Why go logarithmic if we can go linear?

Metwally, Ahmed; Agrawal, Divyakant; Abbadi, Amr El

doi:10.1145/1353343.1353418

Cited by 40 publications

(1 citation statement)

References 52 publications

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“…Giroire's proposal [74] is to estimate the count from the minimum of the hashed values. These and other techniques are compared in [102]. A remarkable conclusion from this study is that, from a practical standpoint, direct bitmaps offer the best tradeoff between complexity and accuracy.…”

Section: Counting the Number Of Active Flowsmentioning

confidence: 99%

Efficient algorithms for passive network measurement

Sanjuàs Cuxart

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Network monitoring has become a necessity to aid in the management and operation of large networks. Passive network monitoring consists of extracting metrics (or any information of interest) by analyzing the traffic that traverses one or more network links. Extracting information from a high-speed network link is challenging, given the great data volumes and short packet inter-arrival times. These difficulties can be alleviated by using extremely efficient algorithms or by sampling the incoming traffic. This work improves the state of the art in both these approaches. For one-way packet delay measurement, we propose a series of improvements over a recently appeared technique called Lossy Difference Aggregator. A main limitation of this technique is that it does not provide per-flow measurements. We propose a data structure called Lossy Difference Sketch that is capable of providing such per-flow delay measurements, and, unlike recent related works, does not rely on any model of packet delays. In the problem of collecting measurements under the sliding window model, we focus on the estimation of the number of active flows and in traffic filtering. Using a common approach, we propose one algorithm for each problem that obtains great accuracy with significant resource savings. In the traffic sampling area, the selection of the sampling rate is a crucial aspect. The most sensible approach involves dynamically adjusting sampling rates according to network traffic conditions, which is known as adaptive sampling. We propose an algorithm called Cuckoo Sampling that can operate with a fixed memory budget and perform adaptive flow-wise packet sampling. It is based on a very simple data structure and is computationally extremely lightweight. The techniques presented in this work are thoroughly evaluated through a combination of theoretical and experimental analysis.

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Section: Counting the Number Of Active Flowsmentioning

confidence: 99%

Efficient algorithms for passive network measurement

Sanjuàs Cuxart

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Counting Flows over Sliding Windows in High Speed Networks

2009

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Abstract. Counting the number of flows present in network traffic is not trivial, given that the naive approach of using a hash table to track the active flows is too slow for the current backbone network speeds. Several algorithms have been proposed in the recent literature that can calculate an approximate count using small amount of memory and few memory accesses per packet. Fewer works have addressed the more complex problem of counting flows over sliding windows, where the main challenge is to continuously expire old information. One of the existing proposals is a straightforward adaptation of the direct bitmaps technique to the sliding window model. We present an algorithm called Countdown Vector that also builds upon the direct bitmaps technique. Our algorithm, however, obtains significant cost reductions both in terms of memory and CPU, by introducing an extra approximation in the mechanism in charge of the expiration of old information.

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Privacy-Preserving Mobility Monitoring Using Sketches of Stationary Sensor Readings

Kamp

Kopp

Mock

et al. 2013

Advanced Information Systems Engineering

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Two fundamental tasks of mobility modeling are (1) to track the number of distinct persons that are present at a location of interest and (2) to reconstruct flows of persons between two or more different locations. Stationary sensors, such as Bluetooth scanners, have been applied to both tasks with remarkable success. However, this approach has privacy problems. For instance, Bluetooth scanners store the MAC address of a device that can in principle be linked to a single person. Unique hashing of the address only partially solves the problem because such a pseudonym is still vulnerable to various linking attacks. In this paper we propose a solution to both tasks using an extension of linear counting sketches. The idea is to map several individuals to the same position in a sketch, while at the same time the inaccuracies introduced by this overloading are compensated by using several independent sketches. This idea provides, for the first time, a general set of primitives for privacy preserving mobility modeling from Bluetooth and similar address-based devices.

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Why go logarithmic if we can go linear?

Cited by 40 publications

References 52 publications

Efficient algorithms for passive network measurement

Efficient algorithms for passive network measurement

Counting Flows over Sliding Windows in High Speed Networks

Privacy-Preserving Mobility Monitoring Using Sketches of Stationary Sensor Readings

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