Zobrazeno 1 - 10
of 143
pro vyhledávání: '"Raicu, Ioan"'
Publikováno v:
In Future Generation Computer Systems October 2024 159:444-458
Autor:
Orhean, Alexandru Iulian, Giannakou, Anna, Ramakrishnan, Lavanya, Chard, Kyle, Glavic, Boris, Raicu, Ioan
Publikováno v:
In Journal of Parallel and Distributed Computing July 2024 189
HRDBMS is a novel distributed relational database that uses a hybrid model combining the best of traditional distributed relational databases and Big Data analytics platforms such as Hive. This allows HRDBMS to leverage years worth of research regard
Externí odkaz:
http://arxiv.org/abs/1901.08666
Loosely coupled programming is a powerful paradigm for rapidly creating higher-level applications from scientific programs on petascale systems, typically using scripting languages. This paradigm is a form of many-task computing (MTC) which focuses o
Externí odkaz:
http://arxiv.org/abs/0901.0134
Cloud Computing has become another buzzword after Web 2.0. However, there are dozens of different definitions for Cloud Computing and there seems to be no consensus on what a Cloud is. On the other hand, Cloud Computing is not a completely new concep
Externí odkaz:
http://arxiv.org/abs/0901.0131
With the advances in e-Sciences and the growing complexity of scientific analyses, more and more scientists and researchers are relying on workflow systems for process coordination, derivation automation, provenance tracking, and bookkeeping. While w
Externí odkaz:
http://arxiv.org/abs/0808.3545
The practical realization of managing and executing large scale scientific computations efficiently and reliably is quite challenging. Scientific computations often involve thousands or even millions of tasks operating on large quantities of data, su
Externí odkaz:
http://arxiv.org/abs/0808.3548
Data Diffusion: Dynamic Resource Provision and Data-Aware Scheduling for Data Intensive Applications
Data intensive applications often involve the analysis of large datasets that require large amounts of compute and storage resources. While dedicated compute and/or storage farms offer good task/data throughput, they suffer low resource utilization p
Externí odkaz:
http://arxiv.org/abs/0808.3535
Our work addresses the enabling of the execution of highly parallel computations composed of loosely coupled serial jobs with no modifications to the respective applications, on large-scale systems. This approach allows new-and potentially far larger
Externí odkaz:
http://arxiv.org/abs/0808.3536
Data-intensive applications often require exploratory analysis of large datasets. If analysis is performed on distributed resources, data locality can be crucial to high throughput and performance. We propose a "data diffusion" approach that acquires
Externí odkaz:
http://arxiv.org/abs/0808.3546