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Applied Sciences
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High-Performance and Parallel Computer Systems: Design and Algorithms
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High-Performance and Parallel Computer Systems: Design and Algorithms

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 November 2016) | Viewed by 4757

Special Issue Editor

Prof. Dr. Arun Somani

E-Mail Website
Guest Editor
Dept. of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA

Special Issue Information

Dear Colleagues,

The evolution of high-performance, distributed, and parallel computing systems has opened new avenues to addresses several large problems in science and commerce, which were previously ignored due to the lack of appropriate computing infrastructure. The developments in the system architecture, system design, storage management, I/O technology, and algorithms, to effectively make use of available computing power, have resulted in an unprecedented growth in solving large problems and the required intelligence to handle them. New paradigms to process the data and data management, and new developments in algorithms to manage computing in parallel and distributed manner has enabled efficient processing. Advanced topics like climate pattern modeling, bio-informatics, GIS, Infrastructure planning, financial policy planning, precision agriculture, surveillance and security applications, and new material design, etc., are now being addressed with a new breed of computing system technology and associated algorithms. Performance improvement, scalability of problem sizes, and energy efficiency in computing are important consideration in effective development and use of such technologies.

This Special Issue seeks papers to report advances in any aspect of these developments. The manuscripts should be unpublished and report significant advancement.

Prof. Dr. Arun Somani
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

 

Keywords

  • High-Performance Computer Systems
  • Distributed Algorithms, Parallel Algorithms
  • Computing Efficiency
  • Large-scale Data Processing
  • Big Data Management, Application
  • Scalable Algorithms
  • Energy Efficient Computing

Published Papers (1 paper)

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Research

499 KiB  
Article
An Adaptive Buffering Scheme for P2P Live and Time-Shifted Streaming
by Eunsam Kim, Taeyoung Kim and Choonhwa Lee
Appl. Sci. 2017, 7(2), 204; https://0-doi-org.brum.beds.ac.uk/10.3390/app7020204 - 18 Feb 2017
Cited by 4 | Viewed by 4433
Abstract
Recently, P2P streaming techniques have been a promising solution to a large-scale live streaming system because of their high scalability and low installation cost. In P2P live streaming systems, however, it is difficult to manage peers’ buffers effectively, because they can buffer only [...] Read more.
Recently, P2P streaming techniques have been a promising solution to a large-scale live streaming system because of their high scalability and low installation cost. In P2P live streaming systems, however, it is difficult to manage peers’ buffers effectively, because they can buffer only a limited amount of data around a live broadcasting time in the main memory and suffer from long playback lag due to the nature of P2P structures. In addition, the number of peers decreases rapidly as the playback position moves further from this time by performing time-shifted viewing. These situations widen the distribution of peers’ playback positions, thereby decreasing the degree of data duplication among peers. Moreover, it is hard to use each peer’s buffer as the caching area because the buffer area where the chunks that have already been played back are stored can be overwritten at any time by new chunks that will arrive soon. In this paper, we therefore propose a novel buffering scheme to significantly increase data duplication in buffering periods among peers in P2P live and time-shifted streaming systems. In our proposed scheme, the buffer ratio of each peer is adaptively adjusted according to its relative playback position in a group by increasing the ratio of the caching area in its buffer as its playback position moves earlier in time and increasing the ratio of the prefetching area as its playback position moves later. Through extensive simulations, we demonstrate that our proposed adaptive buffering scheme outperforms the conventional buffering technique considerably in terms of startup delay, average jitter ratio, and the ratio of necessary chunks in a buffermap. Full article
(This article belongs to the Special Issue High-Performance and Parallel Computer Systems: Design and Algorithms)
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