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Sampling Bias in BitTorrent Measurements

Sampling Bias in BitTorrent Measurements

引用

16th international Euro-Par conference on parallel processing

作者： Zhang, Boxun Iosup, Alexandru Pouwelse, Johan Epema, Dick Sips, Henk Delft Univ Technol Parallel & Distributed Syst Grp Delft Netherlands

ISBN: (纸本)9783642152764

Real-world measurements play an important role in understanding the characteristics and in improving the operation of Bit Torrent, which is currently a popular Internet application. Much like measuring the Internet, the complexity and scale of the Bit Torrent network make a single, complete measurement impractical. While a large number of measurements have already employed diverse sampling techniques to study parts of Bit Torrent network, until now there exists no investigation of their sampling bias, that is, of their ability to objectively represent the characteristics of Bit Torrent. In this work we present the first study of the sampling bias in Bit Torrent measurements. We first introduce a novel taxonomy of sources of sampling bias in Bit Torrent measurements. We then investigate the sampling among fifteen long-term Bit Torrent measurements completed between 2004 and 2009, and find that different data sources and measurement techniques can lead to significantly different measurement results. Last, we formulate three recommendations to improve the design of future Bit Torrent measurements, and estimate the cost of using these recommendations in practice.

关键词： Computers

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Programming and Managing the Swarm - An Operating System for an Emerging System of Mobile Devices

Programming and Managing the Swarm - An Operating System for...

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IEEE 9th international conference on Mobile Ad-hoc and Sensor Networks (MSN)

作者： Graff, Daniel Richling, Jan Werner, Matthias Tech Univ Berlin Commun & Operating Syst Grp D-10587 Berlin Germany Tech Univ Chemnitz Operating Syst Grp D-09111 Chemnitz Germany

ISBN: (纸本)9780769551593

Todays situation is characterized by an increasing pervasiveness of a plethora of mobile devices featuring different capabilities and exhibiting different system interfaces making the handling of these devices and especially the cooperation between different devices a complex task. In this paper, we consider the sum of all these devices as one emerging system (the swarm) and present an approach of a swarm operating system that on a systemic level manages these devices (local devices give up their autonomy) while providing a common interface to user applications. We provide a programming model for distributed mobile applications that abstracts from error-prone aspects such as distribution and concurrency by giving the programmer a systemic view to system resources. The model allows the programmer to define actions that can be restricted in space and time. Together with a high level goal, an entire application emerges implicitly based on those defined actions. In order to execute such applications, we present an architecture for a runtime system that uses virtualization techniques in order to execute multiple independently developed applications in parallel. The system follows a service-oriented architecture: one of the core services is the space-time scheduler that plans applications (a set of actions) in time and space.

关键词： cyber-physical systems spatial-temporal constraints coordination real space-time distributed runtime environment context awareness

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Achieving multilevel elasticity for distributed stream processing systems in the cloud environment: A review and conceptual framework 2022

Achieving multilevel elasticity for distributed stream proce...

引用

14th international conference on Contemporary Computing, IC3 2022

作者： Thakkar, Riddhi Bhavsar, Madhuri Institute of Technology Nirma University Gujarat Ahmedabad India

ISBN: (纸本)9781450396752

Recent awareness and advances in technology have triggered excessive use of social media, IoT devices, remote sensing devices, mobile applications, web applications, and gaming more than ever before in time. Such platforms are hosting their applications on the cloud as it provides various services on a pay-per-use basis. A Cloud Service Provider (CSP) should deliver all its services very swiftly to process real-time applications on time. Real-time stream computations are characteristically long-lived and receive data in an unpredictable form, requiring a fair amount of resources for their processing in constrained time. Such a dynamic nature of applications demands resource elasticity at runtime. The cloud architecture is stacked with different types of resources, each having a discrete adaption process with distinct elasticity properties. Scaling the absolute amount of resources leads to performance boosting. Recent literature landscapes the elasticity at Virtual Machine (VM) level, describing various techniques for scaling VMs. Each technique targets a distinct aspect with specific assumptions. However, the literature lacks a comprehensive survey at the operator level, where actual processing takes place and has a higher impact on the performance of the system. Compared to other works in the literature, this work presents a detailed analysis of various approaches targeting elasticity at the operator level of cloud architecture for stream processing applications, along with the conceptual framework, scaling at the operator, VM, and server levels. We have also discussed the various elastic approaches for scaling the resources at multilevel: VM and operator-level concurrently, for distributed Stream processing (DSP) applications running on the cloud. Conceptually, with the proposed framework, we can attain maximum resource utilization at each layer. In future work, we will evaluate the proposed framework with real-world application. © 2022 ACM.

