Network Traffic Profiling and Anomaly Detection for Cyber Security
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Large-Scale Learning from Data Streams with Apache SAMOA
Large-Scale Learning from Data Streams with Apache SAMOA Nicolas Kourtellis1, Gianmarco De Francisci Morales2, and Albert Bifet3 1 Telefonica Research, Spain, [email protected] 2 Qatar Computing Research Institute, Qatar, [email protected] 3 LTCI, Télécom ParisTech, France, [email protected] Abstract. Apache SAMOA (Scalable Advanced Massive Online Anal- ysis) is an open-source platform for mining big data streams. Big data is defined as datasets whose size is beyond the ability of typical soft- ware tools to capture, store, manage, and analyze, due to the time and memory complexity. Apache SAMOA provides a collection of dis- tributed streaming algorithms for the most common data mining and machine learning tasks such as classification, clustering, and regression, as well as programming abstractions to develop new algorithms. It fea- tures a pluggable architecture that allows it to run on several distributed stream processing engines such as Apache Flink, Apache Storm, and Apache Samza. Apache SAMOA is written in Java and is available at https://samoa.incubator.apache.org under the Apache Software Li- cense version 2.0. 1 Introduction Big data are “data whose characteristics force us to look beyond the traditional methods that are prevalent at the time” [18]. For instance, social media are one of the largest and most dynamic sources of data. These data are not only very large due to their fine grain, but also being produced continuously. Furthermore, such data are nowadays produced by users in different environments and via a multitude of devices. For these reasons, data from social media and ubiquitous environments are perfect examples of the challenges posed by big data. -
DSP Frameworks DSP Frameworks We Consider
Università degli Studi di Roma “Tor Vergata” Dipartimento di Ingegneria Civile e Ingegneria Informatica DSP Frameworks Corso di Sistemi e Architetture per Big Data A.A. 2017/18 Valeria Cardellini DSP frameworks we consider • Apache Storm (with lab) • Twitter Heron – From Twitter as Storm and compatible with Storm • Apache Spark Streaming (lab) – Reduce the size of each stream and process streams of data (micro-batch processing) • Apache Flink • Apache Samza • Cloud-based frameworks – Google Cloud Dataflow – Amazon Kinesis Streams Valeria Cardellini - SABD 2017/18 1 Apache Storm • Apache Storm – Open-source, real-time, scalable streaming system – Provides an abstraction layer to execute DSP applications – Initially developed by Twitter • Topology – DAG of spouts (sources of streams) and bolts (operators and data sinks) Valeria Cardellini - SABD 2017/18 2 Stream grouping in Storm • Data parallelism in Storm: how are streams partitioned among multiple tasks (threads of execution)? • Shuffle grouping – Randomly partitions the tuples • Field grouping – Hashes on a subset of the tuple attributes Valeria Cardellini - SABD 2017/18 3 Stream grouping in Storm • All grouping (i.e., broadcast) – Replicates the entire stream to all the consumer tasks • Global grouping – Sends the entire stream to a single task of a bolt • Direct grouping – The producer of the tuple decides which task of the consumer will receive this tuple Valeria Cardellini - SABD 2017/18 4 Storm architecture • Master-worker architecture Valeria Cardellini - SABD 2017/18 5 Storm -
Comparative Analysis of Data Stream Processing Systems
