Ethical use of data in generative AI is a growing concern. Large generative AI models are trained on pervasive data sets from a variety of sources, where the data records are fused into the models and become inseparable from them. This raises questions regarding data ownership and the use of personal data, artwork, and copyrighted content in generative AI models. Should people and businesses be allowed to request the removal of their data from a model, even if the data were collected in public places? Should the use of data in generative AI vary across different places based on local copyright laws? How should local laws and regulations regarding data misuse and harmful content be enforced? Can people and organizations verify that their data records have been removed from models or are being used properly? In this paper we discuss the geospatial aspects of data ownership in large generative AI models. We present a vision of generative AI applications that are aware of data ownership and location provenance, based on spatio-temporal features of the data and the usage. These aspects of location-aware AI governance could mitigate some of the risks associated with generative AI and support ethical use of it, in both local and global applications.

Accessibility and inclusion in information systems provide guidelines and methods for building applications and devices that work for everyone, including for people with disabilities and other diverse characteristics. Systems that apply accessible and inclusive design (AID) provide an easy way to adapt features for people with different capabilities. In geospatial applications and wearable technology, aspects of location and time should be taken into account as well. In this paper we present a visionary approach of applying contextual filters to the input, output and the underlying dataset of geospatial applications, to examine adaptation of systems, devices and applications to different populations and for verification of accessibility and inclusion. The goal of AID is to help developers add accessibility features to their systems and provide transparency regarding lack of accessibility when needed, e.g., make it clear if there are features that prevent usage by people who lack certain abilities. We explain how to apply AID in physical and virtual worlds, like the metaverse, and we describe how ambient intelligence, accessibility maps and virtual worlds can assist in building accessible and inclusive geospatial applications. We present our vision and elaborate on related challenges and research directions.

The emergence of programmable switches has sparked a significant amount of work on new techniques to perform more powerful measurement tasks, for instance, to obtain fine-grained traffic and performance statistics. Previous work has focused on the efficiency of these measurements alone and has neglected flexibility, resulting in solutions that are hard to reuse or repurpose and that often overlap in functionality or goals. In this paper, we propose the use of a set of reusable primitive building blocks that can be composed to express measurement tasks in a concise and simple way. We describe the rationale for the design of our primitives, that we have named MAFIA (Measurements As FIrst-class Artifacts), and using several examples we illustrate how they can be combined to realize a comprehensive range of network measurement tasks. Writing MAFIA code does not require expert knowledge of low-level switch architecture details. Using a prototype implementation of MAFIA, we demonstrate the applicability of our approach and show that the use of our primitives results in compiled code that is comparable in size and resource usage with manually written specialized P4 code, and can be run in current hardware.

Abstract Numerous tools have been developed to aggressively block the execution of popular JavaScript programs in Web browsers. Such blocking also affects functionality of webpages and impairs user experience. As a consequence, many privacy preserving tools that have been developed to limit online tracking, often executed via JavaScript programs, may suffer from poor performance and limited uptake. A mechanism that can isolate JavaScript programs necessary for proper functioning of the website from tracking JavaScript programs would thus be useful. Through the use of a manually labelled dataset composed of 2,612 JavaScript programs, we show how current privacy preserving tools are ineffective in finding the right balance between blocking tracking JavaScript programs and allowing functional JavaScript code. To the best of our knowledge, this is the first study to assess the performance of current web privacy preserving tools in determining tracking vs. functional JavaScript programs. To improve this balance, we examine the two classes of JavaScript programs and hypothesize that tracking JavaScript programs share structural similarities that can be used to differentiate them from functional JavaScript programs. The rationale of our approach is that web developers often “borrow” and customize existing pieces of code in order to embed tracking (resp. functional) JavaScript programs into their webpages. We then propose one-class machine learning classifiers using syntactic and semantic features extracted from JavaScript programs. When trained only on samples of tracking JavaScript programs, our classifiers achieve accuracy of 99%, where the best of the privacy preserving tools achieve accuracy of 78%. The performance of our classifiers is comparable to that of traditional two-class SVM. One-class classification, where a training set of only tracking JavaScript programs is used for learning, has the advantage that it requires fewer labelled examples that can be obtained via manual inspection of public lists of well-known trackers. We further test our classifiers and several popular privacy preserving tools on a larger corpus of 4,084 websites with 135,656 JavaScript programs. The output of our best classifier on this data is between 20 to 64% different from the tools under study. We manually analyse a sample of the JavaScript programs for which our classifier is in disagreement with all other privacy preserving tools, and show that our approach is not only able to enhance user web experience by correctly classifying more functional JavaScript programs, but also discovers previously unknown tracking services.

