Cloud computing denotes the latest trend in application development for parallel computing on massive data volumes It relies on clouds of servers to handle tasks that used to be managed by an individual server With cloud computing, software vendors can provide business intelligence and data analytic services for Internet scale data sets Many open source projects, such as Hadoop, offer various software components that are essential for building a cloud infrastructure Current Hadoop (and many others) requires users to configure cloud infrastructures via programs and APIs and such configuration is fixed during the runtime In this chapter, we propose a workload manager (WLM), called Cloud Weaver, which provides automated configuration of a cloud infrastructure for runtime execution The workload management is data-driven and can adapt to dynamic nature of operator throughput during different execution phases Cloud Weaver works for a single job and a workload consisting of multiple jobs running concurrently, which aims at maximum throughput using a minimum set of processors

Advances in Web and Network Technologies, and Information Management , Advances in Web and Network Technologies, and Information Management , دانشگاه تهران

An application delivery network (ADN) consists of a set of servers distributed over a large wide area network. In general, ADNs employ two approaches to improve performance in terms of response time: (1) use topological proximity on the network to redirect clients to a closest server, and (2) balance the load of the servers in the original source or the content delivery network using a load balancing algorithm. These two approaches aim to minimize the two major parameters, network latency and server latency, respectively. To accelerate delivery of static content, network latency is the major parameter to minimize. However, to accelerate delivery of dynamically generated content, both parameters are important and a solution requires to achieve a balance between these two approaches. In this paper, we illustrate the need for an integrated approach to this problem. We describe methods for improving the observed performance of an application delivery network by assigning the end user requests to servers. We describe an algorithm which computes such assignments efficiently, so that the assignment task can be performed and adjusted often as the environment changes. The experimental results show that the assignment computation is accurate and close to the optimal.

The large amount of bandwidth and other resources required to deliver streaming media limits the number of concurrent users. We propose a virtual active network (VAN) architecture for streaming media data delivery over wide area networks. In the proposed architecture, cooperating proxies support multiplexing and delivery of live streaming media. The hierarchical delivery structure is dynamically adjusted based on user population distribution, usage patterns, and network conditions. The proposed system architecture provides (1) reliable and high quality live streaming media delivery; (2) lower server resource requirements at the content provider sites; (3) reduced inter-ISP traffic; (4) application level routing for rapid deployment; and (5) cost-effective media data delivery. To deal with one characteristics of live broadcasting events, burst traffic at the beginning of the events, our system features a unique function that dynamically clusters multiple proxy servers to form a server farm to handle a large number of user login events when needed. Experimental results show that the proposed VAN architecture consistently delivers reliable live data streams using resources within 10 percent of the theoretically possible lower bound. The experimental results also show the effectiveness of our load balance algorithms to handle various user patterns, server capacity, and network congestion events.

With the increasing interest in multimedia, researchers across various disciplines, in particular, the database community and image processing community, have teamed up to conduct research in building multimedia database systems through integration of available technologies in various areas. In this paper, we introduce a hybrid approach to multimedia database systems in which traditional database systems are augmented by added media-dependent modules to manipulate and search multimedia data. SEMCOG (SEMantics and COGnition-based image retrieval) is a multimedia database system based on this hybrid architecture. We use it as an example to illustrate how to design and implement a hybrid multimedia database system. We also give our experiences with prototyping SEMCOG and issues raised for future research.

For most Web-based applications, contents are created dynamically based on the current state of a business, such as product prices and inventory, stored in database systems. These applications demand personalized content and track user behavior while maintaining application integrity. Many of such practices are not compatible with Web acceleration solutions. Consequently, although many web acceleration solutions have shown promising performance improvement and scalability, architecting and engineering distributed enterprise Web applications to utilize available content delivery networks remains a challenge. In this paper, we examine the challenge to accelerate J2EE-based enterprise web applications. We list obstacles and recommend some practices to transform typical database-driven J2EE applications to cache friendly Web applications where Web acceleration solutions can be applied. Furthermore, such transformation should be done without modification to the underlying application business logic and without sacrificing functions that are essential to e-commerce. We take the J2EE reference software, the Java PetStore, as a case study. By using the proposed guideline, we are able to cache more than 90% of the content in the PetStore and scale up the Web site more than 20 times.