Shah Zeb Mian Comparative Analysis of Data Stream Processing Systems Master’s Thesis in Information Technology February 23, 2020 University of Jyväskylä Faculty of Information Technology Author: Shah Zeb Mian Contact information: [email protected] Supervisors: Oleksiy Khriyenko, and Vagan Terziyan Title: Comparative Analysis of Data Stream Processing Systems Työn nimi: Vertaileva analyysi Data Stream-käsittelyjärjestelmistä Project: Master’s Thesis Study line: All study lines Page count: 48+0 Abstract: Big data processing systems are evolving to be more stream oriented where data is processed continuously by processing it as soon as it arrives. Earlier data was often stored in a database, a file system or other form of data storage system. Applications would query the data as needed. Stram processing is the processing of data in motion. It works on continuous data retrieved from different resources. Instead of periodically collecting huge static data, streaming frameworks process data as soon as it becomes available, hence reducing latency. This thesis aims to conduct a comparative analysis of different streaming processors based on selected features. Research focuses on Apache Samza, Apache Flink, Apache Storm and Apache Spark Structured Streaming. Also, this thesis explains Apache Kafka which is a log-based data storage widely used in streaming frameworks. Keywords: Big Data, Stream Processing,Batch Processing,Streaming Engines, Apache Kafka, Apache Samza Suomenkielinen tiivistelmä: Big data-käsittelyjärjestelmät ovat tällä hetkellä kehittymässä stream-orientoituneiksi, eli data käsitellään heti saapuessaan. Perinteisemmin data säilöt- tiin tietokantaan, tiedostopohjaisesti tai muuhun tiedonsäilytysjärjestelmään, ja applikaatiot hakivat datan tarvittaessa. Stream-pohjainen järjestelmä käsittelee liikkuvaa dataa, jatkuva- aikaista dataa useasta lähteestä. Sen sijaan, että haetaan ajoittain dataa, stream-pohjaiset frameworkit pystyvät käsittelemään i dataa heti kun se on saatavilla, täten vähentäen viivettä. -
Optimizing Resource Utilization in Distributed Computing Systems For
THESE` DE DOCTORAT DE L’ETABLISSEMENT´ UNIVERSITE´ BOURGOGNE FRANCHE-COMTE´ PREPAR´ EE´ A` L’UNIVERSITE´ DE FRANCHE-COMTE´ Ecole´ doctorale n°37 Sciences Pour l’Ingenieur´ et Microtechniques Doctorat d’Informatique par ANTHONY NASSAR Optimizing Resource Utilization in Distributed Computing Systems for Automotive Applications Optimisation de l’utilisation des ressources dans les systemes` informatiques distribues´ pour les applications automobiles These` present´ ee´ et soutenue publiquement le 04-02-2021 a` Belfort, devant le Jury compose´ de : MR CERIN CHRISTOPHE Professeur a` l’Universite´ Sorbonne Paris Nord President´ MR CHBEIR RICHARD Professeur a` l’Universite´ de Pau et des Pays de l’Adour Rapporteur MME BENBERNOU SALIMA Professeur a` l’Universite´ Paris-Descartes Rapporteur MR MOSTEFAOUI AHMED Maˆıtre de conferences´ a` l’Universite´ de Franche-Comte´ Directeur de these` MR DESSABLES FRANC¸ OIS Ingenieur´ chez Groupe PSA Codirecteur de these` DOCTORAL THESIS OF THE UNIVERSITY BOURGOGNE FRANCHE-COMTE´ INSTITUTION PREPARED AT UNIVERSITE´ DE FRANCHE-COMTE´ Doctoral school n°37 Engineering Sciences and Microtechnologies Computer Science Doctorate by ANTHONY NASSAR Optimizing Resource Utilization in Distributed Computing Systems for Automotive Applications Optimisation de l’utilisation des ressources dans les systemes` informatiques distribues´ pour les applications automobiles Thesis presented and publicly defended in Belfort, on 04-02-2021 Composition of the Jury : CERIN CHRISTOPHE Professor at Universite´ Sorbonne Paris Nord President -
Storage and Ingestion Systems in Support of Stream Processing
Storage and Ingestion Systems in Support of Stream Processing: A Survey Ovidiu-Cristian Marcu, Alexandru Costan, Gabriel Antoniu, María Pérez-Hernández, Radu Tudoran, Stefano Bortoli, Bogdan Nicolae To cite this version: Ovidiu-Cristian Marcu, Alexandru Costan, Gabriel Antoniu, María Pérez-Hernández, Radu Tudoran, et al.. Storage and Ingestion Systems in Support of Stream Processing: A Survey. [Technical Report] RT-0501, INRIA Rennes - Bretagne Atlantique and University of Rennes 1, France. 2018, pp.1-33. hal-01939280v2 HAL Id: hal-01939280 https://hal.inria.fr/hal-01939280v2 Submitted on 14 Dec 2018 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Storage and Ingestion Systems in Support of Stream Processing: A Survey Ovidiu-Cristian Marcu, Alexandru Costan, Gabriel Antoniu, María S. Pérez-Hernández, Radu Tudoran, Stefano Bortoli, Bogdan Nicolae TECHNICAL REPORT N° 0501 November 2018 Project-Team KerData ISSN 0249-0803 ISRN INRIA/RT--0501--FR+ENG Storage and Ingestion Systems in Support of Stream Processing: A Survey Ovidiu-Cristian Marcu∗, Alexandru -