Modern websites use multiple authentication cookies to allow visitors to the site different levels of access. The complexity of modern web applications can make it difficult for a web application programmer to ensure that the use of authentication cookies does not introduce vulnerabilities. Even when a programmer has access to all of the source code, this analysis can be challenging; the problem becomes even more vexing when web programmers cobble together off-the-shelf libraries to implement authentication. We have assembled a checklist for modern web programmers to verify that the cookie based authentication mechanism is securely implemented. Then, we developed a tool, Newton, to help a web application programmer to identify authentication cookies for specific parts of the website and to verify that they are securely implemented according to the checklist. We used Newton to analyze 149 sites, including the Alexa top-200 and many other popular sites across a range of categories including search, shopping, and finance. We found that 113 of them---including high-profile sites such as Yahoo, Amazon, and Fidelity---were vulnerable to hijacking attacks. Many websites have already acknowledged and fixed the vulnerabilities that we found using Newton and reported to them.

Poor visibility into the network hampers progress in a number of important research areas, from network troubleshooting to Internet topology and performance mapping. This persistent, well-known problem has served as motivation for numerous proposals to build or extend existing Internet measurement platforms by recruiting larger, more diverse vantage points. Capturing the edge of the network, however, remains an elusive goal. We argue that at its root the problem is one of incentives. Today's measurement platforms build on the assumption that the goals of experimenters and those hosting the platform are the same. As much of the Internet growth occurs in residential broadband networks, this assumption no longer holds. We present a measurement experimentation platform that reaches the network edge by explicitly aligning the objectives of the experimenters with those of the users hosting the platform. Dasu-our current prototype-is designed to support both network measurement experimentation and broadband characterization. Dasu has been publicly available since July 2010 and has been installed by over 100 000 users with a heterogeneous set of connections spreading across 2431 autonomous systems (ASs) and 166 countries. We discuss some of the challenges we faced building and using a platform for the Internet's edge, describe its design and implementation, and illustrate the unique perspective its current deployment brings to Internet measurement.

Characterizing the flow of Internet traffic is important in a wide range of contexts, from network engineering and application design to understanding the network impact of consumer demand and business relationships. Despite the growing interest, the nearly impossible task of collecting large-scale, Internet-wide traffic data has severely constrained the focus of traffic-related studies.

The large-scale collection and exploitation of personal information to drive targeted online advertisements has raised privacy concerns. As a step towards understanding these concerns, we study the relationship between how much information is collected and how valuable it is for advertising. We use HTTP traces consisting of millions of users to aid our study and also present the first comparative study between aggregators. We develop a simple model that captures the various parameters of today's advertising revenues, whose values are estimated via the traces. Our results show that per aggregator revenue is skewed (5% accounting for 90% of revenues), while the contribution of users to advertising revenue is much less skewed (20% accounting for 80% of revenue). Google is dominant in terms of revenue and reach (presence on 80% of publishers). We also show that if all 5% of the top users in terms of revenue were to install privacy protection, with no corresponding reaction from the publishers, then the revenue can drop by 30%.

Web search is an integral part of our daily lives. Recently, there has been a trend of personalization in Web search, where different users receive different results for the same search query. The increasing personalization is leading to concerns about Filter Bubble effects, where certain users are simply unable to access information that the search engines' algorithm decides is irrelevant. Despite these concerns, there has been little quantification of the extent of personalization in Web search today, or the user attributes that cause it.

