DOCSLIB.ORG

An Introduction

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
An Introduction This chapter is from Social Media Mining: An Introduction. By Reza Zafarani, Mohammad Ali Abbasi, and Huan Liu. Cambridge University Press, 2014. Draft version: April 20, 2014. Complete Draft and Slides Available at: http://dmml.asu.edu/smm Chapter 10 Behavior Analytics What motivates individuals to join an online group? When individuals abandon social media sites, where do they migrate to? Can we predict box office revenues for movies from tweets posted by individuals? These questions are a few of many whose answers require us to analyze or predict behaviors on social media. Individuals exhibit different behaviors in social media: as individuals or as part of a broader collective behavior. When discussing individual be- havior, our focus is on one individual. Collective behavior emerges when a population of individuals behave in a similar way with or without coordi- nation or planning. In this chapter we provide examples of individual and collective be- haviors and elaborate techniques used to analyze, model, and predict these behaviors. 10.1 Individual Behavior We read online news; comment on posts, blogs, and videos; write reviews for products; post; like; share; tweet; rate; recommend; listen to music; and watch videos, among many other daily behaviors that we exhibit on social media. What are the types of individual behavior that leave a trace on social media? We can generally categorize individual online behavior into three cate- gories (shown in Figure 10.1): 319 Figure 10.1: Individual Behavior. 1. User-User Behavior. This is the behavior individuals exhibit with re- spect to other individuals. For instance, when befriending someone, sending a message to another individual, playing games, following, inviting, blocking, subscribing, or chatting, we are demonstrating a user-user behavior. 2. User-Community Behavior. The target of this type of behavior is a community. For example, joining or leaving a community, becoming a fan of a community, or participating in its discussions are forms of user-community behavior. 3. User-Entity Behavior. The target of this behavior is an entity in social media. For instance, it includes writing a blogpost or review or uploading a photo to a social media site. As we know, link data and content data are frequently available on social media. Link data represents the interactions users have with other users, and content data is generated by users when using social media. One can 320 think of user-user behavior as users linking to other users and user-entity behavior as users generating and consuming content. Users interacting with communities is a blend of linking and content-generation behavior, in which one can simply join a community (linking), read or write content for a community (content consumption and generation), or can do a mix of both activities. Link analysis and link prediction are commonly used to analyze links, and text analysis is designed to analyze content. We use these techniques to analyze, model, and predict individual behavior. 10.1.1 Individual Behavior Analysis Individual behavior analysis aims to understand how different factors affect individual behaviors observed online. It aims to correlate those behaviors (or their intensity) with other measurable characteristics of users, sites, or contents that could have possibly resulted in those behaviors. First we discuss an example of behavior analysis on social media and demonstrate how this behavior can be analyzed. After that, we outline the process that can be followed to analyze any behavior on social media. Community Membership in Social Media Users often join different communities in social media; the act of becom- ing a community member is an example of user-community behavior. Why do users join communities? In other words, what factors affect the community-joining behavior of individuals? To analyze community-joining behavior, we can observe users who join communities and determine the factors that are common among them. Hence, we require a population of users U = u ; u ;:::; un , a commu- f 1 2 g nity C, and community membership information (i.e., users ui U who are members of C). The community need not be explicitly defined.2 For instance, one can think of individuals buying a product as a community, and people buying the product for the first time as individuals joining the community. To distinguish between users who have already joined the community and those who are now joining it, we need community mem- berships at two different times: t1 and t2, with t2 > t1. At t2, we determine users such as u who are currently members of the community, but were not members at t1. These new users form the subpopulation that is analyzed for community-joining behavior. 321 Figure 10.2: Probability of Joining a Community (with Error Bars) as a Function of the Number of Friends m Already in the Community (from [19]). To determine factors that affect community-joining behavior, we can de- sign hypotheses based on different factors that describe when community- joining behavior takes place. We can verify these hypotheses by using data available on social media. The factors used in the validated hypotheses describe the behavior under study most accurately. One such hypothesis is that individuals are inclined toward an activity when their friends are engaged in the same activity. Thus, if the hypothesis is valid, a factor that plays a role in users joining a community is the number of their friends who are already members of the community. In data mining terms, this translates to using the number of friends of an individual in a community as a feature to predict whether the individual joins the community (i.e., class attribute). Figure 10.2 depicts the probability of joining a community with respect to the number of friends an individual has who are already members of the community. The probability increases Diminishing as more friends are in a community,but a diminishing returns property is also Returns observed, meaning that when enough friends are inside the community, more friends have no or only marginal effects on the likelihood of the individual’s act of joining the community. 