A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing

Data Grids have been adopted as the platform for scientific communities that need to share, access, transport, process and manage large data collections distributed worldwide. They combine high-end computing technologies with high-performance networking and wide-area storage management techniques. In this paper, we discuss the key concepts behind Data Grids and compare them with other data sharing and distribution paradigms such as content delivery networks, peer-to-peer networks and distributed databases. We then provide comprehensive taxonomies that cover various aspects of architecture, data transportation, data replication and resource allocation and scheduling. Finally, we map the proposed taxonomy to various Data Grid systems not only to validate the taxonomy but also to identify areas for future exploration. Through this taxonomy, we aim to categorise existing systems to better understand their goals and their methodology. This would help evaluate their applicability for solving similar problems. This taxonomy also provides a "gap analysis" of this area through which researchers can potentially identify new issues for investigation. Finally, we hope that the proposed taxonomy and mapping also helps to provide an easy way for new practitioners to understand this complex area of research.Comment: 46 pages, 16 figures, Technical Repor

Systems for Challenged Network Environments.

Developing regions face significant challenges in network access, making even simple network tasks unpleasant and rich media prohibitively difficult to access. Even as cellular network coverage is approaching a near-universal reach, good network connectivity remains scarce and expensive in many emerging markets. The underlying theme in this dissertation is designing network systems that better accommodate users in emerging markets. To do so, this dissertation begins with a nuanced analysis of content access behavior for web users in developing regions. This analysis finds the personalization of content access---and the fragmentation that results from it---to be significant factors in undermining many existing web acceleration mechanisms. The dissertation explores content access behavior from logs collected at shared internet access sites, as well as user activity information obtained from a commercial social networking service with over a hundred million members worldwide. Based on these observations, the dissertation then discusses two systems designed for improving end-user experience in accessing and using content in constrained networks. First, it deals with the challenge of distributing private content in these networks. By leveraging the wide availability of cellular telephones, the dissertation describes a system for personal content distribution based on user access behavior. The system enables users to request future data accesses, and it schedules content transfers according to current and expected capacity. Second, the dissertation looks at routing bulk data in challenged networks, and describes an experimentation platform for building systems for challenged networks. This platform enables researchers to quickly prototype systems for challenged networks, and iteratively evaluate these systems using mobility and network emulation. The dissertation describes a few data routing systems that were built atop this experimentation platform. Finally, the dissertation discusses the marketplace and service discovery considerations that are important in making these systems viable for developing-region use. In particular, it presents an extensible, auction-based market platform that relies on widely available communication tools for conveniently discovering and trading digital services and goods in developing regions. Collectively, this dissertation brings together several projects that aim to understand and improve end-user experience in challenged networks endemic to developing regions.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91401/1/azarias_1.pd

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Generative AI-enabled Mobile Tactical Multimedia Networks: Distribution, Generation, and Perception

Mobile multimedia networks (MMNs) demonstrate great potential in delivering low-latency and high-quality entertainment and tactical applications, such as short-video sharing, online conferencing, and battlefield surveillance. For instance, in tactical surveillance of battlefields, scalability and sustainability are indispensable for maintaining large-scale military multimedia applications in MMNs. Therefore, many data-driven networking solutions are leveraged to optimize streaming strategies based on real-time traffic analysis and resource monitoring. In addition, generative AI (GAI) can not only increase the efficiency of existing data-driven solutions through data augmentation but also develop potential capabilities for MMNs, including AI-generated content (AIGC) and AI-aided perception. In this article, we propose the framework of GAI-enabled MMNs that leverage the capabilities of GAI in data and content synthesis to distribute high-quality and immersive interactive content in wireless networks. Specifically, we outline the framework of GAI-enabled MMNs and then introduce its three main features, including distribution, generation, and perception. Furthermore, we propose a second-score auction mechanism for allocating network resources by considering GAI model values and other metrics jointly. The experimental results show that the proposed auction mechanism can effectively increase social welfare by allocating resources and models with the highest user satisfaction