We employ user activity data from three highly popular gifting applications on Facebook to study the evolution of user activity on applications through the most commonly-used growth mechanism, namely Application Requests. We find user activity graphs differ from friendship graphs in large part due to the inherent directionality of user activity, and node transience. Our results show that, unlike degree distributions in friendship graphs, activity graphs exhibit strong asymmetry in in- and out-degree distributions, and that out-degrees are not accurately described by currently known parametric distributions. As such, user activity graphs cannot be simulated through existing intent- and feature-driven algorithms that can model friendship graphs.

Monetizing personal information is a key economic driver of online industry. End-users are becoming more concerned about their privacy, as evidenced by increased media attention. This paper proposes a mechanism called 'transactional' privacy that can be applied to personal information of users. Users decide what personal information about themselves is released and put on sale while receiving compensation for it. Aggregators purchase access to exploit this information when serving ads to a user. Truthfulness and efficiency, attained through an unlimited supply auction, ensure that the interests of all parties in this transaction are aligned. We demonstrate the effectiveness of transactional privacy for web-browsing using a large mobile trace from a major European capital. We integrate transactional privacy in a privacy-preserving system that curbs leakage of information. These mechanisms combine to form a market of personal information that can be managed by a trusted third party.

The sharing of personal data has emerged as a popular activity over online social networking sites like Facebook. As a result, the issue of online social network privacy has received significant attention in both the research literature and the mainstream media. Our overarching goal is to improve defaults and provide better tools for managing privacy, but we are limited by the fact that the full extent of the privacy problem remains unknown; there is little quantification of the incidence of incorrect privacy settings or the difficulty users face when managing their privacy.

An understanding of Internet topology is central to answer various questions ranging from network resilience to peer selection or data center location. While much of prior work has examined AS-level connectivity, meaningful and relevant results from such an abstract view of Internet topology have been limited. For one, semantically, AS relationships capture business relationships and not physical connectivity. Additionally, many organizations often use multiple ASes, either to implement different routing policies, or as legacies from mergers and acquisitions. In this paper, we move beyond the traditional AS graph view of the Internet to define the problem of AS-to-organization mapping. We describe our initial steps at automating the capture of the rich semantics inherent in the AS-level ecosystem where routing and connectivity intersect with organizations. We discuss preliminary methods that identify multi-AS organizations from WHOIS data and illustrate the challenges posed by the quality of the available data and the complexity of real-world organizational relationships.

Anonymization of social networks before they are published or shared has become an important research question. Recent work on anonymizing social networks has looked at privacy preserving techniques for publishing a single instance of the network. However, social networks evolve and a single instance is inadequate for analyzing the evolution of the social network or for performing any longitudinal data analysis. We study the problem of repeatedly publishing social network data as the network evolves, while preserving privacy of users. Publishing multiple instances of the same network independently has privacy risks, since stitching the information together may allow an adversary to identify users in the networks.

Internet exchange points (IXPs) are an important ingredient of the Internet AS-level ecosystem - a logical fabric of the Internet made up of about 30,000 ASes and their mutual business relationships whose primary purpose is to control and manage the flow of traffic. Despite the IXPs' critical role in this fabric, little is known about them in terms of their peering matrices (i.e., who peers with whom at which IXP) and corresponding traffic matrices (i.e., how much traffic do the different ASes that peer at an IXP exchange with one another). In this paper, we report on an Internet-wide traceroute study that was specifically designed to shed light on the unknown IXP-specific peering matrices and involves targeted traceroutes from publicly available and geographically dispersed vantage points. Based on our method, we were able to discover and validate the existence of about 44K IXP-specific peering links - nearly 18K more links than were previously known. In the process, we also classified all known IXPs depending on the type of information required to detect them. Moreover, in view of the currently used inferred AS-level maps of the Internet that are known to miss a significant portion of the actual AS relationships of the peer-to-peer type, our study provides a new method for augmenting these maps with IXP-related peering links in a systematic and informed manner.

With over half a billion users, Online Social Networks (OSNs) are the major new applications on the Internet. Little information is available on the network impact of OSNs, although there is every expectation that the volume and diversity of traffic due to OSNs is set to explode. In this paper, we examine the specific role played by a key component of OSNs: the extremely popular and widespread set of third-party applications on some of the most popular OSNs. With over 81,000 third-party applications on Facebook alone, their impact is hard to predict and even harder to study.