关键词： Big data

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Sentiment Analysis Approaches, Types, Challenges, and applications: An Exploratory Analysis 7

Sentiment Analysis Approaches, Types, Challenges, and Applic...

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7th international conference on parallel, distributed and Grid Computing, PDGC 2022

作者： Devi, Vandana Sharma, Avinash Mmdu Computer Science and Engineering Mullana Ambala India

ISBN: (纸本)9781665454018

Even though sociolinguistic and psycholinguistic studies demonstrate that the qualities of the speaker and the intended audience have an impact on the message that is transmitted, the majority of NLP models used today assume language as universal. The difficulty of sentiment analysis, often known as opinion mining, in natural language processing (NLP) is one that is currently being faced. It's also challenging for people to share their ideas on websites like Facebook, Twitter, and Yelp since innovation has grown dramatically. Social media's widespread use has resulted in a huge volume of data generation, including evaluations, comments, and opinions. Researchers claim that the phrase, document, aspect, and user levels are the foundations for sentiment analysis. This data requires more time to analyse and is challenging to process. In order to categorize or determine positive, negative, and neutral opinions, an intelligent system must be developed. © 2022 IEEE.

关键词： Sentiment analysis

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From the Cloud to Edge and IoT: a Smart Orchestration Architecture for Enabling Osmotic Computing 32

From the Cloud to Edge and IoT: a Smart Orchestration Archit...

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32nd IEEE international conference on Advanced Information Networking and applications (IEEE AINA)

作者： Carnevale, Lorenzo Celesti, Antonio Galletta, Antonino Dustdar, Schahram Villari, Massimo Univ Messina Dept Math & Comp Sci Phys & Earth Sci Viale F Stagno DAlcontres 31 I-98166 Messina Italy TU Wien Distributed Syst Grp Argentinierstr 8 A-1040 Vienna Austria GNCS Grp Nazl Calcolo Sci INdAM Ple Aldo Moro 5 I-00185 Rome Italy

ISBN: (纸本)9781538653951

The latest technological and conceptual developments have destroyed the centralized Cloud Computing model, moving Cloud services in emerging ICT infrastructures such as Edge, Fog and Internet of Things (IoT) that are closer to end users. Specifically, current Cloud computing programming models and resource orchestration techniques are challenged by the recent evolution of the IoT phenomenon because smart devices are becoming more and more pervasive, powerful and inexpensive. Therefore, services need to be place near such devices. In this regard, the Osmotic Computing aims to provide a new computing paradigm based on the deployment and migration strategies related to the infrastructures and applications requirements across Cloud, Edge, Fog an IoT layers. In this scientific paper, we investigate the Smart Orchestration of a new software abstraction called MicroELement (MEL), that encapsulates resources, services and data necessary to run IoT applications. Several use cases are presented for describing the Artificial Intelligence processes that enables the MELs deployment.

关键词： Osmotic Computing Edge Computing Cloud Computing IoT Orchestration Elasticity Artificial Intelligence

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3rd international Symposium on Signal and Image processing, ISSIP 2020

3rd International Symposium on Signal and Image Processing, ...

引用

3rd international Symposium on Signal and Image processing, ISSIP 2020

ISBN: (纸本)9789813369658

The proceedings contain 16 papers. The special focus in this conference is on Signal and Image processing. The topics include: Deep Convolutional Neural Network-Based Diagnosis of Invasive Ductal Carcinoma;speaker Identification in Spoken Language Mismatch Condition: An Experimental Study;Ultrasound Image Classification Using ACGAN with Small Training Dataset;preface;Chaotic Ions Motion Optimization (CIMO) for Biological Sequences Local Alignment: COVID-19 as a Case Study;assessment of Eyeball Movement and Head Movement Detection Based on Reading;using Hadoop Ecosystem and Python to Explore Climate Change;a Brief Review of Intelligent Rule Extraction techniques;the Effect of Different Feature Selection Methods for Classification of Melanoma;intelligent Hybrid Technique to Secure Bluetooth Communications;parallel Algorithm to find Integer k where a given Well-distributed Graph is k-Metric Dimensional;a Fog-Based Retrieval of Real-Time Data for Health applications;differential Evolution-Based Shot Boundary Detection Algorithm for Content-Based Video Retrieval;qutrit-Based Genetic Algorithm for Hyperspectral Image Thresholding.

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Optimizing message aggregation for parallel simulation on high performance clusters

Optimizing message aggregation for parallel simulation on hi...