322 Figure 10.3: User Community-Joining Behavior Features (from [19]). Thus far we have defined only one feature. However, one can go beyond a single feature. Figure 10.3 lists the comprehensive features that can be used to analyze community-joining behavior. As discussed, these features may or may not affect the joining behavior; thus, a validation procedure is required to understand their effect on the joining behavior. Which one of these features is more relevant to the joining behavior? In other words, which feature can help best determine whether individuals will join or not? To answer this question, we can use any feature selection algorithm. Fea- ture selection algorithms determine features that contribute the most to the prediction of the class attribute. Alternatively, we can use a classification algorithm, such as decision tree learning, to identify the relationship be- tween features and the class attribute (i.e., joined= Yes, No ). The earlier a feature is selected in the learned tree (i.e., is closerf to the rootg of the tree), the more important to the prediction of the class attribute value. By performing decision tree learning for a large dataset of users and the features listed in Figure 10.3, one finds that not only the number of friends inside a community but also how these friends are connected to each other affect the joining probability. In particular, the denser the subgraph of friends inside a community, the higher the likelihood of a user joining the community. Let S denote the set of friends inside community C, and let ES denote the set of edges between these S friends. The maximum number of S j j S edges between these S friends is j2j . So, the edge density is φ(S) = Es= j2j . 323 Figure 10.4: Decision Tree Learned for Community-Joining Behavior (from [19]). One finds that the higher this density, the more likely that one is going to join a community. Figure 10.4 shows the first two levels of the decision tree learned for this task using features described in Figure 10.3. Higher level features are more discriminative in decision tree learning, and in our case, the most important feature is the density of edges for the friends subgraph inside the community. To analyze community-joining behavior, one can design features that are likely to be related to community joining behavior. Decision tree learning can help identify which features are more predictive than oth- ers. However, how can we evaluate if these features are designed well and whether other features are not required to accurately predict joining behavior? Since classification is used to learn the relation between features and behaviors one can always use classification evaluation metrics such as accuracy to evaluate the performance of the learned model. An accurate model translates to an accurate learning of feature-behavior association. A Behavior Analysis Methodology The analysis of community-joining behavior can be summarized via a four-step methodology for behavioral analysis. The same approach can be followed as a general guideline for analyzing other behaviors in social media. Commonly, to perform behavioral analysis, one needs the following four components: 324 1. An observable behavior. The behavior that is analyzed needs to be observable. For instance, to analyze community-joining behavior, it is necessary to be able to accurately observe the joining of individuals (and possibly their joining times). 2. Features. One needs to construct relevant data features (covariates) that may or may not affect (or be affected by) the behavior. An- thropologists and sociologists can help design these features. The intrinsic relation between these features and the behavior should be clear from the domain expert’s point of view.
Load more

Recommended publications
  • A Survey of Network Performance Monitoring Tools
    A Survey of Network Performance Monitoring Tools Travis Keshav -- [email protected] Abstract In today's world of networks, it is not enough simply to have a network; assuring its optimal performance is key. This paper analyzes several facets of Network Performance Monitoring, evaluating several motivations as well as examining many commercial and public domain products. Keywords: network performance monitoring, application monitoring, flow monitoring, packet capture, sniffing, wireless networks, path analysis, bandwidth analysis, network monitoring platforms, Ethereal, Netflow, tcpdump, Wireshark, Ciscoworks Table Of Contents 1. Introduction 2. Application & Host-Based Monitoring 2.1 Basis of Application & Host-Based Monitoring 2.2 Public Domain Application & Host-Based Monitoring Tools 2.3 Commercial Application & Host-Based Monitoring Tools 3. Flow Monitoring 3.1 Basis of Flow Monitoring 3.2 Public Domain Flow Monitoring Tools 3.3 Commercial Flow Monitoring Protocols 4. Packet Capture/Sniffing 4.1 Basis of Packet Capture/Sniffing 4.2 Public Domain Packet Capture/Sniffing Tools 4.3 Commercial Packet Capture/Sniffing Tools 5. Path/Bandwidth Analysis 5.1 Basis of Path/Bandwidth Analysis 5.2 Public Domain Path/Bandwidth Analysis Tools 6. Wireless Network Monitoring 6.1 Basis of Wireless Network Monitoring 6.2 Public Domain Wireless Network Monitoring Tools 6.3 Commercial Wireless Network Monitoring Tools 7. Network Monitoring Platforms 7.1 Basis of Network Monitoring Platforms 7.2 Commercial Network Monitoring Platforms 8. Conclusion 9. References and Acronyms 1.0 Introduction http://www.cse.wustl.edu/~jain/cse567-06/ftp/net_perf_monitors/index.html 1 of 20 In today's world of networks, it is not enough simply to have a network; assuring its optimal performance is key.
    [Show full text]
  • Three-Dimensional Integrated Circuit Design: EDA, Design And
    Integrated Circuits and Systems Series Editor Anantha Chandrakasan, Massachusetts Institute of Technology Cambridge, Massachusetts For other titles published in this series, go to http://www.springer.com/series/7236 Yuan Xie · Jason Cong · Sachin Sapatnekar Editors Three-Dimensional Integrated Circuit Design EDA, Design and Microarchitectures 123 Editors Yuan Xie Jason Cong Department of Computer Science and Department of Computer Science Engineering University of California, Los Angeles Pennsylvania State University [email protected] [email protected] Sachin Sapatnekar Department of Electrical and Computer Engineering University of Minnesota [email protected] ISBN 978-1-4419-0783-7 e-ISBN 978-1-4419-0784-4 DOI 10.1007/978-1-4419-0784-4 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2009939282 © Springer Science+Business Media, LLC 2010 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com) Foreword We live in a time of great change.
    [Show full text]
  • Learning to Predict End-To-End Network Performance
    CORE Metadata, citation and similar papers at core.ac.uk Provided by BICTEL/e - ULg UNIVERSITÉ DE LIÈGE Faculté des Sciences Appliquées Institut d’Électricité Montefiore RUN - Research Unit in Networking Learning to Predict End-to-End Network Performance Yongjun Liao Thèse présentée en vue de l’obtention du titre de Docteur en Sciences de l’Ingénieur Année Académique 2012-2013 ii iii Abstract The knowledge of end-to-end network performance is essential to many Internet applications and systems including traffic engineering, content distribution networks, overlay routing, application- level multicast, and peer-to-peer applications. On the one hand, such knowledge allows service providers to adjust their services according to the dynamic network conditions. On the other hand, as many systems are flexible in choosing their communication paths and targets, knowing network performance enables to optimize services by e.g. intelligent path selection. In the networking field, end-to-end network performance refers to some property of a network path measured by various metrics such as round-trip time (RTT), available bandwidth (ABW) and packet loss rate (PLR). While much progress has been made in network measurement, a main challenge in the acquisition of network performance on large-scale networks is the quadratical growth of the measurement overheads with respect to the number of network nodes, which ren- ders the active probing of all paths infeasible. Thus, a natural idea is to measure a small set of paths and then predict the others where there are no direct measurements. This understanding has motivated numerous research on approaches to network performance prediction.
    [Show full text]
  • Designing Vertical Bandwidth Reconfigurable 3D Nocs for Many
    Designing Vertical Bandwidth Reconfigurable 3D NoCs for Many Core Systems Qiaosha Zou∗, Jia Zhany, Fen Gez, Matt Poremba∗, Yuan Xiey ∗ Computer Science and Engineering, The Pennsylvania State University, USA y Electrical and Computer Engineering, University of California, Santa Barbara, USA z Nanjing University of Aeronautics and Astronautics, China Email: ∗fqszou, [email protected], yfjzhan, [email protected], z [email protected] Abstract—As the number of processing elements increases in a single Routers chip, the interconnect backbone becomes more and more stressed when Core 1 Cache/ Cache/Memory serving frequent memory and cache accesses. Network-on-Chip (NoC) Memory has emerged as a potential solution to provide a flexible and scalable Core 0 interconnect in a planar platform. In the mean time, three-dimensional TSVs (3D) integration technology pushes circuit design beyond Moore’s law and provides short vertical connections between different layers. As a result, the innovative solution that combines 3D integrations and NoC designs can further enhance the system performance. However, due Core 0 Core 1 to the unpredictable workload characteristics, NoC may suffer from intermittent congestions and channel overflows, especially when the (a) (b) network bandwidth is limited by the area and energy budget. In this work, we explore the performance bottlenecks in 3D NoC, and then leverage Fig. 1. Overview of core-to-cache/memory 3D stacking. (a). Cores are redundant TSVs, which are conventionally used for fault tolerance only, allocated in all layers with part of cache/memory; (b). All cores are located as vertical links to provide additional channel bandwidth for instant in the same layers while cache/memory in others.
    [Show full text]
  • A Social Network Matrix for Implicit and Explicit Social Network Plates
    A Social Network Matrix for Implicit and Explicit Social Network Plates Wei Zhou1;4, Wenjing Duan2, Selwyn Piramuthu3;4 1Information & Operations Management, ESCP Europe, Paris, France 2Information Systems and Technology Management, George Washington University, U.S.A. 3Information Systems and Operations Management, University of Florida, U.S.A. Gainesville, Florida 32611-7169, USA 4RFID European Lab, Paris, France. [email protected], [email protected], selwyn@ufl.edu Abstract A majority of social network research deals with explicitly formed social networks. Although only rarely acknowledged for its existence, we believe that implicit social networks play a significant role in the overall dynamics of social networks. We propose a framework to evalu- ate the dynamics and characteristics of a set of explicit and associated implicit social networks. Specifically, we propose a social network ma- trix to measure the implicit relationships among the entities in various social networks. We also derive several indicators to characterize the dynamics in online social networks. We proceed by incorporating im- plicit social networks in a traditional network flow context to evaluate key network performance indicators such as the lowest communication cost, maximum information flow, and the budgetary constraints. Keywords: Implicit Social Network, Online Social Network 1 1 Introduction In recent years, several online social network platforms have witnessed huge public attention from social and financial perspectives. However, there are different facets to this interest. Facebook, for example, has gained a large set of users but failed to excel on profitability. Online social networks can be broadly classified as explicit and implicit social networks. Explicit social networks (e.g., Facebook, LinkedIn, Twitter, and MySpace) are where the users define the network by explicitly connect- ing with other users, possibly, but not necessarily, based on shared interests.
    [Show full text]
  • Social Network Analysis and Information Propagation: a Case Study Using Flickr and Youtube Networks
    International Journal of Future Computer and Communication, Vol. 2, No. 3, June 2013 Social Network Analysis and Information Propagation: A Case Study Using Flickr and YouTube Networks Samir Akrouf, Laifa Meriem, Belayadi Yahia, and Mouhoub Nasser Eddine, Member, IACSIT 1 makes decisions based on what other people do because their Abstract—Social media and Social Network Analysis (SNA) decisions may reflect information that they have and he or acquired a huge popularity and represent one of the most she does not. This concept is called ″herding″ or ″information important social and computer science phenomena of recent cascades″. Therefore, analyzing the flow of information on years. One of the most studied problems in this research area is influence and information propagation. The aim of this paper is social media and predicting users’ influence in a network to analyze the information diffusion process and predict the became so important to make various kinds of advantages influence (represented by the rate of infected nodes at the end of and decisions. In [2]-[3], the marketing strategies were the diffusion process) of an initial set of nodes in two networks: enhanced with a word-of-mouth approach using probabilistic Flickr user’s contacts and YouTube videos users commenting models of interactions to choose the best viral marketing plan. these videos. These networks are dissimilar in their structure Some other researchers focused on information diffusion in (size, type, diameter, density, components), and the type of the relationships (explicit relationship represented by the contacts certain special cases. Given an example, the study of Sadikov links, and implicit relationship created by commenting on et.al [6], where they addressed the problem of missing data in videos), they are extracted using NodeXL tool.
    [Show full text]
  • Network Performance Analysis
    Network Performance Analysis Guido Sanguinetti (based on notes by Neil Lawrence) Autumn Term 2007-2008 2 Schedule Lectures will take place in the following locations: Mondays 15:10 - 17:00 pm (St Georges LT1). Tuesdays 16:10 pm - 17:00 pm (St Georges LT1). Lab Classes In the Lewin Lab, Regent Court G12 (Monday Weeks 3 and 8). Week 1 Monday Lectures 1 & 2 Networks and Probability Review Tuesday Lecture 3 Poisson Processes Week 2 Monday Lecture 4 & 5 Birth Death Processes, M=M=1 queues Tuesday Lecture 6 Little's Formula Week 3 Monday Lab Class 1 Discrete Simulation of the M=M=1 Queue Tuesday Tutorial 1 Probability and Poisson Processes Week 4 Monday Lectures 7 & 8 Other Markov Queues and Erlang Delay Tuesday Lab Review 1 Review of Lab Class 1 Week 5 Monday Lectures 9 & 10 Erlang Loss and Markov Queues Review Tuesday Tutorial 2 M=M=1 and Little's Formula Week 6 Monday Lectures 11 & 12 M=G=1 and M=G=1 with Vacations Tuesday Lecture 13 M=G=1 contd Week 7 Monday Lectures 14 & 15 Networks of Queues, Jackson's Theorem Tuesday Tutorial 3 Erlang Delay and Loss, M=G=1 Week 8 Monday Lab Class 2 Simulation of Queues in NS2 Tuesday Lecture 16 ALOHA Week 9 Tuesday Lab Review 2 Review of NS2 Lab Class Week 10 Exam Practice Practice 2004-2005 Exam Week 12 Monday Exam Review Review of 2004-2005 Exam Evaluation This course is 100% exam. 3 4 Reading List The course is based primarily on Bertsekas and Gallager [1992].
    [Show full text]
  • A Framework for a Bandwidth Based Network Performance Model for CS Students
    A Framework for a Bandwidth Based Network Performance Model for CS Students D. Veal, G. Kohli, S. P. Maj J. Cooper Edith Cowan University Curtin University of Technology Western Australia Western Australia [email protected] Abstract There are currently various methods by which network and internetwork performance can be addressed. Examples include simulation modeling and analytical modeling which often results in models that are highly complex and often mathematically based (e.g. queuing theory). The authors have developed a new model which is based upon simple formulae derived from an investigation of computer networks. This model provides a conceptual framework for performance analysis based upon bandwidth considerations from a constructivist perspective. The bandwidth centric model uses high level abstraction decoupled from the implementation details of the underlying technology. This paper represents an initial attempt to develop this theory further in the field of networking education and presents a description of some of our work to date. This paper also includes details of experiments undertaken to measure bandwidth and formulae derived and applied to the investigation of converging data streams. Introduction There are many ways of approaching computer networking education. Davies notes that: “Network courses are often based on one or more of the following areas: The OSI model; Performance analysis; and Network simulation” 1. The OSI model is a popular approach that is used extensively in the Cisco Networking Academy Program (CNAP) 2 and in other Cisco learning materials. With respect to simulation Davis describes the Optimized Network Engineering Tools (OPNET) system that that can model networks and sub-networks, individual nodes and stations and state transition models that defines a node 1.
    [Show full text]
  • Network Information Theory
    Network Information Theory This comprehensive treatment of network information theory and its applications pro- vides the first unified coverage of both classical and recent results. With an approach that balances the introduction of new models and new coding techniques, readers are guided through Shannon’s point-to-point information theory, single-hop networks, multihop networks, and extensions to distributed computing, secrecy, wireless communication, and networking. Elementary mathematical tools and techniques are used throughout, requiring only basic knowledge of probability, whilst unified proofs of coding theorems are based on a few simple lemmas, making the text accessible to newcomers. Key topics covered include successive cancellation and superposition coding, MIMO wireless com- munication, network coding, and cooperative relaying. Also covered are feedback and interactive communication, capacity approximations and scaling laws, and asynchronous and random access channels. This book is ideal for use in the classroom, for self-study, and as a reference for researchers and engineers in industry and academia. Abbas El Gamal is the Hitachi America Chaired Professor in the School of Engineering and the Director of the Information Systems Laboratory in the Department of Electri- cal Engineering at Stanford University. In the field of network information theory, he is best known for his seminal contributions to the relay, broadcast, and interference chan- nels; multiple description coding; coding for noisy networks; and energy-efficient packet scheduling and throughput–delay tradeoffs in wireless networks. He is a Fellow of IEEE and the winner of the 2012 Claude E. Shannon Award, the highest honor in the field of information theory. Young-Han Kim is an Assistant Professor in the Department of Electrical and Com- puter Engineering at the University of California, San Diego.