A Study of Incremental Checkpointing in Distributed Stream Processing Systems
A Study of Incremental Checkpointing in Distributed Stream Processing Systems A Thesis submitted to the designated by the General Assembly of Special Composition of the Department of Computer Science and Engineering Examination Committee by Aristidis Chronarakis in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE IN COMPUTER SCIENCE WITH SPECIALIZATION IN COMPUTER SYSTEMS University of Ioannina 2019 Examining Committee: • Kostas Magoutis, Assistant Professor, Department of Computer Science and Engineering, University of Ioannina (Supervisor) • Vassilios V. Dimakopoulos, Associate Professor, Department of Computer Sci- ence and Engineering, University of Ioannina • Evaggelia Pitoura, Professor, Department of Computer Science and Engineer- ing, University of Ioannina Dedication Dedicated to my family. Acknowledgements I would like to thank my advisor Prof. Kostas Magoutis for his guidance and support throughout my studies on the department, from the undergraduate level till the graduate. Special thanks to Prof. Vassilios Dimakopoulos and Prof. Evaggelia Pitoura for their participation as members of the examination committee. Finally, I would like to thank my family for the support and my friends for all the good moments we spent. Table of Contents List of Figures iii Abstract v Εκτεταμένη Περίληψη vi 1 Introduction 1 1.1 Objectives ................................... 2 1.2 Structure of this dissertation ......................... 3 2 Background 4 2.1 General concepts ............................... 4 2.2 Checkpoint-rollback methodology ..................... 7 2.3 Continuous eventual checkpointing (CEC) ................. 8 2.4 Apache Samza ................................ 9 2.4.1 Streams ................................ 9 2.4.2 Applications, Tasks, Containers ................... 10 2.4.3 State .................................. 11 2.4.4 Fault tolerance of stateful applications ............... 12 2.4.5 Message (tuple) replay and semantics .............. -
Evaluating the Impact of Streaming Systems Design on Application Performance Alessio Pagliari
Evaluating the impact of streaming systems design on application performance Alessio Pagliari To cite this version: Alessio Pagliari. Evaluating the impact of streaming systems design on application performance. Data Structures and Algorithms [cs.DS]. Université Côte d’Azur, 2021. English. NNT : 2021COAZ4011. tel-03273377 HAL Id: tel-03273377 https://tel.archives-ouvertes.fr/tel-03273377 Submitted on 29 Jun 2021 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. THÈSE DE DOCTORAT Évaluer l'impact de la conception des systèmes de streaming sur la performance des applications Alessio PAGLIARI Laboratoire d’Informatique, Signaux et Systèmes de Sophia Antipolis (I3S) Présentée en vue de l’obtention Devant le jury, composé de : du grade de docteur en Informatique Jean-Marc Pierson, Professeur, Université Paul Sabatier Toulouse 3 d’Université Côte d’Azur Guillaume Pierre, Professeur, Université de Rennes 1 Pietro Michiardi, Professeur, Eurecom Dirigée par : Fabrice Huet / Fabrice Huet, Professeur, Université Côte d’Azur Guillaume Urvoy-Keller, Professeur, -
Apache Samza
Apache Samza Martin Kleppmann Definition vehicles, or the writes of records to a database. Apache Samza is an open source frame- Stream processing jobs are long- work for distributed processing of high- running processes that continuously volume event streams. Its primary design consume one or more event streams, goal is to support high throughput for a invoking some application logic on wide range of processing patterns, while every event, producing derived output providing operational robustness at the streams, and potentially writing output massive scale required by Internet com- to databases for subsequent querying. panies. Samza achieves this goal through While a batch process or a database a small number of carefully designed ab- query typically reads the state of a stractions: partitioned logs for messag- dataset at