ACM SIGMETRICS Performance Evaluation Review

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ISSN : 0163-5999
Total articles ≅ 3,160
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Özge Celenk, Thomas Bauschert, Marcus Eckert
ACM SIGMETRICS Performance Evaluation Review, Volume 48, pp 33-36;

Quality of Experience (QoE) monitoring of video streaming traffic is crucial task for service providers. Nowadays it is challenging due to the increased usage of end-to-end encryption. In order to overcome this issue, machine learning (ML) approaches for QoE monitoring have gained popularity in the recent years. This work proposes a framework which includes a machine learning pipeline that can be used for detecting key QoE related events such as buffering events and video resolution changes for ongoing YouTube video streaming sessions in real-time. For this purpose, a ML model has been trained using YouTube streaming traffic collected from Android devices. Later on, the trained ML model is deployed in the framework's pipeline to make online predictions. The ML model uses statistical traffic information observed from the network-layer for learning and predicting the video QoE related events. It reaches 88% overall testing accuracy for predicting the video events. Although our work is yet at an early stage, the application of the ML model for online detection and prediction of video events yields quite promising results.
Luca Vassio, Zhi-Li Zhang, Danilo Giordano, Abhishek Chandra
ACM SIGMETRICS Performance Evaluation Review, Volume 48, pp 28-28;

We are pleased to welcome you to the 2nd Workshop on AI in Networks and Distributed Systems. This year we have expanded the scope of the workshop to include applications of Machine Learning and AI not merely in Networking, but also in Distributed Systems. The scale and complexity of today's networks and distributed systems make their design, analysis, optimization, and management a daunting task. Hence smart and scalable approaches leveraging machine learning solutions are increasingly called for to take full advantage of these systems and infrastructures.
William Knottenbelt, Katinka Wolter
ACM SIGMETRICS Performance Evaluation Review, Volume 48, pp 2-2;

This volume presents the proceedings of the 2nd Symposium of Cryptocurrency Analysis (SOCCA 2020), originally scheduled to be held in Milan, Italy, on November 6, 2020. The COVID-19 pandemic has necessitated, in common with many other conferences, that SOCCA will be held entirely virtual.
Gastón García González, Pedro Casas, Alicia Fernández, Gabriel Gómez
ACM SIGMETRICS Performance Evaluation Review, Volume 48, pp 49-52;

Despite the many attempts and approaches for anomaly de- tection explored over the years, the automatic detection of rare events in data communication networks remains a com- plex problem. In this paper we introduce Net-GAN, a novel approach to network anomaly detection in time-series, us- ing recurrent neural networks (RNNs) and generative ad- versarial networks (GAN). Different from the state of the art, which traditionally focuses on univariate measurements, Net-GAN detects anomalies in multivariate time-series, ex- ploiting temporal dependencies through RNNs. Net-GAN discovers the underlying distribution of the baseline, multi- variate data, without making any assumptions on its nature, offering a powerful approach to detect anomalies in com- plex, difficult to model network monitoring data. We further exploit the concepts behind generative models to conceive Net-VAE, a complementary approach to Net-GAN for net- work anomaly detection, based on variational auto-encoders (VAE). We evaluate Net-GAN and Net-VAE in different monitoring scenarios, including anomaly detection in IoT sensor data, and intrusion detection in network measure- ments. Generative models represent a promising approach for network anomaly detection, especially when considering the complexity and ever-growing number of time-series to monitor in operational networks.
Vinicius C. Oliveira, Julia Almeida Valadares, Jose Eduardo A. Sousa, Alex Borges Vieira, Heder Soares Bernardino, Saulo Moraes Villela, Glauber Dias Goncalves
ACM SIGMETRICS Performance Evaluation Review, Volume 48, pp 12-15;

