bluuebunny/crossref_metadata_2025_split huggingface dataset:
Data center TCP (DCTCP)
Authors: Alizadeh Mohammad , Greenberg Albert , Maltz David A. , Padhye Jitendra , Patel Parveen , Prabhakar Balaji , Sengupta Sudipta , Sridharan Murari 
Published: October 2012

Cloud data centers host diverse applications, mixing workloads that require small predictable latency with others requiring large sustained throughput. In this environment, today's state-of-the-art TCP protocol falls short. We present measurements of a 6000 server production cluster and reveal impairments that lead to high application latencies, rooted in TCP's demands on the limited buffer space available in data center switches. For example, bandwidth hungry "background" flows build up queues at the switches, and thus impact the performance of latency sensitive "foreground" traffic.
To address these problems, we propose DCTCP, a TCP-like protocol for data center networks. DCTCP leverages Explicit Congestion Notification (ECN) in the network to provide multi-bit feedback to the end hosts. We evaluate DCTCP at 1 and 10Gbps speeds using commodity, shallow buffered switches. We find DCTCP delivers the same or better throughput than TCP, while using 90% less buffer space. Unlike TCP, DCTCP also provides high burst tolerance and low latency for short flows. In handling workloads derived from operational measurements, we found DCTCP enables the applications to handle 10X the current background traffic, without impacting foreground traffic. Further, a 10X increase in foreground traffic does not cause any timeouts, thus largely eliminating incast problems.
Differentially-private network trace analysis
Authors: McSherry Frank , Mahajan Ratul 
Published: October 2012

We consider the potential for network trace analysis while providing the guarantees of "differential privacy." While differential privacy provably obscures the presence or absence of individual records in a dataset, it has two major limitations: analyses must (presently) be expressed in a higher level declarative language; and the analysis results are randomized before returning to the analyst.
We report on our experiences conducting a diverse set of analyses in a differentially private manner. We are able to express all of our target analyses, though for some of them an approximate expression is required to keep the error-level low. By running these analyses on real datasets, we find that the error introduced for the sake of privacy is often (but not always) low even at high levels of privacy. We factor our learning into a toolkit that will be likely useful for other analyses. Overall, we conclude that differential privacy shows promise for a broad class of network analyses.
Fine-grained channel access in wireless LAN
Authors: Tan Kun , Fang Ji , Zhang Yuanyang , Chen Shouyuan , Shi Lixin , Zhang Jiansong , Zhang Yongguang 
Published: October 2012

Modern communication technologies are steadily advancing the physical layer (PHY) data rate in wireless LANs, from hundreds of Mbps in current 802.11n to over Gbps in the near future. As PHY data rates increase, however, the overhead of media access control (MAC) progressively degrades data throughput efficiency. This trend reflects a fundamental aspect of the current MAC protocol, which allocates the channel as a single resource at a time.
This paper argues that, in a high data rate WLAN, the channel should be divided into separate subchannels whose width is commensurate with PHY data rate and typical frame size. Multiple stations can then contend for and use subchannels simultaneously according to their traffic demands, thereby increasing overall efficiency. We introduce FICA, a fine-grained channel access method that embodies this approach to media access using two novel techniques. First, it proposes a new PHY architecture based on OFDM that retains orthogonality among subchannels while relying solely on the coordination mechanisms in existing WLAN, carrier-sensing and broadcasting. Second, FICA employs a frequency-domain contention method that uses physical layer RTS/CTS signaling and frequency domain backoff to efficiently coordinate subchannel access. We have implemented FICA, both MAC and PHY layers, using a software radio platform, and our experiments demonstrate the feasibility of the FICA design. Further, our simulation results suggest FICA can improve the efficiency ratio of WLANs by up to 400% compared to existing 802.11.
NetShield
Authors: Li Zhichun , Xia Gao , Gao Hongyu , Tang Yi , Chen Yan , Liu Bin , Jiang Junchen , Lv Yuezhou 
Published: October 2012