Online Social Networks (OSNs) have already attracted more than half a billion users. However, our understanding of which OSN features attract and keep the attention of these users is poor. Studies thus far have relied on surveys or interviews of OSN users or focused on static properties, e. g., the friendship graph, gathered via sampled crawls. In this paper, we study how users actually interact with OSNs by extracting clickstreams from passively monitored network traffic. Our characterization of user interactions within the OSN for four different OSNs (Facebook, LinkedIn, Hi5, and StudiVZ) focuses on feature popularity, session characteristics, and the dynamics within OSN sessions. We find, for example, that users commonly spend more than half an hour interacting with the OSNs while the byte contributions per OSN session are relatively small.

Rule-based packet classification is a powerful method for identifying traffic anomalies, with network security as a key application area. While popular systems like Snort are used in many network locations, comprehensive deployment across Tier-1 service provider networks is costly due to the need for high-speed monitors at many network ingress points. Since ISPs already collect flow statistics ubiquitously, can we use it for detecting the same anomalies as the packet based rules in spite of aggregation and absence of payload information? We exploit correlations between packet and flow level information via a machine learning (ML) approach to associate packet level alarms with a feature vector derived from flow records on the same traffic. We describe a system architecture for network-wide flow- alarming and describe the steps required to establish a proof- of-concept. We evaluate prediction accuracy of candidate ML algorithms on actual packet traces. The duration of prediction effectiveness is an issue for ML approaches and more so in resource intensive network applications. Initial results show little impairment of performance over periods of one or two weeks.

For the last few years we have been studying the diffusion of private information for users as they visit various Web sites triggering data gathering aggregation by third parties. This paper reports on our longitudinal study consisting of multiple snapshots of our examination of such diffusion over four years. We examine the various technical ways by which third-party aggregators acquire data and the depth of user-related information acquired. We study techniques for protecting privacy diffusion as well as limitations of such techniques. We introduce the concept of secondary privacy damage. Our results show increasing aggregation of user-related data by a steadily decreasing number of entities. A handful of companies are able to track users' movement across almost all of the popular Web sites. Virtually all the protection techniques have significant limitations highlighting the seriousness of the problem and the need for alternate solutions.

The recent rise in popularity of social networks, such as Facebook and MySpace, has created large quantities of data about interactions within these networks. Such data contains many private details about individuals so anonymization is required prior to attempts to make the data more widely available for scientific research. Prior work has considered simple graph data to be anonymized by removing all non-graph information and adding or deleting some edges. Since social network data is richer in details about the users and their interactions, loss of details due to anonymization limits the possibility for analysis. We present a new set of techniques for anonymizing social network data based on grouping the entities into classes, and masking the mapping between entities and the nodes that represent them in the anonymized graph. Our techniques allow queries over the rich data to be evaluated with high accuracy while guaranteeing resilience to certain types of attack. To prevent inference of interactions, we rely on a critical "safety condition" when forming these classes. We demonstrate utility via empirical data from social networking settings. We give examples of complex queries that may be posed and show that they can be answered over the anonymized data efficiently and accurately.

Classification of data is an important step in the knowledge evolution of sciences. Traditionally, in sciences, classification of data was performed by human experts. Human knowledge can recognize unique functional properties that are necessary and sufficient to place complex structures and phenomena into a particular class or group.However, with the growth in scientific data and rapid changes in knowledge, it is no longer feasible for humans to classify objects. Automation of the classification process is necessary to cope with the growing amount of data. Otherwise, classification will become the rate-limiting step for scientific data analysis.In this paper, we address the needs of such automation in the SciAEther project and develop ChES, a fast and reproducible framework for classifying molecules in chemical data. Our framework captures human understanding through an ontology and the diversity in classification types through a rule based system to classify complex molecular compounds. We have tested our system with molecules from PubChem repository and found that our knowledge-based, automatic classification matches, and some-times surpasses, that of the human experts.

