As The integration of Physical space and cyberspace, the large-scaledata distributing to diversification terminal which is geographical distribution of mass has become a huge challenge. When the data size can't b...
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ISBN:
(纸本)9781889335513
As The integration of Physical space and cyberspace, the large-scaledata distributing to diversification terminal which is geographical distribution of mass has become a huge challenge. When the data size can't be processed by the technology for traditional scope, how to deal with the user quality of service and efficient use of system resources has become an important issue of concern, with the resources becoming limited. This paper presents a data-driven mechanism for large-scale data distribution which is consists of four core part of the data production, data collection and pre-processing, data analysis engine, data consumption, aims to excavate the valuable information to improve the efficiency of resource use and accurate fault location for the large-scale data distribution system. At the same time, this paper studies the resource scheduling optimization with analyzing data driven for the system behavior and Fault location with analyzing data-driven environment, which proves the effectiveness for the operation of the large-scale data distribution system optimization by the data-driven working.
The distribution of largedata sets from a centralized node to several destination sites is frequently required by many data-intensive networking applications;this distribution can be efficiently achieved by the means...
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The distribution of largedata sets from a centralized node to several destination sites is frequently required by many data-intensive networking applications;this distribution can be efficiently achieved by the means of multicasting. Multicasting has been typically considered for on-demand applications and services, e. g., video-on-demand, IPTV, etc., which usually require start of data transmission immediately. We consider that multicast sessions can be provisioned starting with flexible times and that the multicast client can specify a maximum allowed time by which all data needs to be delivered to destinations. This is true for e-Science and high-performance applications, in which datadistribution is not necessarily immediate. In this paper, we study the problem of provisioning dynamic multicast data-distribution requests (MDDRs) with flexible scheduling over optical WDM networks. We consider the practical case of fractional-capacity multicast sessions that require less than the entire wavelength capacity (nodes are equipped with multicast-capable opaque switches). We devise provisioning methods based on the multicast tree (or light-tree) distribution model. In our first approach (named Rand), we generate multiple randomized alternate trees on which we try to provision the multicast session and then assign wavelengths and schedule the session. In our second approach (named AllSlots), we dynamically generate light-trees depending on the network state. In our next approach (named Break), when provisioning an entire tree fails, we try to "break" the tree into time-independent subtrees. We also study the impact of allowing data to be buffered at intermediary nodes and then transmitted toward destinations (method named Buffer) and consider an approach that partitions the data sets. Finally, we study the impact of the switch architecture on our provisioning by restricting our approaches to full-wavelength MDDRs.
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