Accuracy and speed are the two most important metrics for Network Intrusion Detection/Prevention Systems (NIDS/NIPSes). Due to emerging polymorphic attacks and the fact that in many cases regular expressions (regexes) cannot capture the vulnerability conditions accurately, the accuracy of existing regex-based NIDS/NIPS systems has become a serious problem. In contrast, the recently-proposed vulnerability signatures (a.k.a data patches) can exactly describe the vulnerability conditions and achieve better accuracy. However, how to efficiently apply vulnerability signatures to high speed NIDS/NIPS with a large ruleset remains an untouched but challenging issue.
This paper presents the first systematic design of vulnerability signature based parsing and matching engine, NetShield, which achieves multi-gigabit throughput while offering much better accuracy. Particularly, we made the following contributions: (i) we proposed a candidate selection algorithm which efficiently matches thousands of vulnerability signatures simultaneously requiring a small amount of memory; (ii) we proposed an automatic lightweight parsing state machine achieving fast protocol parsing. Experimental results show that the core engine of NetShield achieves at least 1.9+Gbps signature matching throughput on a 3.8GHz single-core PC, and can scale-up to at least 11+Gbps under a 8-core machine for 794 HTTP vulnerability signatures.
California fault lines
Authors: Turner Daniel , Levchenko Kirill , Snoeren Alex C. , Savage Stefan 
Published: October 2012

Of the major factors affecting end-to-end service availability, network component failure is perhaps the least well understood. How often do failures occur, how long do they last, what are their causes, and how do they impact customers? Traditionally, answering questions such as these has required dedicated (and often expensive) instrumentation broadly deployed across a network.
We propose an alternative approach: opportunistically mining "low-quality" data sources that are already available in modern network environments. We describe a methodology for recreating a succinct history of failure events in an IP network using a combination of structured data (router configurations and syslogs) and semi-structured data (email logs). Using this technique we analyze over five years of failure events in a large regional network consisting of over 200 routers; to our knowledge, this is the largest study of its kind.
An analysis of social network-based Sybil defenses
Authors: Viswanath Bimal , Post Ansley , Gummadi Krishna P. , Mislove Alan 
Published: October 2012

Recently, there has been much excitement in the research community over using social networks to mitigate multiple identity, or Sybil, attacks. A number of schemes have been proposed, but they differ greatly in the algorithms they use and in the networks upon which they are evaluated. As a result, the research community lacks a clear understanding of how these schemes compare against each other, how well they would work on real-world social networks with different structural properties, or whether there exist other (potentially better) ways of Sybil defense.
In this paper, we show that, despite their considerable differences, existing Sybil defense schemes work by detecting local communities (i.e., clusters of nodes more tightly knit than the rest of the graph) around a trusted node. Our finding has important implications for both existing and future designs of Sybil defense schemes. First, we show that there is an opportunity to leverage the substantial amount of prior work on general community detection algorithms in order to defend against Sybils. Second, our analysis reveals the fundamental limits of current social network-based Sybil defenses: We demonstrate that networks with well-defined community structure are inherently more vulnerable to Sybil attacks, and that, in such networks, Sybils can carefully target their links in order make their attacks more effective.
The little engine(s) that could
Authors: Pujol Josep M. , Erramilli Vijay , Siganos Georgos , Yang Xiaoyuan , Laoutaris Nikos , Chhabra Parminder , Rodriguez Pablo 
Published: October 2012

The difficulty of scaling Online Social Networks (OSNs) has introduced new system design challenges that has often caused costly re-architecting for services like Twitter and Facebook. The complexity of interconnection of users in social networks has introduced new scalability challenges. Conventional vertical scaling by resorting to full replication can be a costly proposition. Horizontal scaling by partitioning and distributing data among multiples servers - e.g. using DHTs - can lead to costly inter-server communication.
We design, implement, and evaluate SPAR, a social partitioning and replication middle-ware that transparently leverages the social graph structure to achieve data locality while minimizing replication. SPAR guarantees that for all users in an OSN, their direct neighbor's data is co-located in the same server. The gains from this approach are multi-fold: application developers can assume local semantics, i.e., develop as they would for a single server; scalability is achieved by adding commodity servers with low memory and network I/O requirements; and redundancy is achieved at a fraction of the cost.
We detail our system design and an evaluation based on datasets from Twitter, Orkut, and Facebook, with a working implementation. We show that SPAR incurs minimum overhead, and can help a well-known open-source Twitter clone reach Twitter's scale without changing a line of its application logic and achieves higher throughput than Cassandra, Facebook's DHT based key-value store database.
Accelerating SSL with GPUs
Authors: Jang Keon , Han Sangjin , Han Seungyeop , Moon Sue , Park KyoungSoo 
Published: October 2012

SSL/TLS is a standard protocol for secure Internet communication. Despite its great success, today's SSL deployment is largely limited to security-critical domains. The low adoption rate of SSL is mainly due to high computation overhead on the server side.
In this paper, we propose Graphics Processing Units (GPUs) as a new source of computing power to reduce the server-side overhead. We have designed and implemented an SSL proxy that opportunistically offloads cryptographic operations to GPUs. The evaluation results show that our GPU implementation of cryptographic operations, RSA, AES, and HMAC-SHA1, achieves high throughput while keeping the latency low. The SSL proxy significantly boosts the throughput of SSL transactions, handling 25.8K SSL transactions per second, and has comparable response time even when overloaded.
PixNet
Authors: Perli Samuel David , Ahmed Nabeel , Katabi Dina 
Published: October 2012