Various bits of information about users accessing Web sites. some of which are private, have been gathered since the inception of the Web. Increasingly the gathering, aggregation, and processing has been outsourced to third parties. The goal of this work is to examine the effectiveness of specific techniques to limit this diffusion of private information to third parties. We also examine the impact of these privacy protection techniques on the usability and quality of the Web pages returned. Using objective measures for privacy protection and page quality we examine their tradeoffs for different privacy protection techniques applied to a collection of popular Web sites as well as a focused set of sites with significant privacy concerns. We study privacy protection both at a browser and at a proxy.

As a follow up to characterizing traffic deemed as unwanted by Web clients such as advertisements, we examine how information related to individual users is aggregated as a result of browsing seemingly unrelated Web sites. We examine the privacy diffusion on the Internet, hidden transactions, and the potential for a few sites to be able to construct a profile of individual users. We define and generate a privacy footprint allowing us to assess and compare the diffusion of privacy information across a wide variety of sites. We examine the effectiveness of existing and new techniques to reduce this diffusion. Our results show that the size of the privacy footprint is a legitimate cause for concern across the sets of sites that we study.

Web pages include extraneous material that may be viewed as undesirable by a user. Increasingly many Web sites also require users to register to access either all or portions of the site. Such tension between content owners and users has resulted in a "cat and mouse" game between content provided and how users access it.We carried out a measurement-based study to understand the nature of extraneous content and its impact on performance as perceived by users. We characterize how this content is distributed and the effectiveness of blocking mechanisms to stop it as well as countermeasures taken by content owners to negate such mechanisms. We also examine sites that require some form of registration to control access and the attempts made to circumvent it.Results from our study show that extraneous content exists on a majority of popular pages and that a 25-30% reduction in downloaded objects and bytes with corresponding latency reduction can be attained by blocking such content. The top ten advertisement delivering companies delivered 40% of all URLs matched as ads in our study. Both the server name and the remainder of the URL are important in matching a URL as an ad. A majority of popular sites require some form of registration and for such sites users can obtain an account from a shared public database. We discuss future measures and countermeasures on the part of each side.

Web performance measurements and availability tests have been carried out using a variety of infrastructures over the last several years. Disruptions in the Internet can lead to Web sites being unavailable or increase user-perceived latency. The unavailability could be due to DNS, failures in segments of the physical network cutting off thousands of users, or attacks. Prompt reactions to network-wide events can be facilitated by local or remote measurement and monitoring. Better yet, a distributed set of intercommunicating measurement and monitoring entities that react to events dynamically could go a long way to handle disruptions.We have designed and built ATMEN, a triggered measurement infrastructure to communicate and coordinate across various administrative entities. ATMEN nodes can trigger new measurements, query ongoing passive measurements or historical measurements stored on remote nodes, and coordinate the responses to make local decisions. ATMEN reduces wasted measurements by judiciously reusing measurements along three axes: spatial, temporal, and application.We describe the use of ATMEN for key Web applications such as performance based ranking of popular Web sites and availability of DNS servers on which most Web transactions are dependent. The evaluation of ATMEN is done using multiple network monitoring entities called Gigascopes installed across the USA, measurement data of a popular network application involving millions of users distributed across the Internet, and scores of clients to aid in gathering measurement information upon demand. Our results show that such a system can be built in a scalable fashion.

For the last few years, large Web content providers interested in improving their scalability and availability have increasingly turned to three techniques: mirroring, content distribution, and ISP multihoming. The Domain Name System (DNS) has gained a prominent role in the way each of these techniques directs client requests to achieve the goals of scalability and availability. The DNS is thought to offer the transparent and agile control necessary to react quickly to ISP link failures or phenomenon such as flash crowds.

Traffic anomalies such as failures and attacks are commonplace in today's network, and identifying them rapidly and accurately is critical for large network operators. The detection typically treats the traffic as a collection of flows that need to be examined for significant changes in traffic pattern (eg, volume, number of connections). However, as link speeds and the number of flows increase, keeping per-flow state is either too expensive or too slow. We propose building compact summaries of the traffic data using the notion of sketches. We have designed a variant of the sketch data structure, k-ary sketch, which uses a constant, small amount of memory, and has constant per-record update and reconstruction cost. Its linearity property enables us to summarize traffic at various levels. We then implement a variety of time series forecast models (ARIMA, Holt-Winters, etc.) on top of such summaries and detect significant changes by looking for flows with large forecast errors. We also present heuristics for automatically configuring the model parameters.Using a large amount of real Internet traffic data from an operational tier-1 ISP, we demonstrate that our sketch-based change detection method is highly accurate, and can be implemented at low computation and memory costs. Our preliminary results are promising and hint at the possibility of using our method as a building block for network anomaly detection and traffic measurement.

