Content providers build serving stacks to deliver content to users. An important goal of a content provider is to ensure good user experience, since user experience has an impact on revenue. In this paper, we describe a system at Yahoo called YTrace that diagnoses bad user experience in near real time. We present the different components of YTrace for end-to-end multi-layer diagnosis (instrumentation, methods and backend system), and the system architecture for delivering diagnosis in near real time across all user sessions at Yahoo. YTrace diagnoses problems across service and network layers in the end-to-end path spanning user host, Internet, CDN and the datacenters, and has three diagnosis goals: detection, localization and root cause analysis (including cascading problems) of performance problems in user sessions with the cloud. The key component of the methods in YTrace is capturing and discovering causality, which we design based on a mix of instrumentation API, domain knowledge and blackbox methods. We show three case studies from production that span a large-scale distributed storage system, a datacenter-wide network, and an end-to-end video serving stack at Yahoo. We end by listing a number of open directions for performance diagnosis in cloud and content providers.
There has been significant research on network diagnosis methods. capture TCP variables at the CDN to localize performance bottlenecks @cite_39 ; they require OS kernel changes in the critical path, since they require TCP instrumentation outside of the tcp structure. WhyHigh @cite_25 and LatLong @cite_35 further discover client clusters with performance problems and diagnose user-to-CDN path problems at an aggregate level (instead of per-session). @cite_15 and @cite_33 diagnose datacenter network performance using detailed instrumentation (e.g., socket logs and packet traces). At large serving rates, such logging may be infeasible. Network tomography techniques @cite_16 localize bad performance to network interfaces; they assume that the path between two hosts is known -- uncommon in datacenter networks. MonitorRank @cite_19 uses similar tomography-based localization.
@cite_35 @cite_33 @cite_39 @cite_19 @cite_15 @cite_16 @cite_25
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Minimizing user-perceived latency is crucial for Content Distribution Networks (CDNs) hosting interactive services. Latency may increase for many reasons, such as interdomain routing changes and the CDN's own load-balancing policies. CDNs need greater visibility into the causes of latency increases, so they can adapt by directing traffic to different servers or paths. In this paper, we propose a tool for CDNs to diagnose large latency increases, based on passive measurements of performance, traffic, and routing. Separating the many causes from the effects is challenging. We propose a decision tree for classifying latency changes, and determine how to distinguish traffic shifts from increases in latency for existing servers, routers, and paths. Another challenge is that network operators group related clients to reduce measurement and control overhead, but the clients in a region may use multiple servers and paths during a measurement interval. We propose metrics that quantify the latency contributions across sets of servers and routers. Based on the design, we implement the LatLong tool for diagnosing large latency increases for CDN. We use LatLong to analyze a month of data from Google's CDN, and find that nearly 1 of the daily latency changes increase delay by more than 100 msec. Note that the latency increase of 100 msec is significant, since these are daily averages over groups of clients, and we only focus on latency-sensitive traffic for our study. More than 40 of these increases coincide with interdomain routing changes, and more than one-third involve a shift in traffic to different servers. This is the first work to diagnose latency problems in a large, operational CDN from purely passive measurements. Through case studies of individual events, we identify research challenges for managing wide-area latency for CDNs. In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text. Replicating content across a geographically distributed set of servers and redirecting clients to the closest server in terms of latency has emerged as a common paradigm for improving client performance. In this paper, we analyze latencies measured from servers in Google's content distribution network (CDN) to clients all across the Internet to study the effectiveness of latency-based server selection. Our main result is that redirecting every client to the server with least latency does not suffice to optimize client latencies. First, even though most clients are served by a geographically nearby CDN node, a sizeable fraction of experience latencies several tens of milliseconds higher than other in the same region. Second, we find that queueing delays often override the benefits of a client interacting with a nearby server. To help the administrators of Google's CDN cope with these problems, we have built a system called WhyHigh. First, WhyHigh measures client latencies across all nodes in the CDN and correlates measurements to identify the prefixes affected by inflated latencies. Second, since clients in several thousand prefixes have poor latencies, WhyHigh prioritizes problems based on the impact that solving them would have, e.g., by identifying either an AS path common to several inflated prefixes or a CDN node where path inflation is widespread. Finally, WhyHigh diagnoses the causes for inflated latencies using active measurements such as traceroutes and pings, in combination with datasets such as BGP paths and flow records. Typical causes discovered include lack of peering, routing misconfigurations, and side-effects of traffic engineering. We have used WhyHigh to diagnose several instances of inflated latencies, and our efforts over the course of a year have significantly helped improve the