Prefetching in Content Distribution Networks via Web Communities Identification and Outsourcing

Content distribution networks (CDNs) improve scalability and reliability, by replicating content to the “edge” of the Internet. Apart from the pure networking issues of the CDNs relevant to the establishment of the infrastructure, some very crucial data management issues must be resolved to exploit the full potential of CDNs to reduce the “last mile” latencies. A very important issue is the selection of the content to be prefetched to the CDN servers. All the approaches developed so far, assume the existence of adequate content popularity statistics to drive the prefetch decisions. Such information though, is not always available, or it is extremely volatile, turning such methods problematic. To address this issue, we develop self-adaptive techniques to select the outsourced content in a CDN infrastructure, which requires no apriori knowledge of request statistics. We identify clusters of “correlated” Web pages in a site, called Web site communities, and make these communities the basic outsourcing unit. Through a detailed simulation environment, using both real and synthetic data, we show that the proposed techniques are very robust and effective in reducing the user-perceived latency, performing very close to an unfeasible, off-line policy, which has full knowledge of the content popularity.     

Content delivery networks: status and trends

CDNs improve network performance and offer fast and reliable applications and services by distributing content to cache servers located close to users. The Web's growth has transformed communications and business services such that speed, accuracy, and availability of network-delivered content has become absolutely critical - both on their own terms and in terms of measuring Web performance. Proxy servers partially address the need for rapid content delivery by providing multiple clients with a shared cache location. In this context, if a requested object exists in a cache (and the cached version has not expired), clients get a cached copy, which typically reduces delivery time. CDNs act as trusted overlay networks that offer high-performance delivery of common Web objects, static data, and rich multimedia content by distributing content load among servers that are close to the clients. CDN benefits include reduced origin server load, reduced latency for end users, and increased throughput. CDNs can also improve Web scalability and disperse flash-crowd events. Here we offer an overview of the CDN architecture and popular CDN service providers.     

Community detection in Social Media

The proposed survey discusses the topic of community detection in the context of Social Media. Community detection constitutes a significant tool for the analysis of complex networks by enabling the study of mesoscopic structures that are often associated with organizational and functional characteristics of the underlying networks. Community detection has proven to be valuable in a series of domains, e.g. biology, social sciences, bibliometrics. However, despite the unprecedented scale, complexity and the dynamic nature of the networks derived from Social Media data, there has only been limited discussion of community detection in this context. More specifically, there is hardly any discussion on the performance characteristics of community detection methods as well as the exploitation of their results in the context of real-world web mining and information retrieval scenarios. To this end, this survey first frames the concept of community and the problem of community detection in the context of Social Media, and provides a compact classification of existing algorithms based on their methodological principles. The survey places special emphasis on the performance of existing methods in terms of computational complexity and memory requirements. It presents both a theoretical and an experimental comparative discussion of several popular methods. In addition, it discusses the possibility for incremental application of the methods and proposes five strategies for scaling community detection to real-world networks of huge scales. Finally, the survey deals with the interpretation and exploitation of community detection results in the context of intelligent web applications and services.     

Perforating necrobiosis lipoidica: 2 cases

INTRODUCTION: Perforating necrobiosis lipoidica is a rare clinical form of necrobiosis lipoidica and is almost always associated with diabetes. Necrotized tissue is usually eliminated via transfollicular perforations. CASE REPORT: We report two cases of perforating necrobiosis lipoidica associated with type II diabetes mellitus. The characteristic clinical feature was the presence of keratotic plugs around the periphery of the lesions. Transfollicular perforation predominated in the first case. The second also presented epidermal perforation. DISCUSSION: There have been only 6 cases of perforating necrobiosis lipoidica in the literature. To our knowledge, this is the first case report of transepidermal elimination. We confirmed the constant association between this perforation type and diabetes mellitus.     

Local community detection with hints

Local community detection is a widely used method for identifying groups of nodes starting from seeding nodes. The seed(s) are usually selected either randomly or based only on structural properties of the network. However, in many cases the choice of seed(s) incorporates external knowledge that attaches to these nodes an additional importance for their community. This knowledge, may be derived from an expert on the domain, or may arise from the network’s side information and it constitutes our motivation for the present work; this additional information about the importance of seed(s) can be exploited for detection of better and more relevant communities. We call such biased seed(s), hint(s). Our approach, is to reflect the importance of hints by changing appropriately the network in their vicinity. To the best of our knowledge, no such viewpoint of the seeding nodes in local community detection has been considered before. The aim of this study is to identify a single community which contains the hint(s). Our key contribution is the proposed Hint Enhancement Framework(HEF) that applies a two-step procedure to discover the community of the hint(s): 1) it changes the network by amplifying the hint(s) using re-weighting or re-wiring strategies so as to materialize the bias towards them and 2) it applies local community detection algorithms on the altered network of step 1. We experimentally evaluate HEF in synthetic and real datasets, and demonstrate the positive aspects of the framework in identifying better communities, in comparison with plain local community detection algorithms as well as a global one.

     

Proxy Cache Replacement Algorithms: A History-Based Approach

Accesing and circulation of Web objects has been facilitated by the design and implementation of effective caching schemes. Web caching has been integrated in prototype and commercial Web-based information systems in order to reduce the overall bandwidth and increase system's fault tolerance. This paper presents an overview of a series of Web cache replacement algorithms based on the idea of preserving a history record for cached Web objects. The number of references to Web objects over a certain time period is a critical parameter for the cache content replacement. The proposed algorithms are simulated and experimented under a real workload of Web cache traces provided by a major (Squid) proxy cache server installation. Cache and bytes hit rates are given with respect to different cache sizes and a varying number of request workload sets and it is shown that the proposed cache replacement algorithms improve both cache and byte hit rates.     

LDAP: framework, practices, and trends

Directory services facilitate access to information organized under a variety of frameworks and applications. The lightweight directory access protocol is a promising technology that provides access to directory information using a data structure similar to that of the X.500 protocol. IBM Tivoli, Novell, Sun, Oracle, Microsoft, and many other vendors feature LDAP-based implementations. The technology's increasing popularity is due both to its flexibility and its compatibility with existing applications.     

DeTRACT: a decentralized,transparent, immutable and open PKI certificate framework

International Journal of Information Security - Public key infrastructure (PKI) is widely used over the Internet to secure and to encrypt communication among parties. PKI involves digital...     

Cloud Computing: Distributed Internet Computing for IT and Scientific Research

Cloud computing is a disruptive technology with profound implications not only for Internet services but also for the IT sector as a whole. Its emergence promises to streamline the on-demand provisioning of software, hardware, and data as a service, achieving economies of scale in IT solutions' deployment and operation. This issue's articles tackle topics including architecture and management of cloud computing infrastructures, SaaS and IaaS applications, discovery of services and data in cloud computing infrastructures, and cross-platform interoperability. Still, several outstanding issues exist, particularly related to SLAs, security and privacy, and power efficiency. Other open issues include ownership, data transfer bottlenecks, performance unpredictability, reliability, and software licensing issues. Finally, hosted applications' business models must show a clear pathway to monetizing cloud computing. Several companies have already built Internet consumer services such as search, social networking, Web email, and online commerce that use cloud computing infrastructure. Above all, cloud computing's still unknown "killer application" will determine many of the challenges and the solutions we must develop to make this technology work in practice.