Application level optimization of big data transfers through pipelining, parallelism and concurrency
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Application level optimization of big data transfers through pipelining, parallelism and concurrency +91-9994232214,7806844441, ieeeprojectchennai@gmail.com, www.projectsieee.com, www.ieee-projects-chennai.com IEEE PROJECTS 2016-2017 ----------------------------------- Contact:+91-9994232214,+91-7806844441 Email: ieeeprojectchennai@gmail.com
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HEAD OFFICE (CHENNAI):
JP INFOTECH,
Old No. 31, New No: 86, 1st Floor, 1st Avenue,
Ashok Nagar (Ashok Pillar),
Chennai – 83.
Landmark: Next to Kotak Mahendra Bank / Bharath Scans
MOBILE: (0)9952649690
LANDLINE: (044) 43012642
E-MAIL: jpinfotechprojects@gmail.com
PUDUCHERRY / PONDICHERRY OFFICE:
JP INFOTECH,
#45, Kamaraj Salai, Thattanchavady,
Pondy-Tindivanam High Road,
Puducherry/Pondicherry – 605009.
Landmark: Opp. To Thattanchavady Industrial Estate and Next to VVP Nagar Arch.
MOBILE: (0)9952649690 / (0) 8608600246
LANDLINE: (0413)4300535
E-MAIL: jpinfotechprojects@gmail.com
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between independent organizations that are scatter geographically. A data grid is a kind of grid types that make relations computational
and storage resources. Data replication is an efficient way in data grid to obtain high performance and high availability by saving
numerous replicas in different locations e.g. grid sites. In this research, we propose a new architecture for dynamic Group data
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Advances in micro fabrication and communication techniques have led to unimaginable proliferation of
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potential of huge savings globally. Therefore, every energy efficient step is an endeavour to cut costs and
‘Go Green’. In this paper, we have proposed a framework to reduce communication workload through: Innetwork compression and multiple query synthesis at the base-station and modification of query syntax
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50120130406035
This document summarizes an article from the International Journal of Computer Engineering and Technology (IJCET) that proposes an algorithm called Replica Placement in Graph Topology Grid (RPGTG) to optimally place data replicas in a graph-based data grid while ensuring quality of service (QoS). The algorithm aims to minimize data access time, balance load among replica servers, and avoid unnecessary replications, while restricting QoS in terms of number of hops and deadline to complete requests. The article describes how the algorithm converts the graph structure of the data grid to a hierarchical structure to better manage replica servers and proposes services to facilitate dynamic replication, including a replica catalog to track replica locations and a replica manager to perform replication
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basic-wireline-operations-course-mahmoud-f-radwan.pdf
Application level optimization of big data transfers through pipelining, parallelism and concurrency
- 1. Application-Level Optimization of Big Data Transfers through Pipelining, Parallelism and Concurrency Abstract: In end-to-end data transfers, there are several factors affecting the data transfer throughput, such as the network characteristics (e.g., network bandwidth, round- trip-time, background traffic); end-system characteristics (e.g., NIC capacity, number of CPU cores and their clock rate, number of disk drives and their I/O rate); and the dataset characteristics (e.g., average file size, dataset size, file size distribution). Optimization of big data transfers over inter-cloud and intra- cloud networks is a challenging task that requires joint-consideration of all of these parameters. This optimization task becomes even more challenging when transferring datasets comprised of heterogeneous file sizes (i.e., large files and small files mixed). Previous work in this area only focuses on the end-system and network characteristics however does not provide models regarding the dataset characteristics. In this study, we analyze the effects of the three most important transfer parameters that are used to enhance data transfer throughput: pipelining,parallelism and concurrency. We provide models and guidelines to set the best values for these parameters and present two different transfer optimization algorithms that use the models developed. The tests conducted over high-speed networking and cloud testbeds show that our algorithms outperform the most popular data transfer tools like Globus Online and UDT in majority of the cases.