With a key component of latency on the Web being connection set up between clients and Web servers, several ways to avoid connections have been explored. While the work in recent years on Content Distribution Networks (CDNs) have moved some content 'closer' to users at the cost of increasing DNS traffic, they have not fully exploited the available unused potential of existing protocols. We explore ways by which a variety of Web responses can be piggybacked on DNS messages. While we evaluated our idea in the Web context, the approach is generic and not restricted to Web responses. We propose an architecture for HTTP piggybacking in DNS messages and carry out a detailed performance analysis based on a trace-driven simulation study. Our architecture requires minimal extensions to existing protocols, utilizing only the allowed optional fields for these extensions. It is fully compatible and can coexist with the current Web.

In earlier work we proposed a way for a Web server to detect connectivity information about clients accessing it in order to take tailored actions for a client request. This paper describes the design, implementation, and evaluation of such a working system. A Web site has a strong incentive to reduce the `time-to-glass' to retain users who may otherwise lose interest and leave the site. We have performed a measurement study from multiple client sites around the world with various levels of connectivity to the Internet communicating with modified Apache Web servers under our control. The results show that clients can be classified in a correct and stable manner and that user-perceived latency can be reduced via tailored actions. Our measurements show that classification and determination of server actions are done without significant overhead on the Web server. We explore a variety of modified actions ranging from selecting a lower quality version of the resource to altering the manner of content delivery. By studying numerous performance related factors in a single unified framework and examining both individual actions as well as combination of actions, our modified Web server implementation shows the efficacy of various server actions.

No abstract available.

No abstract available.

The paper studies two types of events that often overload Web sites to a point when their services are degraded or disrupted entirely - flash events (FEs) and denial of service attacks (DoS). The former are created by legitimate requests and the latter contain malicious requests whose goal is to subvert the normal operation of the site. We study the properties of both types of events with a special attention to characteristics that distinguish the two. Identifying these characteristics allows a formulation of a strategy for Web sites to quickly discard malicious requests. We also show that some content distribution networks (CDNs) may not provide the desired level of protection to Web sites against flash events. We therefore propose an enhancement to CDNs that offers better protection and use trace-driven simulations to study the effect of our enhancement on CDNs and Web sites.

We categorize the set of clients communicating with a server on the Web based on information that can be determined by the server. The Web server uses the information to direct tailored actions. Users with poor connectivity may choose not to stay at a Web site if it takes a long time to receive a page, even if the Web server at the site is not the bottleneck. Retaining such clients may be of interest to a Web site. Better connected clients can receive enhanced representations of Web pages, such as with higher quality images.We explore a variety of considerations that could be used by a Web server in characterizing a client. Once a client is characterized as poor or rich, the server can deliver altered content, alter how content is delivered, alter policy and caching decisions, or decide when to redirect the client to a mirror site. We also use network-aware client clustering techniques to provide a coarser level of client categorization and use it to categorize subsequent clients from that cluster for which a client-specific categorization is not available.Our results for client characterization and applicable server actions are derived from real, recent, and diverse set of Web server logs. Our experiments demonstrate that a relatively simple characterization policy can classify poor clients such that these clients subsequently make the majority of badly performing requests to a Web server. This policy is also stable in terms of clients staying in the same class for a large portion of the analysis period. Client clustering can significantly help in initially classifying clients for which no previous information about the client is known. We also show that different server actions can be applied to a significant number of request sequences with poor performance.

Presents READY, a multi-threaded implementation of the CORBA Notification Service. The main contribution of our work is the design and development of scalable solutions for the implementation of the CORBA Notification Service. In particular, we present the overall architecture of READY, discuss the key design challenges and choices we made with respect to filter evaluation and event dispatching, and present the current implementation status. Finally, we present preliminary experimental results from our current implementation.