one point in time, and then ing, fault-tolerant local state, and cluster- finishes, a stream processor is never based task scheduling. finished: it continually awaits the arrival of new events, and it only shuts down when terminated by an administrator. Many tasks can be naturally ex- Overview pressed as stream processing jobs, for example: Stream processing is playing an increas- • aggregating occurrences of events, ingly important part of the data man- e.g., counting how many times a agement needs of many organizations. particular item has been viewed; Event streams can represent many kinds • computing the rate of certain events, of data, for example, the activity of users e.g., for system diagnostics, report- on a website, the movement of goods or ing, and abuse prevention; 1 2 Martin Kleppmann • enriching events with information the scalability of Samza is directly at- from a database, e.g., extending user tributable to the choice of these founda- click events with information about tional abstractions. -
An Evaluation of Real-Time Processing of Call Detail Records Using Stream Processing
UNIVERSITY OF NAIROBI COLLEGE OF BIOLOGICAL AND PHYSICAL SCIENCES SCHOOL OF COMPUTING AND INFORMATICS An Evaluation of Real-Time Processing of Call Detail Records Using Stream Processing CATHERINE KITHUSI WAMBUA P53/73389/2014 A research project report submitted to the School of Computing and Informatics in partial fulfillment of the requirements for the award of the Degree of Masters of Science in Distributed Computing Technology at the University of Nairobi December 2017. DECLARATION I certify that this research project report to the best of my knowledge, is my original authorial work except as acknowledged therein and has not been submitted for any other degree or professional qualification award in this or any other University. Signature: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ Date: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ Catherine Kithusi Wambua (P53/73389/2014) This research report has been submitted in partial fulfillment of the requirements for the Degree of Master of Science in Distributed Computing Technology at the University of Nairobi with my approval as the University supervisor. Signature: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ Date: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ Dr. Christopher Chepken i | P a g e DEDICATION To my beloved parents, for their unrelenting dedication to ensuring that my siblings and I acquired the best education despite all odds. -
Code Smell Prediction Employing Machine Learning Meets Emerging Java Language Constructs"
Appendix to the paper "Code smell prediction employing machine learning meets emerging Java language constructs" Hanna Grodzicka, Michał Kawa, Zofia Łakomiak, Arkadiusz Ziobrowski, Lech Madeyski (B) The Appendix includes two tables containing the dataset used in the paper "Code smell prediction employing machine learning meets emerging Java lan- guage constructs". The first table contains information about 792 projects selected for R package reproducer [Madeyski and Kitchenham(2019)]. Projects were the base dataset for cre- ating the dataset used in the study (Table I). The second table contains information about 281 projects filtered by Java version from build tool Maven (Table II) which were directly used in the paper. TABLE I: Base projects used to create the new dataset # Orgasation Project name GitHub link Commit hash Build tool Java version 1 adobe aem-core-wcm- www.github.com/adobe/ 1d1f1d70844c9e07cd694f028e87f85d926aba94 other or lack of unknown components aem-core-wcm-components 2 adobe S3Mock www.github.com/adobe/ 5aa299c2b6d0f0fd00f8d03fda560502270afb82 MAVEN 8 S3Mock 3 alexa alexa-skills- www.github.com/alexa/ bf1e9ccc50d1f3f8408f887f70197ee288fd4bd9 MAVEN 8 kit-sdk-for- alexa-skills-kit-sdk- java for-java 4 alibaba ARouter www.github.com/alibaba/ 93b328569bbdbf75e4aa87f0ecf48c69600591b2 GRADLE unknown ARouter 5 alibaba atlas www.github.com/alibaba/ e8c7b3f1ff14b2a1df64321c6992b796cae7d732 GRADLE unknown atlas 6 alibaba canal www.github.com/alibaba/ 08167c95c767fd3c9879584c0230820a8476a7a7 MAVEN 7 canal 7 alibaba cobar www.github.com/alibaba/ -