Given the abundance of cameras and LCDs in today's environment, there exists an untapped opportunity for using these devices for communication. Specifically, cameras can tune to nearby LCDs and use them for network access. The key feature of these LCD-camera links is that they are highly directional and hence enable a form of interference-free wireless communication. This makes them an attractive technology for dense, high contention scenarios. The main challenge, however, to enable such LCD-camera links is to maximize coverage, that is to deliver multiple Mb/s over multi-meter distances, independent of the view angle. To do so, these links need to address unique types of channel distortions, such as perspective distortion and blur.
In this demo, we show how these LCD-camera links can be used to wirelessly transmit information. We present PixNet, an LCD-camera communication system. PixNet generalizes the popular OFDM transmission algorithms to address the unique properties of the LCD-camera link, including perspective distortion and blur. We have built a prototype of PixNet using off-the-shelf LCDs and cameras. In our demo, we will show our prototype communicating data from an LCD to a camera-equipped PC, over multi-meter distances and wide viewing angles.
VMtorrent
Authors: Reich Joshua , Laadan Oren , Brosh Eli , Sherman Alex , Misra Vishal , Nieh Jason , Rubenstein Dan 
Published: October 2012

Virtual Appliances (VAs) are Virtual Machines (VMs) geared towards a specific set of tasks. They require little or no configuration, working out-of-the-box. VAs fit neatly into the Cloud Computing paradigm - many copies of an identical machine can be launched in a data center, or home/business users can grab the appliance they need from the cloud to run locally just for so long as required. Companies and projects whose sole offerings are VAs ready for either desktop or data center use [3, 11] attest to the growing popularity of VAs. VMware's Appliance directory alone currently lists over 1400 VAs available for the VMware family of Virtual Machine Monitors (VMMs) [13].
Current VA distribution generally requires download of the complete virtual disk image, only after which the VA can be run. Given that compressed VA sizes run anywhere from several hundred MB to a few GB, there can be significant delays from the time a user decides he/she wants to run a particular VA until the time that VA can be used. These problems are only exacerbated when demand for particular VAs spikes and server bandwidth resources become the distribution bottleneck.
Circumventing censorship with collage
Authors: Burnett Sam , Feamster Nick , Vempala Santosh 
Published: October 2012

Oppressive regimes and even democratic governments restrict Internet access. Existing anti-censorship systems often require users to connect through proxies, but these systems are relatively easy for a censor to discover and block. We explore a possible next step in the censorship arms race: rather than relying on a single system or set of proxies to circumvent censorship firewalls, we use the vast deployment of sites that host user-generated content to breach these firewalls. We have developed Collage, which allows users to exchange messages through hidden channels in sites that host user-generated content. To send a message, a user embeds it into cover traffic and posts the content on some site, where receivers retrieve this content. Collage makes it difficult for a censor to monitor or block these messages by exploiting the sheer number of sites where users can exchange messages and the variety of ways that a message can be hidden.
We have built a censorship-resistant news reader using Collage that can retrieve from behind a censorship firewall and show Collage's effectiveness with a live demonstration of its complete infrastructure.
Twittering by cuckoo
Authors: Xu Tianyin , Chen Yang , Fu Xiaoming , Hui Pan 
Published: October 2012

Online microblogging services, as exemplified by Twitter, have become immensely popular during the latest years. However, current microblogging systems severely suffer from performance bottlenecks and malicious attacks due to the centralized architecture. As a result, centralized microblogging systems may threaten the scalability, reliability as well as availability of the offered services, not to mention the high operational and maintenance cost.
This demo presents a decentralized, socio-aware microblogging system named Cuckoo. The key aspects of Cuckoo's design is to take advantage of the inherent social relations while leveraging peer-to-peer (P2P) techniques in order to provide scalable, reliable microblogging services. The demo will show these aspects of Cuckoo and provide insights on the performance gain that decentralization and socio-awareness can bring for microblogging systems.
A constraint-based approach to scheduling an individual's activities
Authors: Refanidis Ioannis , Yorke-Smith Neil 
Published: October 2012

