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2016

N. Rivetti, E. Anceaume, Y. Busnel, L. Querzoni, B. Sericola
Online Scheduling for Shuffle Grouping in Distributed Stream Processing Systems

To appear in Proceedings of the ACM/IFIP/USENIX Middleware Conference, 2016

Abstract [+]

"Shuffle grouping is a technique used by stream processing frameworks to share input load among parallel instances of stateless operators. With shuffle grouping each tuple of a stream can be assigned to any available operator instance, independently from any previous assignment. A common approach to implement shuffle grouping is to adopt a Round-Robin policy, a simple solution that fares well as long as the tuple execution time is almost the same for all the tuples. However, such an assumption rarely holds in real cases where execution time strongly depends on tuple content. As a consequence, parallel stateless operators within stream processing applications may experience unpredictable unbalance that, in the end, causes undesirable increase in tuple completion times. In this paper we propose Online Shuffle Grouping (OSG), a novel approach to shuffle grouping aimed at reducing the overall tuple completion time. OSG estimates the execution time of each tuple, enabling a proactive and online scheduling of input load to the target operator instances. Sketches are used to efficiently store the otherwise large amount of information required to schedule incoming load. We provide a probabilistic analysis and illustrate, through both simulations and a running prototype, its impact on stream processing applications."

Downloads:pdf - Paper
bib - BibTeX reference



L. Aniello, C. Ciccotelli, M. Cinque, F. Frattini, L. Querzoni, S. Russo
Automatic Invariant Selection for Online Anomaly Detection

(to appear) In Proceedings of the 35th International Conference on Computer Safety, Reliability and Security (SAFECOMP), 2016

Abstract [+]

"Invariants are stable relationships among system metrics expected to hold during normal operating conditions. The violation of such relationships can be used to detect anomalies at runtime. However, this approach does not scale to large systems, as the number of invariants quickly grows with the number of considered metrics. The resulting "background noise" for the invariant-based detection system hinders its effectiveness. In this paper we propose a general and automatic approach for identifying a subset of mined invariants that properly model system runtime behavior with a reduced amount of background noise. This translates into better overall performance (i.e., less false positives)."

Downloads:bib - BibTeX reference



G. Laurenza, D. Ucci, L. Aniello, R. Baldoni
An Architecture for Semi-Automatic Collaborative Malware Analysis for CIs

The 3rd International Workshop on Reliability and Security Aspects for Critical Infrastructure, 2016

Abstract [+]

"Critical Infrastructures (CIs) are among the main targets of activists, cyber terrorists and state sponsored attacks. To protect itself, a CI needs to build and keep updated a domestic knowledge base of cyber threats. It cannot indeed completely rely on external service providers because information on incidents can be so sensitive to impact national security. In this paper, we propose an architecture for a malware analysis framework to support CIs in such a challenging task. Given the huge number of new malware produced daily, the architecture is designed to automate the analysis to a large extent, leaving to human analysts only a small and manageable part of the whole effort. Such a non-automatic part of the analysis requires a range of expertise, usually contributed by more analysts. The architecture enables analysts to work collaboratively to improve the understanding of samples that demand deeper investigations (intra-CI collaboration). Furthermore, the architecture allows to share partial and configurable views of the knowledge base with other interested CIs, to collectively obtain a more complete vision of the cyber threat landscape (inter-CI collaboration)."

Downloads:pdf - ReSA4CI2016
bib - BibTeX reference



N. Rivetti, Y. Busnel, L. Querzoni
Load-Aware Shedding in Stream Processing Systems

Proceedings of the 10th ACM International Conference on Distributed and Event-Based Systems (DEBS), 2016

Abstract [+]

"Load shedding is a technique employed by stream proces ing systems to handle unpredictable spikes in the input load whenever available computing resources are not adequately provisioned. A load shedder drops tuples to keep the input load below a critical threshold and thus avoid unbounded queuing and system trashing. In this paper we propose Load-Aware Shedding (LAS), a novel load shedding solution that drops tuples with the aim of maintaining queuing times below a tunable threshold. Tuple execution durations are estimated at runtime using efficient sketch data structures. We provide a theoretical analysis proving that LAS is an (ε,δ)-approximation of the optimal online load shedder and show its performance through a practical evaluation based both on simulations and on a running prototype."

Downloads:pdf - Paper
bib - BibTeX reference



F. Petroni, L. Querzoni, R. Beraldi, M. Paolucci
LCBM: a fast and lightweight collaborative filtering algorithm for binary ratings

Journal of Systems and Software, num. 117, pages 583-594, Elsevier, 2016

Abstract [+]

"In the last ten years, recommendation systems evolved from novelties to powerful business tools, deeply changing the internet industry. Collaborative Filtering (CF) represents a widely adopted strategy today to build recommendation engines. The most advanced CF techniques (i.e. those based on matrix factorization) provide high quality results, but may incur prohibitive computational costs when applied to very large data sets. In this paper we present Linear Classifier of Beta distributions Means (LCBM), a novel collaborative filtering algorithm for binary ratings that is (i) inherently parallelizable (ii) provides results whose quality is on-par with state-of-the-art solutions (iii) at a fraction of the computational cost. These characteristics allow LCBM to efficiently handle large instances of the collaborative filtering problem on a single machine in short timeframes."

Downloads:pdf - Paper (accepted version)
bib - BibTeX reference



F. Petroni, L. Querzoni, R. Beraldi, M. Paolucci
Exploiting User Feedback for Online Filtering in Event-based Systems

In Proceedings of the 31st ACM Symposium on Applied Computing (SAC), 2016

Abstract [+]

"Modern large-scale internet applications, like the ubiquitous social networks, represent today a fundamental source of information for millions of users. The larger is the user base, the more difficult it is to control the quality of data that is spread from producers to consumers. This can easily hamper the usability of such systems as the amount of low quality data received by consumers grows uncontrolled. In this paper we propose a novel solution to automatically filter new data injected in event-based systems with the aim of delivering to consumers only content they are actually interested in. Filtering is executed at run-time by first profiling both producers and consumers, and then matching their profiles as new data is produced."

Downloads:pdf - Paper
bib - BibTeX reference



M. Pomilia
A study on obfuscation techniques for Android malware

Master thesis (Tesi di Laurea Specialistica)
Downloads:pdf - Thesis
bib - BibTeX reference



F. Petroni
Mining at scale with latent factor models for matrix completion

PhD thesis - University of Rome "La Sapienza"
Downloads:pdf - Thesis
bib - BibTeX reference



D. Dell'Atti
Reverse Engineering For Malware Analysis: Dissecting The Novel Banking Trojan ZeusVM

Master thesis (Tesi di Laurea Specialistica)
Downloads:pdf - Thesis
bib - BibTeX reference

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