Ad-hoc Stream Adaptive Protocol

With the growing market of smart-phones, sophisticated applications that do extensive computation are common on mobile platform; and with consumers’ high expectation of technologies to stay connected on the go, academic researchers and industries have been making efforts to find ways to stream multimedia contents to mobile devices. However, the restricted wireless channel bandwidth, unstable nature of wireless channels, and unpredictable nature of mobility, has been the major road block for wireless streaming advance forward. In this paper, various recent studies on mobility and P2P system proposal are explained and analyzed, and propose a new design based on existing P2P systems, aimed to solve the wireless and mobility issues

DOH: A Content Delivery Peer-to-Peer Network

Many SMEs and non-pro¯t organizations su®er when their Web servers become unavailable due to °ash crowd e®ects when their web site becomes popular. One of the solutions to the °ash-crowd problem is to place the web site on a scalable CDN (Content Delivery Network) that replicates the content and distributes the load in order to improve its response time. In this paper, we present our approach to building a scalable Web Hosting environment as a CDN on top of a structured peer-to-peer system of collaborative web-servers integrated to share the load and to improve the overall system performance, scalability, availability and robustness. Unlike clusterbased solutions, it can run on heterogeneous hardware, over geographically dispersed areas. To validate and evaluate our approach, we have developed a system prototype called DOH (DKS Organized Hosting) that is a CDN implemented on top of the DKS (Distributed K-nary Search) structured P2P system with DHT (Distributed Hash table) functionality [9]. The prototype is implemented in Java, using the DKS middleware, the Jetty web-server, and a modi¯ed JavaFTP server. The proposed design of CDN has been evaluated by simulation and by evaluation experiments on the prototype

DCMS: A data analytics and management system for molecular simulation

Molecular Simulation (MS) is a powerful tool for studying physical/chemical features of large systems and has seen applications in many scientific and engineering domains. During the simulation process, the experiments generate a very large number of atoms and intend to observe their spatial and temporal relationships for scientific analysis. The sheer data volumes and their intensive interactions impose significant challenges for data accessing, managing, and analysis. To date, existing MS software systems fall short on storage and handling of MS data, mainly because of the missing of a platform to support applications that involve intensive data access and analytical process. In this paper, we present the database-centric molecular simulation (DCMS) system our team developed in the past few years. The main idea behind DCMS is to store MS data in a relational database management system (DBMS) to take advantage of the declarative query interface (i.e., SQL), data access methods, query processing, and optimization mechanisms of modern DBMSs. A unique challenge is to handle the analytical queries that are often compute-intensive. For that, we developed novel indexing and query processing strategies (including algorithms running on modern co-processors) as integrated components of the DBMS. As a result, researchers can upload and analyze their data using efficient functions implemented inside the DBMS. Index structures are generated to store analysis results that may be interesting to other users, so that the results are readily available without duplicating the analysis. We have developed a prototype of DCMS based on the PostgreSQL system and experiments using real MS data and workload show that DCMS significantly outperforms existing MS software systems. We also used it as a platform to test other data management issues such as security and compression

Energy-aware and adaptive fog storage mechanism with data replication ruled by spatio-temporal content popularity

Data traffic demand increases at a very fast pace in edge networking environments, with strict requisites on latency and throughput. To fulfil these requirements, among others, this paper proposes a fog storage system that incorporates mobile nodes as content providers. This fog storage system has a hybrid design because it does not only bring data closer to edge consumers but, as a novelty, it also incorporates in the system other relevant functional aspects. These novel aspects are the user data demand, the energy consumption, and the node distance. In this way, the decision whether to replicate data is based on an original edge service managed by an adaptive distance metric for node clustering. The adaptive distance is evaluated from several important system parameters like, distance from consumer to the data storage location, spatio-temporal data popularity, and the autonomy of each battery-powered node. Testbed results evidence that this flexible cluster-based proposal offers a more responsive data access to consumers, reduces core traffic, and depletes in a fair way the available battery energy of edge nodes.info:eu-repo/semantics/acceptedVersio

Edge Intelligence : Empowering Intelligence to the Edge of Network

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe

Edge Intelligence : Empowering Intelligence to the Edge of Network

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe