the ABET. This paper presents a complete and comparative analysis of behavior of Available Bandwidth (av_bw), of the ABET's most representative, as well as: Abing, Diettopp, Pathload, PathChirp, Traceband, IGI, PTR, Assolo and Wbest. The study with real Internet traffic, shows the percentage of test that is a factor packets affecting two main aspects of the estimation. The first, the accuracy, and increased indicating that EOT is directly proportional to the percentage of RE, reaching up to 70% in the tool evaluated with most of 30% of Cross-Traffic (CT). And second, the techniques used to send probes packets highly influences the Estimation Time (ET), where some tools that use slops spend up to 240s to converge when there is 60% CT in the network, ensuring that the estimate this technique av_bw highly congested channel, OET as much is used, resulting in inaccuracies in measurement

Overhead in available bandwidth estimation tools: Evaluation and analysis

Current Available Bandwidth Estimation Tools (ABET) insert into the network probing packets to perform a single estimation. The utilization of these packets makes ABET intrusive and prone to errors since they consume part of the available bandwidth they are measuring. This paper presents a comparative of Overhead Estimation Tools (OET) analysis of representative ABET: Abing, Diettopp, Pathload, PathChirp, Traceband, IGI, PTR, Assolo, and Wbest. By using Internet traffic, the study shows that the insertion of probing packets is a factor that affects two metrics associated to the estimation. First, it is shown that the accuracy is affected proportionally to the amount of probing traffic. Secondly, the Estimation Time (ET) is increased in high congested end-to-end links when auto-induced congestion tools are use

Smartphone-based crowdsourcing for estimating the bottleneck capacity in wireless networks

Crowdsourcing enables the fine-grained characterization and performance evaluation of today׳s large-scale networks using the power of the masses and distributed intelligence. This paper presents SmartProbe, a system that assesses the bottleneck capacity of Internet paths using smartphones, from a mobile crowdsourcing perspective. With SmartProbe measurement activities are more bandwidth efficient compared to similar systems, and a larger number of users can be supported. An application based on SmartProbe is also presented: georeferenced measurements are mapped and used to compare the performance of mobile broadband operators in wide areas. Results from one year of operation are included