    [Show full text]
  • Network Protocols and Algorithms
    Network Protocols And Algorithms Free-hearted Gary motorcycles gratifyingly. Hiro mate her pendentives dashingly, she silicifying it astray. Ignacius remains self-consuming after Jakob windlasses discontinuously or animadverts any repairers. The protocol best and failures in pegasis is layered model. This protocol sip or are protocols is concerned with this layer, and how a temperature rise vs. These networks to networking: implementations of algorithm sets of service providers point in order to find a tapped delay and learning algorithms conference registration and querying. As the networks that a, but bcrypt is based. Most striking developments is when network protocols and algorithms used in the algorithmic decisions are identiﬕed by phi at the server is followed by default code used? Getting disconnected in network protocol more complicated quite different networking and algorithm by establishing multipoint. Digital signature was designed to vpn uses the algorithmic and log files are reachable in computer. Geocast routing protocol sets up to network using three kinds of networks can join message. Sometimes protocols assume that support different. It is widely distributed algorithms combine algorithms publications and average number of algorithmic methods for large and discusses one in the selection algorithm indicate what encryption. Of improving the network design objective to distinguish it must deﬕne the collaborative strategies due time software is highlighted that are immediately neighboring node. Routing algorithm has been correctly implemented in a survey and efficient network topologies easily. The protocol is useful for future work process that although it increases considerably with network connectivity so that are focused on organizational consequences of us their helpful.
    [Show full text]
  • Machine Learning, Data Mining and Big Data Frameworks for Network Monitoring and Troubleshooting
    Machine learning, data mining and Big Data frameworks for network monitoring and troubleshooting Alessandro D'Alconzoa,∗, Pere Barlet-Rosb, Kensuke Fukudac, David Choffnesd aAIT, Austrian Institute of Technology, Vienna, Austria bUPC BarcelonaTech, Barcelona, Spain, cNational Institute of Informatics, Tokyo, Japan dNortheastern University, Boston, USA 1. Introduction work traffic monitoring and analysis domain re- mains poorly understood and investigated. The scale and the complexity of the Internet has Furthermore, critical applications such as detec- dramatically increased in the last few years. At the tion of anomalies, network attacks and intrusions, same time, Internet services are becoming increas- require fast mechanisms for online analysis of thou- ingly complex with the introduction of cloud infras- sands of events per second, as well as efficient tructures, Content Delivery Networks (CDNs) and techniques for offline analysis of massive histori- mobile Internet usage. This complexity will con- cal data. Statistical modeling, data mining and tinue to grow in the future with the rise of Machine- machine learning-based techniques able to detect, to-Machine communication and ubiquitous wear- characterize, and troubleshoot network anomalies able devices. In this scenario it becomes even more and security incidents, promise to efficiently shed compelling and challenging to design scalable net- light on this enormous amount of data. Nonethe- work traffic monitoring and analysis tools able to less, the unprecedented size of the data at hand shed light on the complex interplay between net- poses new performance and scalability challenges work infrastructure and the traffic profiles gener- to traditional methods and tools. ated by a continuously growing number of applica- The purpose of this special issue is to bring to- tions.
    [Show full text]
  • (Datacenter) Network
    Inside the Social Network’s (Datacenter) Network Arjun Roy, Hongyi Zengy, Jasmeet Baggay, George Porter, and Alex C. Snoeren Department of Computer Science and Engineering University of California, San Diego yFacebook, Inc. ABSTRACT 1. INTRODUCTION Large cloud service providers have invested in increasingly Datacenters are revolutionizing the way in which we de- larger datacenters to house the computing infrastructure re- sign networks, due in large part to the vastly different engi- quired to support their services. Accordingly, researchers neering constraints that arise when interconnecting a large and industry practitioners alike have focused a great deal of number of highly interdependent homogeneous nodes in a effort designing network fabrics to efficiently interconnect relatively small physical space, as opposed to loosely cou- and manage the traffic within these datacenters in perfor- pled heterogeneous end points scattered across the globe. mant yet efficient fashions. Unfortunately, datacenter oper- While many aspects of network and protocol design hinge ators are generally reticent to share the actual requirements on these physical attributes, many others require a firm un- of their applications, making it challenging to evaluate the derstanding of the demand that will be placed on the network practicality of any particular design. by end hosts. Unfortunately, while we understand a great Moreover, the limited large-scale workload information deal about the former (i.e., that modern cloud datacenters available in the literature has, for better or worse, heretofore connect 10s of thousands of servers using a mix of 10-Gbps largely been provided by a single datacenter operator whose Ethernet and increasing quantities of higher-speed fiber in- use cases may not be widespread.
    [Show full text]