1 Introduction 2 Map 3 The Data Stream Phenomenon 4 Data Streaming: Formal Aspects 5 Foundations: Basic Mathematical Ideas 6 Foundations: Basic Algorithmic Techniques 7 Foundations: Summary 8 Streaming Systems 9 New Directions 10 Historic Notes 11 Concluding Remarks Acknowledgements References

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Data Streams: Algorithms and Applications

The increasing practicality of large-scale flow capture makes it possible to conceive of traffic analysis methods that detect and identify a large and diverse set of anomalies. However the challenge of effectively analyzing this massive data source for anomaly diagnosis is as yet unmet. We argue that the distributions of packet features (IP addresses and ports) observed in flow traces reveals both the presence and the structure of a wide range of anomalies. Using entropy as a summarization tool, we show that the analysis of feature distributions leads to significant advances on two fronts: (1) it enables highly sensitive detection of a wide range of anomalies, augmenting detections by volume-based methods, and (2) it enables automatic classification of anomalies via unsupervised learning. We show that using feature distributions, anomalies naturally fall into distinct and meaningful clusters. These clusters can be used to automatically classify anomalies and to uncover new anomaly types. We validate our claims on data from two backbone networks (Abilene and Geant) and conclude that feature distributions show promise as a key element of a fairly general network anomaly diagnosis framework.

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Mining anomalies using traffic feature distributions

Anomalies are unusual and significant changes in a network's traffic levels, which can often span multiple links. Diagnosing anomalies is critical for both network operators and end users. It is a difficult problem because one must extract and interpret anomalous patterns from large amounts of high-dimensional, noisy data.In this paper we propose a general method to diagnose anomalies. This method is based on a separation of the high-dimensional space occupied by a set of network traffic measurements into disjoint subspaces corresponding to normal and anomalous network conditions. We show that this separation can be performed effectively by Principal Component Analysis.Using only simple traffic measurements from links, we study volume anomalies and show that the method can: (1) accurately detect when a volume anomaly is occurring; (2) correctly identify the underlying origin-destination (OD) flow which is the source of the anomaly; and (3) accurately estimate the amount of traffic involved in the anomalous OD flow.We evaluate the method's ability to diagnose (i.e., detect, identify, and quantify) both existing and synthetically injected volume anomalies in real traffic from two backbone networks. Our method consistently diagnoses the largest volume anomalies, and does so with a very low false alarm rate.

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Diagnosing network-wide traffic anomalies

Accurate network traffic measurement is required for accounting, bandwidth provisioning and detecting DoS attacks. These applications see the traffic as a collection of flows they need to measure. As link speeds and the number of flows increase, keeping a counter for each flow is too expensive (using SRAM) or slow (using DRAM). The current state-of-the-art methods (Cisco's sampled NetFlow) which log periodically sampled packets are slow, inaccurate and resource-intensive. Previous work showed that at different granularities a small number of "heavy hitters" accounts for a large share of traffic. Our paper introduces a paradigm shift for measurement by concentrating only on large flows --- those above some threshold such as 0.1% of the link capacity.We propose two novel and scalable algorithms for identifying the large flows: sample and hold and multistage filters, which take a constant number of memory references per packet and use a small amount of memory. If $M$ is the available memory, we show analytically that the errors of our new algorithms are proportional to $1/M$; by contrast, the error of an algorithm based on classical sampling is proportional to $1/\sqrtM$, thus providing much less accuracy for the same amount of memory. We also describe further optimizations such as early removal and conservative update that further improve the accuracy of our algorithms, as measured on real traffic traces, by an order of magnitude. Our schemes allow a new form of accounting called threshold accounting in which only flows above a threshold are charged by usage while the rest are charged a fixed fee. Threshold accounting generalizes usage-based and duration based pricing.

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New directions in traffic measurement and accounting

Networks today rely on middleboxes to provide critical performance, security, and policy compliance capabilities. Achieving these benefits and ensuring that the traffic is directed through the desired sequence of middleboxes requires significant manual effort and operator expertise. In this respect, Software-Defined Networking (SDN) offers a promising alternative. Middleboxes, however, introduce new aspects (e.g., policy composition, resource management, packet modifications) that fall outside the purvey of traditional L2/L3 functions that SDN supports (e.g., access control or routing). This paper presents SIMPLE, a SDN-based policy enforcement layer for efficient middlebox-specific "traffic steering''. In designing SIMPLE, we take an explicit stance to work within the constraints of legacy middleboxes and existing SDN interfaces. To this end, we address algorithmic and system design challenges to demonstrate the feasibility of using SDN to simplify middlebox traffic steering. In doing so, we also take a significant step toward addressing industry concerns surrounding the ability of SDN to integrate with existing infrastructure and support L4-L7 capabilities.

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SIMPLE-fying middlebox policy enforcement using SDN

Engineering a large IP backbone network without an accurate network-wide view of the traffic demands is challenging. Shifts in user behavior, changes in routing policies, and failures of network elements can result in significant (and sudden) fluctuations in load. In this paper, we present a model of traffic demands to support traffic engineering and performance debugging of large Internet Service Provider networks. By defining a traffic demand as a volume of load originating from an ingress link and destined to a set of egress links, we can capture and predict how routing affects the traffic traveling between domains. To infer the traffic demands, we propose a measurement methodology that combines flow-level measurements collected at all ingress links with reachability information about all egress links. We discuss how to cope with situations where practical considerations limit the amount and quality of the necessary data. Specifically, we show how to infer interdomain traffic demands using measurements collected at a smaller number of edge links-the peering links connecting the neighboring providers. We report on our experiences in deriving the traffic demands in the AT&T IP BAckbone, by collecting, validating, and joining very large and diverse sets of usage, configuration, and routing data over extended periods of time. The paper concludes with a preliminary analysis of the observed dynamics of the traffic demands and a discussion of the practical implications for traffic engineering.

Deriving traffic demands for operational IP networks: methodology and experience

Engineering a large IP backbone network without an accurate, network-wide view of the traffic demands is challenging. Shifts in user behavior, changes in routing policies, and failures of network elements can result in significant (and sudden) fluctuations in load. In this paper, we present a model of traffic demands to support traffic engineering and performance debugging of large Internet Service Provider networks. By defining a traffic demand as a volume of load originating from an ingress link and destined to a set of egress links, we can capture and predict how routing affects the traffic traveling between domains. To infer the traffic demands, we propose a measurement methodology that combines flow-level measurements collected at all ingress links with reachability information about all egress links. We discuss how to cope with situations where practical considerations limit the amount and quality of the necessary data. Specifically, we show how to infer interdomain traffic demands using measurements collected at a smaller number of edge links --- the peering links connecting to neighboring providers. We report on our experiences in deriving the traffic demands in the AT&T IP Backbone, by collecting, validating, and joining very large and diverse sets of usage, configuration, and routing data over extended periods of time. The paper concludes with a preliminary analysis of the observed dynamics of the traffic demands and a discussion of the practical implications for traffic engineering.

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Fred True

Papers

Deriving traffic demands for operational IP networks: methodology and experience

Deriving traffic demands for operational IP networks: methodology and experience