10,426 research outputs found
Exploiting Traffic Balancing and Multicast Efficiency in Distributed Video-on-Demand Architectures
Distributed Video-on-Demand (DVoD) systems are proposed as a
solution to the limited streaming capacity and null scalability of centralized
systems. In a previous work, we proposed a fully distributed large-scale VoD
architecture, called Double P-Tree, which has shown itself to be a good approach
to the design of flexible and scalable DVoD systems. In this paper, we
present relevant design aspects related to video mapping and traffic balancing in
order to improve Double P-Tree architecture performance. Our simulation results
demonstrate that these techniques yield a more efficient system and considerably
increase its streaming capacity. The results also show the crucial importance
of topology connectivity in improving multicasting performance in
DVoD systems. Finally, a comparison among several DVoD architectures was
performed using simulation, and the results show that the Double P-Tree architecture
incorporating mapping and load balancing policies outperforms similar
DVoD architectures.This work was supported by the MCyT-Spain under contract TIC 2001-2592 and partially supported by the Generalitat de Catalunya- Grup de Recerca Consolidat 2001SGR-00218
Analysis and implementation of the Large Scale Video-on-Demand System
Next Generation Network (NGN) provides multimedia services over broadband
based networks, which supports high definition TV (HDTV), and DVD quality
video-on-demand content. The video services are thus seen as merging mainly
three areas such as computing, communication, and broadcasting. It has numerous
advantages and more exploration for the large-scale deployment of
video-on-demand system is still needed. This is due to its economic and design
constraints. It's need significant initial investments for full service
provision. This paper presents different estimation for the different
topologies and it require efficient planning for a VOD system network. The
methodology investigates the network bandwidth requirements of a VOD system
based on centralized servers, and distributed local proxies. Network traffic
models are developed to evaluate the VOD system's operational bandwidth
requirements for these two network architectures. This paper present an
efficient estimation of the of the bandwidth requirement for the different
architectures.Comment: 9 pages, 8 figure
BIBS: A Lecture Webcasting System
The Berkeley Internet Broadcasting System (BIBS) is a lecture webcasting system developed and operated by the Berkeley Multimedia Research Center. The system offers live remote viewing and on-demand replay of course lectures using streaming audio and video over the Internet. During the Fall 2000 semester 14 classes were webcast, including several large lower division classes, with a total enrollment of over 4,000 students. Lectures were played over 15,000 times per month during the semester. The primary use of the webcasts is to study for examinations. Students report they watch BIBS lectures because they did not understand material presented in lecture, because they wanted to review what the instructor said about selected topics, because they missed a lecture, and/or because they had difficulty understanding the speaker (e.g., non-native English speakers). Analysis of various survey data suggests that more than 50% of the students enrolled in some large classes view lectures and that as many as 75% of the lectures are played by members of the Berkeley community. Faculty attitudes vary about the virtues of lecture webcasting. Some question the use of this technology while others believe it is a valuable aid to education. Further study is required to accurately assess the pedagogical impact that lecture webcasts have on student learning
Designing a VM-level vertical scalability service in current cloud platforms: A new hope for wearable computers
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Public clouds are becoming ripe for enterprise adoption. Many companies, including large enterprises, are increasingly relying on public clouds as a substitute for, or a supplement to, their own computing infrastructures. On the other hand, cloud storage service has attracted over 625 million users. However, apart from the storage service, other cloud services, such as the computing service, have not yet attracted the end users’ interest for economic and technical reasons. Cloud service providers offers horizontal scalability to make their services scalable and economical for enterprises while it is still not economical for the individual users to use their computing services due to the lack of vertical scalability. Moreover, current virtualization technologies and operating systems, specifically the guest operating systems installed on virtual machines, do not support the concept of vertical scalability. In addition, network remote access protocols are meant to administer remote machines but they are unable to run the non-administrative tasks such as playing heavy games and watching high quality videos remotely in a way that makes the users feel as if they are sitting locally on their personal machines. On the other hand, the industry is yet unable to make efficient wearable computers a reality due to the limited size of the wearable devices, where it is infeasible to place efficient processors and big enough hard disks. This paper aims to highlight the need for the vertical scalability service and design the appropriate cloud, virtualization layer, and operating system services to incorporate vertical scalability in current cloud platforms in a way that will make it economically and technically efficient for the end users to use cloud virtual machines as if they are using their personal laptops. Through these services, the cloud takes wearable computing to the next stage and makes wearable computers a reality
DRS: Dynamic Resource Scheduling for Real-Time Analytics over Fast Streams
In a data stream management system (DSMS), users register continuous queries,
and receive result updates as data arrive and expire. We focus on applications
with real-time constraints, in which the user must receive each result update
within a given period after the update occurs. To handle fast data, the DSMS is
commonly placed on top of a cloud infrastructure. Because stream properties
such as arrival rates can fluctuate unpredictably, cloud resources must be
dynamically provisioned and scheduled accordingly to ensure real-time response.
It is quite essential, for the existing systems or future developments, to
possess the ability of scheduling resources dynamically according to the
current workload, in order to avoid wasting resources, or failing in delivering
correct results on time. Motivated by this, we propose DRS, a novel dynamic
resource scheduler for cloud-based DSMSs. DRS overcomes three fundamental
challenges: (a) how to model the relationship between the provisioned resources
and query response time (b) where to best place resources; and (c) how to
measure system load with minimal overhead. In particular, DRS includes an
accurate performance model based on the theory of \emph{Jackson open queueing
networks} and is capable of handling \emph{arbitrary} operator topologies,
possibly with loops, splits and joins. Extensive experiments with real data
confirm that DRS achieves real-time response with close to optimal resource
consumption.Comment: This is the our latest version with certain modificatio
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