performance offered to clients by Google's CDN. Purpose – The purpose of this paper is to suggest an alternative to the widely used Times Cited criterion for analysing citation networks. The approach involves taking account of the natures of the papers that cite a given paper, so as to differentiate between papers that attract the same number of citations.Design methodology approach – ArticleRank is an algorithm that has been derived from Google's PageRank algorithm to measure the influence of journal articles. ArticleRank is applied to two datasets – a citation network based on an early paper on webometrics, and a self‐citation network based on the 19 most cited papers in the Journal of Documentation – using citation data taken from the Web of Knowledge database.Findings – ArticleRank values provide a different ranking of a set of papers from that provided by the corresponding Times Cited values, and overcomes the inability of the latter to differentiate between papers with the same numbers of citations. The difference in rankings between Times Cited ... Content distribution networks (CDNs) need to make decisions, such as server selection and routing, to improve performance for their clients. The performance may be limited by various factors such as packet loss in the network, a small receive buffer at the client, or constrained server CPU and disk resources. Conventional measurement techniques are not effective for distinguishing these performance problems: application-layer logs are too coarse-grained, while network-level traces are too expensive to collect all the time. We argue that passively monitoring the transport-level statistics in the server's network stack is a better approach. This paper presents a tool for monitoring and analyzing TCP statistics, and an analysis of a CoralCDN node in PlanetLab for six weeks. Our analysis shows that more than 10 of connections are server-limited at least 40 of the time, and many connections are limited by the congestion window despite no packet loss. Still, we see that clients in 377 Autonomous Systems (ASes) experience persistent packet loss. By separating network congestion from other performance problems, our analysis provides a much more accurate view of the performance of the network paths than what is possible with server logs alone. A new form of document coupling called co-citation is defined as the frequency with which two documents are cited together. The co-citation frequency of two scientific papers can be determined by comparing lists of citing documents in the Science Citation Index and counting identical entries. Networks of co-cited papers can be generated for specific scientific specialties, and an example is drawn from the literature of particle physics. Co-citation patterns are found to differ significantly from bibliographic coupling patterns, but to agree generally with patterns of direct citation. Clusters of co-cited papers provide a new way to study the specialty structure of science. They may provide a new approach to indexing and to the creation of SDI profiles. Citation relationship between scientific publications has been successfully used for scholarly bibliometrics, information retrieval and data mining tasks, and citation-based recommendation algorithms are well documented. While previous studies investigated citation relations from various viewpoints, most of them share the same assumption that, if paper1 cites paper2 (or author1 cites author2), they are connected, regardless of citation importance, sentiment, reason, topic, or motivation. However, this assumption is oversimplified. In this study, we employ an innovative "context-rich heterogeneous network" approach, which paves a new way for citation recommendation task. In the network, we characterize 1) the importance of citation relationships between citing and cited papers, and 2) the topical citation motivation. Unlike earlier studies, the citation information, in this paper, is characterized by citation textual contexts extracted from the full-text citing paper. We also propose algorithm to cope with the situation when large portion of full-text missing information exists in the bibliographic repository. Evaluation results show that, context-rich heterogeneous network can significantly enhance the citation recommendation performance. In recent years scholars have built maps of science by connecting the academic fields that cite each other, are cited together, or that cite a similar literature. But since scholars cannot always publish in the fields they cite, or that cite them, these science maps are only rough proxies for the potential of a scholar, organization, or country, to enter a new academic field. Here we use a large dataset of scholarly publications disambiguated at the individual level to create a map of science-or research space-where links connect pairs of fields based on the probability that an individual has published in both of them. We find that the research space is a significantly more accurate predictor of the fields that individuals and organizations will enter in the future than citation based science maps. At the country level, however, the research space and citations based science maps are equally accurate. These findings show that data on career trajectories-the set of fields that individuals have previously published in-provide more accurate predictors of future research output for more focalized units-such as individuals or organizations-than citation based science maps. Abstract Documents in computer-readable form can be used to provide information about other documents, i.e. those they cite. To do this efficiently requires procedures for computer recognition of citing statements. This is not easy, especially for multi-sentence citing statements. Computer recognition procedures have been developed which are accurate to the following extent: 73 of the words in statements selected by computer procedures as being citing statements are words which are correctly attributable to the