A Novel Cloud Broker-Based Resource Elasticity Management and Pricing for Big Data Streaming Applications
A Novel Cloud Broker-based Resource Elasticity Management and Pricing for Big Data Streaming Applications by Olubisi A. Runsewe Thesis submitted to the Faculty of Graduate and Postdoctoral Studies in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Electronic Business School of Electrical Engineering and Computer Science Faculty of Engineering University of Ottawa c Olubisi A. Runsewe, Ottawa, Canada, 2019 Abstract The pervasive availability of streaming data from various sources is driving todays' enterprises to acquire low-latency big data streaming applications (BDSAs) for extracting useful information. In parallel, recent advances in technology have made it easier to collect, process and store these data streams in the cloud. For most enterprises, gaining insights from big data is immensely important for maintaining competitive advantage. However, majority of enterprises have difficulty managing the multitude of BDSAs and the complex issues cloud technologies present, giving rise to the incorporation of cloud service brokers (CSBs). Generally, the main objective of the CSB is to maintain the heterogeneous quality of service (QoS) of BDSAs while minimizing costs. To achieve this goal, the cloud, al- though with many desirable features, exhibits major challenges | resource prediction and resource allocation | for CSBs. First, most stream processing systems allocate a fixed amount of resources at runtime, which can lead to under- or over-provisioning as BDSA demands vary over time. Thus, obtaining optimal trade-off between QoS violation and cost requires accurate demand prediction methodology to prevent waste, degradation or shutdown of processing. Second, coordinating resource allocation and pricing decisions for self-interested BDSAs to achieve fairness and efficiency can be complex. -
Parte I Studio Delle Tecnologie Utili Per L'analisi, L'elaborazione E L'interrogazione Di Big Data
UNIVERSITÀ DEGLI STUDI DI MODENA E REGGIO EMILIA Dipartimento di Scienze Fisiche, Informatiche e Matematiche Corso di Laurea in Informatica Titolo Tesi Progettazione e sviluppo di un’applicazione Big Data per l’analisi e l’elaborazione di tweet in real-time RELATORE CANDIDATO Chiar.mo Professore Alessandro Pillo Riccardo Martoglia MATR. 111759 Anno Accademico 2018/2019 Indice Introduzione ………………………………….………………………………… pag. 1 Parte I Studio delle tecnologie utili per l’analisi, l’elaborazione e l’interrogazione di Big Data Capitolo I - “Ecosistema Hadoop” 1.1 Big Data ….………………………………….……………….………… pag. 3 1.2 Analisi derivate dalla figura ………………….…………….…….…… pag. 5 1.3 Processing layer …………………….……………………….………….. pag. 6 1.4 Distributed data processing & programming .…………….….…………. pag. 7 1.5 Sistemi analizzati in tabella .…………….……………..…….……..…… pag. 11 1.5.1 Apache Hadoop ……….….……………..………………………. pag. 11 1.5.2 Apache Apex ……….……….…………..………..……….….….. pag. 17 1.5.3 Apache Beam ……….…………………..…………………….…. pag. 20 1.5.4 Apache Flink……….…………………………………………..… pag. 24 1.5.5 Apache Samza …….……………..……………..……………….. pag. 30 1.5.6 Apache Spark .…….……………..……………………………… pag. 34 1.5.7 Apache Storm…….……………..……………………………….. pag. 38 1.5.8 Apache Tez .………….……………..……………………….…… pag. 40 1.5.9 Google MillWheel….……………..…………………………….. pag. 41 1.5.10 Google Cloud Dataflow…………..……………………………… pag. 41 1.5.11 IBM InfoSphere Streams ..………..………………………..…… pag. 43 1.5.12 Twitter Heron ………..………………..………………………… pag. 44 !I Capitolo II - “Machine Learning” 2.1 Introduzione al ML ………………………….………………………….. pag. 46 2.2 Algoritmi di machine learning …….…………………………………….. pag. 46 2.2.1 Training e Test Dataset ……….………………………………….. pag. 46 2.2.2 Fitting del modello: underfitting e overfitting ……………….. pag. 47 2.2.3 Apprendimento ………………………………………………….. pag. 49 2.3 Machine learning: “tradizionale” e “online” ………………………….… pag. 50 Parte II Studio di un’applicazione reale Capitolo III - “Applicazione reale: progetto” 3.1 Descrizione della realtà da analizzare ………………………………..….