READY is an event notification service that provides efficient, decoupled, and asynchronous event notifications. READY supports: consumer specifications that match over single and compound event patterns; communication sessions that manage quality of service for event delivery; grouping constructs for sessions and specifications; and event zones and boundary routers that bound the scope of event distribution and control the mapping of events across zones.

Being able to identify the groups of clients that are responsible for a significant portion of a Web site's requests can be helpful to both the Web site and the clients. In a Web application, it is beneficial to move content closer to groups of clients that are responsible for large subsets of requests to an origin server. We introduce clusters---a grouping of clients that are close together topologically and likely to be under common administrative control. We identify clusters using a ``network-aware" method, based on information available from BGP routing table snapshots.

This work studies the interaction of Web proxy cache coherency and replacement policies using trace-driven simulations. We specifically examine the relative importance of each type of policy in affecting the overall costs, the potential of incorporating coherency issues in cache replacement and the inclusion of additional factors such as frequency of resource use in replacement and coherency policies. The results show that the cache replacement policy in use is the primary cost determinant for relatively small caches, while the cache coherency policy is the determinant for larger caches. Incorporating cache coherency issues in cache replacement policies yields little improvement in overall performance. The use of access frequency in cache replacement, along with temporal locality and size information, results in a simple and better performing policy than found in previously published work. Combining this new replacement policy with the best piggyback-based cache coherency policy results in a 4.5% decrease in costs and 89% reduction in staleness ratio when compared to policy combinations in current use. Preliminary work indicates that cache replacement and coherency policies continue to affect costs in the presence of HTTP protocol enhancements such as persistent connections.

Internet traffic has grown significantly with the popularity of the Web. Consequently user perceived latency in retrieving Web pages has increased. Caching and prefetching at the client side, aided by hints from the server, are attempts at solving this problem. We suggest techniques to group resources that are likely to be accessed together into volumes, which are used to generate hints tailored to individual applications, such as prefetching, cache replacement, and cache validation. We discuss theoretical aspects of optimal volume construction, and develop efficient heuristics. Tunable parameters allow our algorithms to predict as many accesses as possible while reducing false predictions and limiting the size of hints. We analyze a collection of large server logs, extracting access patterns to construct and evaluate volumes. We examine sampling techniques to process only portions of the server logs while constructing equally good volumes. We show that it is possible to predict requests at low cost with a high degree of precision.

Keyword-based searches on the World Wide Web are often of limited use, because they return too many uninteresting matches. We propose here a novel mechanism that permits the user to specify directory sets to restrict the space of documents searched, and, at the same time, increase the speed of the search. We view the Web as a single, huge hierarchy, reflected in the structure of the URLs. We refer to each subtree in this hierarchy as a directory, and group semantically related documents from multiple directories into a directory set. Starting from a collection of pre-defined directory sets, a user can dynamically generate new directory sets by using operators in a directory set algebra, and focus keywordbased searches to documents that belong to a (pre-defined or dynamically generated) directory set. We design algorithms for efficiently evaluating expressions in the directory set algebra, and describe a technique for tightly integrating directory sets into keyword-based search engines....

... This paper offers an end-to-end framework by collectively examining the Web components -- clients, proxies, servers, and the network. Our goal is to reduce user-perceived latency and the number of TCP connections, improve cache coherency and cache replacement, and enable prefetching of resources that are likely to be accessed in the near future. In our scheme, server response messages include piggybacked information customized to the requesting proxy. Our enhancement to the existing requestresponse protocol does not require per-proxy state at server or per-server state at the proxy, and can be implemented without changes to HTTP 1.1. The server groups related resources into volumes (based on access patterns and the file system's directory structure) and applies a proxy-generated filter (indicating the type of information of interest to the proxy) to tailor the piggyback information. We present efficient data structures for constructing server volumes and applying proxy filters, and a transparent way to perform volume maintenance and piggyback generation at a router along the path between the proxy and the server. We demonstrate the effectiveness of our end-toend approach by evaluating various volume construction and filtering techniques across a collection of large client and server logs.