Wide-area database replication technologies and the availability of content delivery networks allow Web applications to be hosted and served from powerful data centers. This form of application support requires a complete Web application suite to be distributed along with the database replicas. A major advantage of this approach is that dynamic content is served from locations closer to users, leading into reduced network latency and fast response times. However, this is achieved at the expense of overheads due to (a) invalidation of cached dynamic content in the edge caches and (b) synchronization of database replicas in the data center. These have adverse effects on the freshness of delivered content. In this paper, we propose a freshness-driven adaptive dynamic content caching, which monitors the system status and adjusts caching policies to provide content freshness guarantees. The proposed technique has been intensively evaluated to validate its effectiveness. The experimental results show that the freshness-driven adaptive dynamic content caching technique consistently provides good content freshness. Furthermore, even a Web site that enables dynamic content caching can further benefit from our solution, which improves content freshness up to 7 times, especially under heavy user request traffic and long network latency conditions. Our approach also provides better scalability and significantly reduced response times up to 70% in the experiments.

World Wide Web has emerged as a primary means for storing and structuring information. In this paper, we present a framework for mining implicit associations among Web documents. We focus on the following problem: “For a given set of seed URLs, find a list of Web pages which reflect the association among these seeds.” In the proposed framework, associations of two documents are induced by the connectivity and linking path length. Based on this framework, we have developed a random walk-based Web mining technique and validated it by experiments on real Web data. In this paper, we also discuss the extension of the algorithm for considering document contents.

In today's multitiered application architectures, clients do not access data stored in the databases directly. Instead, they use applications which in turn invoke the DBMS to generate the relevant content. Since executing application programs may require significant time and other resources, it is more advantageous to cache application results in a result cache. Various view materialization and update management techniques have been proposed to deal with updates to the underlying data. These techniques guarantee that the cached results are always consistent with the underlying data. Several applications, including e-commerce sites, on the other hand, do not require the caches be consistent all the time. Instead, they require that all outdated pages in the caches are invalidated in a timely fashion. In this paper, we show that invalidation is inherently different from view maintenance. We develop algorithms that benefit from this difference in reducing the cost of update management in certain applications and we present an invalidation framework that benefits from these algorithms.

Response time is one key point of di(cid:11)erentiation among electronic commerce (e-commerce) Web sites. For many e-commerce sites, Web pages are created dynamically based on the current state of a business stored in database systems. Snafu and slow-downs during special events or peak times demonstrate the challenges to engineer high performance database-driven e-commerce Web sites. One way to achieve this goal is through dynamic content caching. To ensure the freshness of dynamic content in the caches, it requires integration of caches, Web servers, application servers, and back-end database systems. CachePortal is a technology developed at NEC for accelerating database-driven e-commerce Web sites. The technology enables dynamic content caching by (1) deriving the relationships between cached pages and database contents via a snif-fer; and (2) intelligently monitoring database changes to "eject/delete" related pages from caches via an invalidator. Being evaluated and compared with many alternative solutions, CachePortal has shown that it can provide many e-commerce applications 3 to 25 times improvement for user response time.

Response time is one key point of di erentiation among electronic commerce (e-commerce) Web sites. Snafu and slow-downs at major Web sites during special events or peak times demonstrate the diAEculty of scaling up ecommerce sites. Such slow response times and down times can be devastating for e-commerce sites as indicated in a recent study by Zona Research[1] on the relationship between Web page download time and user abandonment rate. The study shows that only 2% of users will leave a Web site (i.e. abandonment rate) if the download time is less than 7 seconds. However, The abandonment rate goes up to 70% when the download time is around 12 seconds. This study clearly establishes the importance of fast response times to an e-commerce Web site to retain its customers.

Article Retrieving and organizing web pages by “information unit” Share on Authors: Wen-Syan Li C&C Research Laboratories, NE, Inc., 110 Rio Robles, M/S SJ100, San Jose, CA C&C Research Laboratories, NE, Inc., 110 Rio Robles, M/S SJ100, San Jose, CAView Profile , K. Selçuk Candan C&C Research Laboratories, NE, Inc., 110 Rio Robles, M/S SJ100, San Jose, CA C&C Research Laboratories, NE, Inc., 110 Rio Robles, M/S SJ100, San Jose, CAView Profile , Quoc Vu C&C Research Laboratories, NE, Inc., 110 Rio Robles, M/S SJ100, San Jose, CA C&C Research Laboratories, NE, Inc., 110 Rio Robles, M/S SJ100, San Jose, CAView Profile , Divyakant Agrawal C&C Research Laboratories, NE, Inc., 110 Rio Robles, M/S SJ100, San Jose, CA C&C Research Laboratories, NE, Inc., 110 Rio Robles, M/S SJ100, San Jose, CAView Profile Authors Info & Claims WWW '01: Proceedings of the 10th international conference on World Wide WebMay 2001 Pages 230–244https://doi.org/10.1145/371920.372057Online:01 April 2001Publication History 66citation1,478DownloadsMetricsTotal Citations66Total Downloads1,478Last 12 Months11Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

. A multimedia database query consists of a set of fuzzy and boolean (or crisp) predicates, constants, variables, and conjunction, disjunction, and negation operators. The fuzzy predicates are evaluated based on different media criteria, such as color, shape, layout, keyword. Since media-based evaluation yields similarity values, results to such a query is defined as an ordered set. Since many multimedia applications require partial matches, query results also include tuples which do not satisfy all predicates. Hence, any fuzzy semantics which extends the boolean semantics of conjunction in a straight forward manner may not be desirable for multimedia databases. In this paper, we focus on the problem of ‘ given a multimedia query which consists of multiple fuzzy and crisp predicates, how to provide the user with a meaningful overall ranking .’ More specifically, we study the problem of merging similarity values in queries with multiple fuzzy predicates. We describe the essential multimedia retrieval semantics, compare these with the known approaches, and propose a semantics which captures the retrieval requirements in multimedia databases.

Due to the fuzziness of query specification and media matching, multimedia retrieval is conducted by way of exploration. It is essential to provide feedback so that users can visualize query reformulation alternatives and database content distribution. Since media matching is an expensive task, another issue is how to efficiently support exploration so that the system is not overloaded by perpetual query reformulation. In this paper, we present a uniform framework to represent statistical information of both semantics and visual metadata for images in the databases. We propose the concept of query verification, which evaluates queries using statistics, and provides users with feedback, including the strictness and reformulation alternatives of each query condition as well as estimated numbers of matches. With query verification, the system increases the efficiency of the multimedia database exploration for both users and the system. Such statistical information is also utilized to support progressive query processing and query relaxation.

Site maps are essential to assist users in navigating a Web site. Most of the site maps are constructed manually and are static. However, di erent users may have di erent preferences and purposes for using a Web site. For example, a user may want to see a more detailed map while another user prefers a more abstract map. Two users looking for di erent topics at a large portal site would bene t more from two site maps with di erent focuses than a single map. In this paper, we present a technique for automatically constructing multi-granular and topic-focused site maps by utilizing directory paths, page contents, and link structures. In these site maps, the Web site topology is preserved and document importance, indicated by citation and semantic relevancy to user's topics of interest, is used for prioritizing the presentation of pages and directories. Experiments on real Web data have been conducted to validate the usefulness of the technique.

. Applications in a wide variety of industries require access to multiple heterogeneous distributed databases. One step in heterogeneous database integration is semantic integration: identifying corresponding attributes in different databases that represent the same real world concept. The rules of semantic integration can not be ‘pre-programmed’ since the information to be accessed is heterogeneous and attribute correspondences could be fuzzy. Manually comparing all possible pairs of attributes is an unreasonably large task. We have applied artificial neural networks (ANNs) to this problem. Metadata describing attributes is automatically extracted from a database to represent their ‘signatures’. The metadata is used to train neural networks to find similar patterns of metadata describing corresponding attributes from other databases. In our system, the rules to determine corresponding attributes are discovered through machine learning. This paper describes how we applied neural network techniques in a database integration problem and how we represent an attribute with its metadata as discriminators . This paper focuses on our experiments on effectiveness of neural networks and each discriminator. We also discuss difficulties of using neural networks for this problem and our wish list for the Machine Learning community.