The goal of helping to automate the management of an individual's time is ambitious in terms both of knowledge engineering and of the quality of the plans produced by an AI system. Modeling an individual's activities is itself a challenge, due to the variety of activity, constraint, and preference types involved. Activities might be simple or interruptible; they might have fixed or variable durations, constraints over their temporal domains, and binary constraints between them. Activities might require the individual being at specific locations in order, whereas traveling time should be taken into account. Some activities might require exclusivity, whereas others can be overlapped with compatible concurrent activities. Finally, while scheduled activities generate utility for the individual, extra utility might result from the way activities are scheduled in time, individually and in conjunction.
This article presents a rigorous, expressive model to represent an individual's activities, that is, activities whose scheduling is not contingent on any other person. Joint activities such as meetings are outside our remit; it is expected that these are arranged manually or through negotiation mechanisms and they are considered as fixed busy times in the individual's calendar. The model, formulated as a constraint optimization problem, is general enough to accommodate a variety of situations. We present a scheduler that operates on this rich model, based on the general squeaky wheel optimization framework and enhanced with domain-dependent heuristics and forward checking. Our empirical evaluation demonstrates both the efficiency and the effectiveness of the selected approach. Part of the work described has been implemented in the SelfPlanner system, a Web-based intelligent calendar application that utilizes Google Calendar.
Social Network Analysis and Mining for Business Applications
Authors: Bonchi Francesco , Castillo Carlos , Gionis Aristides , Jaimes Alejandro 
Published: October 2012

Social network analysis has gained significant attention in recent years, largely due to the success of online social networking and media-sharing sites, and the consequent availability of a wealth of social network data. In spite of the growing interest, however, there is little understanding of the potential business applications of mining social networks. While there is a large body of research on different problems and methods for social network mining, there is a gap between the techniques developed by the research community and their deployment in real-world applications. Therefore the potential business impact of these techniques is still largely unexplored.In this article we use a business process classification framework to put the research topics in a business context and provide an overview of what we consider key problems and techniques in social network analysis and mining from the perspective of business applications. In particular, we discuss data acquisition and preparation, trust, expertise, community structure, network dynamics, and information propagation. In each case we present a brief overview of the problem, describe state-of-the art approaches, discuss business application examples, and map each of the topics to a business process classification framework. In addition, we provide insights on prospective business applications, challenges, and future research directions. The main contribution of this article is to provide a state-of-the-art overview of current techniques while providing a critical perspective on business applications of social network analysis and mining.
Watch the Story Unfold with TextWheel
Authors: Cui Weiwei , Qu Huamin , Zhou Hong , Zhang Wenbin , Skiena Steve 
Published: October 2012

Keyword-based searching and clustering of news articles have been widely used for news analysis. However, news articles usually have other attributes such as source, author, date and time, length, and sentiment which should be taken into account. In addition, news articles and keywords have complicated macro/micro relations, which include relations between news articles (i.e., macro relation), relations between keywords (i.e., micro relation), and relations between news articles and keywords (i.e., macro-micro relation). These macro/micro relations are time varying and pose special challenges for news analysis.
In this article we present a visual analytics system for news streams which can bring multiple attributes of the news articles and the macro/micro relations between news streams and keywords into one coherent analytical context, all the while conveying the dynamic natures of news streams. We introduce a new visualization primitive called TextWheel which consists of one or multiple keyword wheels, a document transportation belt, and a dynamic system which connects the wheels and belt. By observing the TextWheel and its content changes, some interesting patterns can be detected. We use our system to analyze several news corpora related to some major companies and the results demonstrate the high potential of our method.
Context-Aware Semi-Local Feature Detector
Authors: Ji Rongrong , Yao Hongxun , Tian Qi , Xu Pengfei , Sun Xiaoshuai , Liu Xianming 
Published: October 2012

How can interest point detectors benefit from contextual cues? In this articles, we introduce a context-aware semi-local detector (CASL) framework to give a systematic answer with three contributions: (1) We integrate the context of interest points to recurrently refine their detections. (2) This integration boosts interest point detectors from the traditionally local scale to a semi-local scale to discover more discriminative salient regions. (3) Such context-aware structure further enables us to bring forward category learning (usually in the subsequent recognition phase) into interest point detection to locate category-aware, meaningful salient regions.
Our CASL detector consists of two phases. The first phase accumulates multiscale spatial correlations of local features into a difference of contextual Gaussians (DoCG) field. DoCG quantizes detector context to highlight contextually salient regions at a semi-local scale, which also reveals visual attentions to a certain extent. The second phase locates contextual peaks by mean shift search over the DoCG field, which subsequently integrates contextual cues into feature description. This phase enables us to integrate category learning into mean shift search kernels. This learning-based CASL mechanism produces more category-aware features, which substantially benefits the subsequent visual categorization process. We conducted experiments in image search, object characterization, and feature detector repeatability evaluations, which reported superior discriminability and comparable repeatability to state-of-the-art works.