Ethereum has emerged as one of the most important cryptocurrencies in terms of the number of transactions. Given the recent growth of Ethereum, the cryptocurrency community and researchers are interested in understanding the Ethereum transactions behavior. In this work, we investigate a key aspect of Ethereum: the prediction of a transaction confirmation or failure based on its features. This is a challenging issue due to the small, but still relevant, fraction of failures in millions of recorded transactions and the complexity of the distributed mechanism to execute transactions in Ethereum. To conduct this investigation, we train machine learning models for this prediction, taking into consideration carefully balanced sets of confirmed and failed transactions. The results show high-performance models for classification of transactions with the best values of F1-score and area under the ROC curve approximately equal to 0.67 and 0.87, respectively. Also, we identified the gas used as the most relevant feature for the prediction.
Andrea Marin, Carey Williamson
ACM SIGMETRICS Performance Evaluation Review, Volume 48, pp 53-61;

Craps is a simple dice game that is popular in casinos around the world. While the rules for Craps, and its mathematical analysis, are reasonably straightforward, this paper instead focuses on the best ways to cheat at Craps, by using loaded (biased) dice. We use both analytical modeling and simulation modeling to study this intriguing dice game. Our modeling results show that biasing a die away from the value 1 or towards the value 5 lead to the best (and least detectable) cheating strategies, and that modest bias on two loaded dice can increase the winning probability above 50%. Our Monte Carlo simulation results provide validation for our analytical model, and also facilitate the quantitative evaluation of other scenarios, such as heterogeneous or correlated dice.
Nikolas Wehner, Michael Seufert, Joshua Schuler, Sarah Wassermann, Pedro Casas, Tobias Hossfeld
ACM SIGMETRICS Performance Evaluation Review, Volume 48, pp 37-40;

This paper addresses the problem of Quality of Experience (QoE) monitoring for web browsing. In particular, the inference of common Web QoE metrics such as Speed Index (SI) is investigated. Based on a large dataset collected with open web-measurement platforms on different device-types, a unique feature set is designed and used to estimate the RUMSI - an efficient approximation to SI, with machinelearning based regression and classification approaches. Results indicate that it is possible to estimate the RUMSI accurately, and that in particular, recurrent neural networks are highly suitable for the task, as they capture the network dynamics more precisely.
Shunsuke Higuchi, Junji Takemasa, Yuki Koizumi, Atsushi Tagami, Toru Hasegawa
ACM SIGMETRICS Performance Evaluation Review, Volume 48, pp 45-48;

This paper revisits longest prefix matching in IP packet forwarding because an emerging data structure, learned index, is recently presented. A learned index uses machine learning to associate key-value pairs in a key-value store. The fundamental idea to apply a learned index to an FIB is to simplify the complex longest prefix matching operation to a nearest address search operation. The size of the proposed FIB is less than half of an existing trie-based FIB while it achieves the computation speed nearly equal to the trie-based FIB. Moreover, the computation speed of the proposal is independent of the length of IP prefixes, unlike trie-based FIBs.
Jefferson E. Simoes, Eduardo Ferreira, Daniel S. Menasch´e, Carlos A. V. Campos
ACM SIGMETRICS Performance Evaluation Review, Volume 48, pp 8-11;

Cryptocurrencies typically aim at preserving the privacy of their users. Different cryptocurrencies preserve privacy at various levels, some of them requiring users to rely on strategies to raise the privacy level to their needs. Among those strategies, we focus on two of them: merge avoidance and mixing services. Such strategies may be adopted on top of virtually any blockchain-based cryptocurrency. In this paper, we show that whereas optimal merge avoidance leads to an NP-hard optimization problem, incentive-compatible mixing services are subject to a certain class of impossibility results. Together, our results contribute to the body of work on fundamental limits of privacy mechanisms in blockchainbased cryptocurrencies.
Giulio Masetti, Silvano Chiaradonna, Felicita Di Giandomenico, William H. Sanders, Brett Feddersen
ACM SIGMETRICS Performance Evaluation Review, Volume 48, pp 62-67;

Mobius is well known as a modeling and evaluation environment for performance and dependability indicators. It has been conceived in a modular and flexible fashion, to be easily expanded to incorporate new features, formalisms and tools. The need of modeling systems characterized by a large population of heterogeneous interacting components, which are nowadays more and more common in a variety of application contexts, provided the opportunity to focus on a new operator to efficiently manage non-anonymous replication, as requested for these systems. This tool paper presents the implementation of a new replication operator, called Advanced Rep, in Mobius. Efficiency of Advanced Rep is evaluated against a recently developed alternative solution.
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