Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Iman Sadooghi"'
Autor:
Yong Zhao, Ioan Raicu, Steven Timm, Tiago Pais Pitta de Lacerda Ruivo, Ketan Maheshwari, Jesus Hernandez Martin, Gabriele Garzoglio, Tonglin Li, Kevin Brandstatter, Iman Sadooghi
Publikováno v:
IEEE Transactions on Cloud Computing. 5:358-371
Commercial clouds bring a great opportunity to the scientific computing area. Scientific applications usually require significant resources, however not all scientists have access to sufficient high-end computing systems. Cloud computing has gained t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b44c9ca20437414829330d11c5ca8794
https://doi.org/10.1109/tcc.2015.2404821
https://doi.org/10.1109/tcc.2015.2404821
Publikováno v:
Journal of Parallel and Distributed Computing. 96:27-37
Although distributed key–value store is becoming increasingly popular in compensating the conventional distributed file systems, it is often criticized due to its costly full-size replication for high availability that causes high I/O overhead. Thi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0e1f6560230a962af2005f390d0be29b
https://doi.org/10.1016/j.jpdc.2016.04.015
https://doi.org/10.1016/j.jpdc.2016.04.015
Publikováno v:
Concurrency and Computation: Practice and Experience. 28:70-94
Data-driven programming models such as many-task computing MTC have been prevalent for running data-intensive scientific applications. MTC applies over-decomposition to enable distributed scheduling. To achieve extreme scalability, MTC proposes a ful
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::717f11ade00c369a285dadec2c7220c5
https://doi.org/10.1002/cpe.3617
https://doi.org/10.1002/cpe.3617
Publikováno v:
Concurrency and Computation: Practice and Experience. 28:44-69
This paper presents a convergence of distributed key-value storage systems in clouds and supercomputers. It specifically presents ZHT, a zero-hop distributed key-value store system, which has been tuned for the requirements of high-end computing syst
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::05c1d7826089f02c631d6b5f87c697df
https://doi.org/10.1002/cpe.3614
https://doi.org/10.1002/cpe.3614
Publikováno v:
eScience
Data Analytics has become very popular on large datasets in different organizations. It is inevitable to use distributed resources such as Clouds for Data Analytics and other types of data processing at larger scales. To effectively utilize all syste
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::67c65fc16b6878770f4c1a30536e8403
https://doi.org/10.1109/escience.2016.7870881
https://doi.org/10.1109/escience.2016.7870881
Autor:
Shiva Srivastava, Ioan Raicu, Iman Sadooghi, Dongfang Zhao, Tonglin Li, Dharmit Patel, Ke Wang
Publikováno v:
BDC
The advent of Big Data has brought many challenges and opportunities in distributed systems, which have only amplified with the rate of growth of data. There is a need to rethink the software stack for supporting data intensive computing and big data
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::96a7f368895c632b7c597aceba66b468
https://doi.org/10.1109/bdc.2015.42
https://doi.org/10.1109/bdc.2015.42
Publikováno v:
IEEE BigData
In the era of big data and cloud, distributed key-value stores are increasingly used as building blocks of large-scale applications. Comparing to traditional relational databases, key-value stores are particularly compelling due to their low latency
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c5b1d252704a521c5eca62b41fa64f9c
https://doi.org/10.1109/bigdata.2015.7363794
https://doi.org/10.1109/bigdata.2015.7363794
Publikováno v:
IEEE BigData
In this paper, we propose and implement a key-value store that supports MPI while allowing application access at any time without having to declaring in the same MPI communication world. This feature may significantly simplify the application design
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9bddd340c6934a02c4b92f545c4ae687
https://doi.org/10.1109/bigdata.2015.7364116
https://doi.org/10.1109/bigdata.2015.7364116
Autor:
Ke Wang, Dongfang Zhao, Xiaobing Zhou, Tonglin Li, Ioan Raicu, Jiabao Li, Iman Sadooghi, Chaoqi Ma
Publikováno v:
CLUSTER
The emerging applications in big data and social networks issue rapidly increasing demands on graph processing. Graph query operations that involve a large number of vertices and edges can be tremendously slow on traditional databases. The state-of-t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9dd1a9d53a225123bcdf8b913f396c85
https://doi.org/10.1109/cluster.2015.90
https://doi.org/10.1109/cluster.2015.90