1,505 research outputs found
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Optimizing Quality for Collaborative Video Viewing
The increasing popularity of distance learning and online courses has highlighted the lack of collaborative tools for student groups. In addition, the introduction of lecture videos into the online curriculum has drawn attention to the disparity in the network resources used by the students. We present an architecture and adaptation model called AI2TV (Adaptive Internet Interactive Team Video), a system that allows geographically dispersed participants, possibly some or all disadvantaged in network resources, to collaboratively view a video in synchrony. AI2TV upholds the invariant that each participant will view semantically equivalent content at all times. Video player actions, like play, pause and stop, can be initiated by any of the participants and the results of those actions are seen by all the members. These features allow group members to review a lecture video in tandem to facilitate the learning process. We employ an autonomic (feedback loop) controller that monitors clients' video status and adjusts the quality of the video according to the resources of each client. We show in experimental trials that our system can successfully synchronize video for distributed clients while, at the same time, optimizing the video quality given actual (fluctuating) bandwidth by adaptively adjusting the quality level for each participant
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Adaptive Synchronization of Semantically Compressed Instructional Videos for Collaborative Distance Learning
The increasing popularity of online courses has highlighted the need for collaborative learning tools for student groups. In addition, the introduction of lecture videos into the online curriculum has drawn attention to the disparity in the network resources available to students. We present an e-Learning architecture and adaptation model called AI2TV (Adaptive Interactive Internet Team Video), which allows groups of students to collaboratively view a video in synchrony. AI2TV upholds the invariant that each student will view semantically equivalent content at all times. A semantic compression model is developed to provide instructional videos at different level-of-details to accommodate dynamic network conditions and usersäó» system requirements. We take advantage of the semantic compression algorithmäó»s ability to provide different layers of semantically equivalent video by adapting the client to play at the appropriate layer that provides the client with the richest possible viewing experience. Video player actions, like play, pause and stop, can be initiated by any group member and and the results of those actions are synchronized with all the other students. These features allow students to review a lecture video in tandem, facilitating the learning process. Experimental trials show that AI2TV successfully synchronizes instructional videos for distributed students while concurrently optimizing the video quality, even under conditions of fluctuating bandwidth, by adaptively adjusting the quality level for each student while still maintaining the invariant
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Autonomic Control for Quality Collaborative Video Viewing
We present an autonomic controller for quality collaborative video viewing, which allows groups of geographically dispersed users with different network and computer resources to view a video in synchrony while optimizing the video quality experienced. The autonomic controller is used within a tool for enhancing distance learning with synchronous group review of online multimedia material. The autonomic controller monitors video state at the clients' end, and adapts the quality of the video according to the resources of each client in (soft) real time. Experimental results show that the autonomic controller successfully synchronizes video for small groups of distributed clients and, at the same time, enhances the video quality experienced by users, in conditions of fluctuating bandwidth and variable frame rate
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
Personal Autonomic Computing Reflex Reactions and Self-Healing
The overall goal of this research is to improve theself-awareness and environment-awareness aspect of personal au-tonomic computing (PAC) to facilitate self-managing capabilitiessuch as self-healing. Personal computing offers unique challengesfor self-management due to its multiequipment, multisituation, andmultiuser nature. The aim is to develop a support architecture formultiplatform working, based on autonomic computing conceptsand techniques. Of particular interest is collaboration among per-sonal systems to take a shared responsibility for self-awareness andenvironment awareness. Concepts mirroring human mechanisms,such as reflex reactions and the use ofvital signsto assess oper-ational health, are used in designing and implementing the PACarchitecture. As proof of concept, this was implemented as a self-healing tool utilizing a pulse monitor and a vital signs health moni-tor within the autonomic manager. This type of functionality opensnew opportunities to provide self-configuring, self-optimizing, andself-protecting, as well as self-healing autonomic capabilities topersonal computing
Conceptualizing a framework for cyber-physical systems of systems development and deployment
ABSTRACT
Cyber-physical systems (CPS) refer to the next generation of
embedded ICT systems that are interconnected, collaborative and that provide users and businesses with a wide range of smart applications and services. Software in CPS applications ranges from small systems to large systems, aka. Systems of Systems (SoS), such as smart grids and cities. CPSoS require managing massive amounts of data, being aware of their emerging behavior, and scaling out to progressively evolve and add new systems. Cloud
computing supports processing and storing massive amounts of
data, hosting and delivering services, and configuring selfprovisioned resources. Therefore, cloud computing is the natural candidate to solve CPSoS needs. However, the diversity of platforms and the low-level cloud programming models make difficult to find a common solution for the development and deployment of CPSoS. This paper presents the architectural foundations of a cloud-centric framework for automating the development and deployment of CPSoS service applications to converge towards a common open service platform for CPSoS applications. This framework relies on the well-known qualities of the microservices architecture style, the autonomic computing paradigm, and the model-driven software development approach. Its implementation and validation is on-going at two European and national projects
Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud
With the advent of cloud computing, organizations are nowadays able to react
rapidly to changing demands for computational resources. Not only individual
applications can be hosted on virtual cloud infrastructures, but also complete
business processes. This allows the realization of so-called elastic processes,
i.e., processes which are carried out using elastic cloud resources. Despite
the manifold benefits of elastic processes, there is still a lack of solutions
supporting them.
In this paper, we identify the state of the art of elastic Business Process
Management with a focus on infrastructural challenges. We conceptualize an
architecture for an elastic Business Process Management System and discuss
existing work on scheduling, resource allocation, monitoring, decentralized
coordination, and state management for elastic processes. Furthermore, we
present two representative elastic Business Process Management Systems which
are intended to counter these challenges. Based on our findings, we identify
open issues and outline possible research directions for the realization of
elastic processes and elastic Business Process Management.Comment: Please cite as: S. Schulte, C. Janiesch, S. Venugopal, I. Weber, and
P. Hoenisch (2015). Elastic Business Process Management: State of the Art and
Open Challenges for BPM in the Cloud. Future Generation Computer Systems,
Volume NN, Number N, NN-NN., http://dx.doi.org/10.1016/j.future.2014.09.00
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