corresponding documents. The retrieval effectiveness of computer-recognized citing statements was tested in the following way. First, for eight retrieval requests in inorganiic chemistry, average recall by search of Chemical Abstracts Service indexing and Chemical Abstracts abstract text words was found to be 50 . Words from citing statements referring to the papers to be retrieved were then added to the index terms and abstract words as additional access points, and searching was repeated. Average recall increased to 70 . Only words from citing statements published within a year of the cited papers were used. The retrieval effect of citing statement words alone (published within a year) without index or abstract terms was the following: average recall was 40 . When just the words of the titles of the cited papers were added to those citing statement words, average recall increased to 50 . A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. Scholars are increasingly citing electronic “web references” which are not preserved in libraries or full text archives. WebCite is a new standard for citing web references. To “webcite” a document involves archiving the cited Web page through www.webcitation.org and citing the WebCite permalink instead of (or in addition to) the unstable live Web page. This journal has amended its “instructions for authors” accordingly, asking authors to archive cited Web pages before submitting a manuscript. Almost 200 other journals are already using the system. We discuss the rationale for WebCite, its technology, and how scholars, editors, and publishers can benefit from the service. Citing scholars initiate an archiving process of all cited Web references, ideally before they submit a manuscript. Authors of online documents and websites which are expected to be cited by others can ensure that their work is permanently available by creating an archived copy using WebCite and providing the citation information including the WebCite link on their Web document(s). Editors should ask their authors to cache all cited Web addresses (Uniform Resource Locators, or URLs) “prospectively” before submitting their manuscripts to their journal. Editors and publishers should also instruct their copyeditors to cache cited Web material if the author has not done so already. Finally, WebCite can process publisher submitted “citing articles” (submitted for example as eXtensible Markup Language [XML] documents) to automatically archive all cited Web pages shortly before or on publication. Finally, WebCite can act as a focussed crawler, caching retrospectively references of already published articles. Copyright issues are addressed by honouring respective Internet standards (robot exclusion files, no-cache and no-archive tags). Long-term preservation is ensured by agreements with libraries and digital preservation organizations. The resulting WebCite Index may also have applications for research assessment exercises, being able to measure the impact of Web services and published Web documents through access and Web citation metrics. @PARASPLIT [J Med Internet Res 2005;7(5):e60] Over the last several years, datacenter computing has become a pervasive part of the computing landscape. In spite of the success of the datacenter computing paradigm, there are significant challenges remaining to be solved--particularly in the area of networking. The success of TCP IP in the Internet makes TCP IP a natural candidate for datacenter network communication. A growing body of research and operational experience, however, has found that TCP often performs poorly in datacenter settings. TCP's poor performance has led some groups to abandon TCP entirely in the datacenter. This is not desirable, however, as it requires reconstruction of a new transport protocol as well as rewriting applications to use the new protocol. Over the last few years, promising research has focused on adapting TCP to operate in the datacenter environment. We have been running large datacenter computations for several years, and have experienced the promises and the pitfalls that datacenter computation presents. In this paper, we discuss our experiences with network communication performance within our datacenter, and discuss how we have leveraged and extended recent research to significantly improve network performance within our datacenter. With the tremendous amount of research publications, recommending relevant papers to researchers to fulfill their information need becomes a significant problem. The major challenge to be tackled by our work is that given a target paper, how to effectively recommend a set of relevant papers from an existing citation network. In this paper, we propose a novel method to address the problem by incorporating various citation relations for a proper set of papers, which are more relevant but with a very limited size. The proposed method has two unique properties. Firstly, a metric called Local Relation Strength is defined to measure the dependency between cited and citing papers. Secondly, a model called Global Relation Strength is proposed to capture the relevance between two papers in the whole citation graph. We evaluate our proposed model on a real-world publication dataset and conduct an extensive comparison with the state-of-the-art baseline methods. The experimental results demonstrate that our method can have a promising improvement over the state-of-the-art techniques. Historically, papers have been physically bound to the journal in which they were published; but in the digital age papers are available individually, no longer tied to their respective journals. Hence, papers now can be read and cited based on their own merits, independently of the journal's physical availability, reputation, or impact factor (IF). We compare the strength of the relationship between journals' IFs and the actual citations received by their respective papers from 1902 to 2009. Throughout most of the 20th century, papers' citation rates were increasingly linked to their respective journals' IFs. However, since 1990, the advent of the digital age, the relation between IFs and paper citations has been weakening. This began first in physics, a field that was quick to make the transition into the electronic domain. Furthermore, since 1990 the overall proportion of highly cited papers coming from highly cited journals has been decreasing and, of these highly cited papers, the proportion not coming from highly cited journals has been increasing. Should this pattern continue, it might bring an end to the use of the IF as a way to evaluate the quality of journals, papers, and researchers. © 2012 Wiley Periodicals, Inc. Logarithmic spirals are conjectured to be optimal escape paths from a half plane ocean. Assuming this, we find the rate of increase for both min-max and min-mean interpretations of optimal''. For the one-dimensional analog, which we call logarithmic coils, our min-mean solution differs from a widely-cited published account. StackMap leverages the best aspects of kernel-bypass networking into a new low-latency Linux network service based on the full-featured TCP kernel implementation, by dedicating network interfaces to applications and offering an extended version of the netmap API as a zero-copy, low-overhead data path while retaining the socket API for the control path. For small-message, transactional workloads, StackMap outperforms baseline Linux by 4 to 80 in latency and 4 to 391 in throughput. It also achieves comparable performance with Seastar, a highly-optimized user-level TCP IP stack for DPDK. An overwhelming number of news articles are available every day via the internet. Unfortunately, it is impossible for us to peruse more than a handful; furthermore it is difficult to ascertain an article’s social context, i.e., is it popular, what sorts of people are reading it, etc. In this paper, we develop a system to address this problem in the restricted domain of political news by harnessing implicit and explicit contextual information from the blogosphere. Specifically, we track thousands of blogs and the news articles they cite, collapsing news articles that have highly overlapping content. We then tag each article with the number of blogs citing it, the political orientation of those blogs, and the level of emotional charge expressed in the blog posts that link to the news article. We summarize and present the results to the user via a novel visualization which displays this contextual information; the user can then find the most popular articles, the articles most cited by liberals, the articles most emotionally discussed in the political blogosphere, etc. Remote access to NVMe Flash enables flexible scaling and high utilization of Flash capacity and IOPS within a datacenter. However, existing systems for remote Flash access either introduce significant performance overheads or fail to isolate the multiple remote clients sharing each Flash device. We present ReFlex, a software-based system for remote Flash access, that provides nearly identical performance to accessing local Flash. ReFlex uses a dataplane kernel to closely integrate networking and storage processing to achieve low latency and high throughput at low resource requirements. Specifically, ReFlex can serve up to 850K IOPS per core over TCP IP networking, while adding 21us over direct access to local Flash. ReFlex uses a QoS scheduler that can enforce tail latency and throughput service-level objectives (SLOs) for thousands of remote clients. We show that ReFlex allows applications to use remote Flash while maintaining their original performance with local Flash. Multipath TCP (MPTCP) is a major modification to TCP that enables a single transport connection to use multiple paths. Smartphones can benefit from MPTCP by using both WiFi and 3G 4G interfaces for their data-traffic, potentially improving the performance and allowing mobility through vertical handover. However, MPTCP requires a modification of the end hosts, thus suffers from the chicken-and-egg deployment problem. A global deployment of MPTCP is therefore expected to take years. To increase the incentives for clients and servers to upgrade their system, we propose MiMBox an efficient protocol converter that can translate MPTCP into TCP and vice versa to provide multipath benefits to early adopters of MPTCP. MiMBox is application agnostic and can be used transparently or explicitly. Moreover, a close attention was paid to the implementation's design to achieve good forwarding performance. MiMBox is implemented entirely in the Linux kernel so that it is able to more easily circumvent the bottlenecks of a user-space implementation. Measurements show that we always outperform user-space solutions and that the performance is close to plain IP packet forwarding. The soft real-time nature of large scale web applications in today's datacenters, combined with their distributed workflow, leads to deadlines being associated with the datacenter application traffic. A network flow is useful, and contributes to application throughput and operator revenue if, and only if, it completes within its deadline. Today's transport pro- tocols (TCP included), given their Internet origins, are agnostic to such flow deadlines. Instead, they strive to share network resources fairly. We show that this can hurt application performance. Motivated by these observations, and other (previously known) deficiencies of TCP in the datacenter environment, this paper presents the design and implementation of D3, a deadline-aware control protocol that is customized for the datacenter environment. D3 uses explicit rate control to apportion bandwidth according to flow deadlines. Evaluation from a 19-node, two-tier datacenter testbed shows that D3, even without any deadline information, easily outper- forms TCP in terms of short flow latency and burst tolerance. Further, by utilizing deadline information, D3 effectively doubles the peak load that the datacenter network cansupport.