Caching in the World Wide Web currently follows a naive model, which assumes that resources are referenced many times between changes. The model also provides no way to update a cache entry if a resource does change, except by transferring the resource's entire new value. Several previous papers have proposed updating cache entries by transferring only the differences, or "delta," between the cached entry and the current value.In this paper, we make use of dynamic traces of the full contents of HTTP messages to quantify the potential benefits of delta-encoded responses. We show that delta encoding can provide remarkable improvements in response size and response delay for an important subset of HTTP content types. We also show the added benefit of data compression, and that the combination of delta encoding and data compression yields the best results.We propose specific extensions to the HTTP protocol for delta encoding and data compression. These extensions are compatible with existing implementations and specifications, yet allow efficient use of a variety of encoding techniques.

Automated monitoring of events that occur in a computer network is essential for non-intrusive software process enactment.Relevant events include modification of product artifacts like source files and documents, and execution and termination of tool invocations.Monitoring these events requires capturing the context in which they occur and constraining the monitoring to relevant events only.We introduce the concept of event context, and present constructs for using it to constrain event matching.The constructs have been implemented as an extension to an existing event monitoring system called Yeast, which serves as the event monitoring component in Provence.Provence is an open process-centered environment that monitors the actual execution of software processes, abstracted as sequences of low-level events, and maps these sequences of events into transitions in the process model enactment.

Distributed networks of personal workstations are becoming the dominant computing environment for software development organizations. Many cooperative activities that are carried out in such environments are particularly well suited for automated support. Taking the point of view that such activities are modeled most naturally as the occurrence of events requiring actions to be performed, we developed a system called Yeast (Yet another Event Action Specification Tool). Yeast is a client server system in which distributed clients register event action specifications with a centralized server, which performs event detection and specification management. Each specification submitted by a client defines a pattern of events that is of interest to the client's application plus an action that is to be executed in response to an occurrence of the event pattern; the server triggers the action of a specification once it has detected an occurrence of the associated event pattern. Yeast provides a global space of events that is visible to and shared by all users. In particular, events generated by one user can trigger specifications registered by another user. Higher level applications are built as collections of Yeast specifications. We use Yeast on a daily basis for a variety of applications, from deadline notification to software process automation. The paper presents an in depth description of Yeast and an example application of Yeast, in which Yeast specifications are used to automate a software distribution process involving several interdependent software tools.>

Reuse in software has been discussed at length in the community with a variety of techniques and varying rates of success. We take a slightly longer range view and present an eight year experience report. The software discussed in this paper include some well known tools with a very large user base both inside and outside our company. The assumptions under which work was done here were: users could choose between our work and the free market, reused items will have dynamic life cycles, and a consistent and stable system architecture was vital. A lesson learnt was that a configuration and distribution process oriented to software product control and delivery provides, with some augmentation, an effective base for reuse support. Having a process-oriented software base constantly enhanced with customer feedback has promoted a high degree of reuse of our software.>

Summary form only. Software architecture is loosely defined as the packaging of functions and objects, together with their interface, and control to implement applications in a domain. An accepted model of software reuse consists of taking a library of reusable assets, instituting a reuse process that permits augmentation of the assets in a supportive environment and providing deliverables via a streamlined development process. Classification of reusable libraries aids in packaging of objects/functions. Given the proper consideration at an earlier (architectural) level it is possible to attain large scale reuse. Without well understood constraints there is little possibility of a high degree of reuse. Role separation and constraints provide the needed stability for reuse. One way to view architecture is as a collection of architectural styles and services. An architectural style helps in arranging the components. The services support the non-functional requirements of the architectural styles. Thus to increase reuse the key is to identify a set of styles and services and combine them appropriately. Besides code, other reusable entities include scripts, documents, design, experience, specification, and application system. Reusability of specification often leads to reusable software components. Architectural constraints may dictate what components may be reused and availability of reused entities may enhance the stability of the architecture.>

Abstract This paper describes software that allows the user to edit the output of several common software tools and to cause the changes to be written back to the input files. For example, it is possible to edit the output of a spelling checker and have the changes propagated back to the source files. This technique makes some corrections simpler and more direct. A trial implementation is embedded in Emacs.