Inefficiencies in Digital Advertising Markets

Digital advertising markets are growing and attracting increased scrutiny. This article explores four market inefficiencies that remain poorly understood: ad effect measurement, frictions between and within advertising channel members, ad blocking, and ad fraud. Although these topics are not unique to digital advertising, each manifests in unique ways in markets for digital ads. The authors identify relevant findings in the academic literature, recent developments in practice, and promising topics for future research

Operating an Advertising Programmatic Buying Platform: A Case Study

This paper analyses how new technological developments and the possibilities generated by the internet are shaping the online advertising market. More specifically it focuses on a programmatic advertising case study. The origin of the problem is how publishers resort to automated buying and selling when trying to shift unsold inventory. To carry out our case study, we will use a programmatic online advertising sales platform, which identifies the optimal way of promoting a given product. The platform executes, evaluates, manages and optimizes display advertising campaigns, all in real-time. The empirical analysis carried out in the case study reveals that the platform and its exclusion algorithms are suitable mechanisms for analysing the performance and efficiency of the various segments that might be used to promote products. Thanks to Big Data tools and artificial intelligence the platform performs automatically, providing information in a user-friendly and simple manner

Web page performance analysis

Computer systems play an increasingly crucial and ubiquitous role in human endeavour by carrying out or facilitating tasks and providing information and services. How much work these systems can accomplish, within a certain amount of time, using a certain amount of resources, characterises the systems’ performance, which is a major concern when the systems are planned, designed, implemented, deployed, and evolve. As one of the most popular computer systems, the Web is inevitably scrutinised in terms of performance analysis that deals with its speed, capacity, resource utilisation, and availability. Performance analyses for the Web are normally done from the perspective of the Web servers and the underlying network (the Internet). This research, on the other hand, approaches Web performance analysis from the perspective of Web pages. The performance metric of interest here is response time. Response time is studied as an attribute of Web pages, instead of being considered purely a result of network and server conditions. A framework that consists of measurement, modelling, and monitoring (3Ms) of Web pages that revolves around response time is adopted to support the performance analysis activity. The measurement module enables Web page response time to be measured and is used to support the modelling module, which in turn provides references for the monitoring module. The monitoring module estimates response time. The three modules are used in the software development lifecycle to ensure that developed Web pages deliver at worst satisfactory response time (within a maximum acceptable time), or preferably much better response time, thereby maximising the efficiency of the pages. The framework proposes a systematic way to understand response time as it is related to specific characteristics of Web pages and explains how individual Web page response time can be examined and improved

Active caching for recommender systems

Web users are often overwhelmed by the amount of information available while carrying out browsing and searching tasks. Recommender systems substantially reduce the information overload by suggesting a list of similar documents that users might find interesting. However, generating these ranked lists requires an enormous amount of resources that often results in access latency. Caching frequently accessed data has been a useful technique for reducing stress on limited resources and improving response time. Traditional passive caching techniques, where the focus is on answering queries based on temporal locality or popularity, achieve a very limited performance gain. In this dissertation, we are proposing an ‘active caching’ technique for recommender systems as an extension of the caching model. In this approach estimation is used to generate an answer for queries whose results are not explicitly cached, where the estimation makes use of the partial order lists cached for related queries. By answering non-cached queries along with cached queries, the active caching system acts as a form of query processor and offers substantial improvement over traditional caching methodologies. Test results for several data sets and recommendation techniques show substantial improvement in the cache hit rate, byte hit rate and CPU costs, while achieving reasonable recall rates. To ameliorate the performance of proposed active caching solution, a shared neighbor similarity measure is introduced which improves the recall rates by eliminating the dependence on monotinicity in the partial order lists. Finally, a greedy balancing cache selection policy is also proposed to select most appropriate data objects for the cache that help to improve the cache hit rate and recall further