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Proceedings of the 1999 7th international Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems - MASCOTS '99

作者： Pham, C.D. Albrecht, C. Universite Claude Bernard Lyon I France

High performance clusters (HPCs) based on commodity hardware are becoming more and more popular in the parallel computing community. These new platforms offer a hardware capable of a very low latency and a very high throughput at an unbeatable cost, making them attractive for a large variety of parallel and distributed applications. With adequate communication software, HPCs have the potential to achieve a level of performance similar to massively parallel computers. However, for parallel applications that present a high communication/computation ratio, it is still essential to provide the lowest latency in order to minimize the communication overhead. In this paper, we are investigating message aggregation techniques to improve parallel simulations of fine-grain ATM communication network models. Even if message aggregation is a well-known solution for improving the communication performance of high latency interconnection networks, the complex interaction between message aggregation and the underlying communication software is often ignored. We show that message aggregation must carefully take into account the characteristics of the communication software to be efficient on an HPC. This methodology can be applied as a preliminary step to tune a message aggregation algorithm for a given combination of hardware architecture and communication software layer.

关键词： parallel processing systems

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11th Annual international Phoenix conference on Computers and Communication, IPCCC 1992 - Proceedings

11th Annual International Phoenix Conference on Computers an...

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11th Annual international Phoenix conference on Computers and Communication, IPCCC 1992

ISBN: (纸本)0780306058

The proceedings contain 104 papers. The topics discussed include: incrementally extensible hypercube (IEH) graphs;efficient distributed routing algorithms for a synchronous circuit-switched hypercube;distributed algorithms for shortest-path, deadlock-free routing and broadcasting in Fibonacci cubes;predicting the limits of multiple processor performance using job profiles;performance modeling of a shared-memory multiprocessor system;the performance of local and global scheduling strategies in multi-programmed parallel systems;partially shared variables and hierarchical shared memory multiprocessor architectures;and throughput enhancement in multiprocessor architectures for pipelining and digital signal processing applications.

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Integrating parallel and distributed computing topics into an undergraduate CS curriculum

Integrating parallel and distributed computing topics into a...

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2013 IEEE 37th Annual Computer Software and applications conference, COMPSAC 2013

作者： Danner, Andrew Newhall, Tia Computer Science Department Swarthmore College Swarthmore PA 19081 United States

ISBN: (纸本)9780769549798

We present changes to our undergraduate computer science curriculum for a small liberal arts college. The changes are designed to incorporate parallel and distributed computing topics into all levels of our curriculum, with the goal of ensuring that all graduating CS majors have exposure to, and experience with, parallel and distributed computing. Our effort is motivated by the ACM/IEEE Ironman Curriculum, which includes a increased focus on these important topics. In addition, we use the NSF/IEEE-TCPP model curriculum as a guide in our effort. Because of the small size of our department, and the breadth constraints of a liberal arts college, we face some unique challenges. Our multi-year effort involves at least six courses in our curriculum. Of these courses, one is a new introductory-level course, while the others are existing courses whose content has been modified to include more focus on these important topics. We present our curricular changes and we discuss an initial evaluation of the first implementation of these changes. © 2013 IEEE.

关键词： Curricula

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Sentiment Classification System of Twitter Data for US Airline Service Analysis 42

Sentiment Classification System of Twitter Data for US Airli...

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42nd Annual IEEE-Computer-Society Computers, Software and applications (COMPSAC) conference - Staying Smarter in a Smartening World

作者： Rane, Ankita Kumar, Anand BITS Pilani Comp Sci & Engn Dubai Campus Dubai U Arab Emirates BITS Pilani Elect & Elect Engn Dubai Campus Dubai U Arab Emirates

ISBN: (纸本)9781538626672

The airline industry is a very competitive market which has grown rapidly in the past 2 decades. Airline companies resort to traditional customer feedback forms which in turn are very tedious and time consuming. This is where Twitter data serves as a good source to gather customer feedback tweets and perform a sentiment analysis. In this paper, we worked on a dataset comprising of tweets for 6 major US Airlines and performed a multi-class sentiment analysis. This approach starts off with pre-processing techniques used to clean the tweets and then representing these tweets as vectors using a deep learning concept (Doc2vec) to do a phrase-level analysis. The analysis was carried out using 7 different classification strategies: Decision Tree, Random Forest, SVM, K-Nearest Neighbors, Logistic Regression, Gaussian Naive Bayes and AdaBoost. The classifiers were trained using 80% of the data and tested using the remaining 20% data. The outcome of the test set is the tweet sentiment (positive/negative/neutral). Based on the results obtained, the accuracies were calculated to draw a comparison between each classification approach and the overall sentiment count was visualized combining all six airlines.

关键词： Machine Learning Classification techniques Deep Learning distributed Memory Model

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