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You searched for +publisher:"Rutgers University" +contributor:("Rodero, Ivan"). Showing records 1 – 13 of 13 total matches.

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Rutgers University

1. Qin, Yubo. Exploring power-performance-quality tradeoffs for exascale combustion simulation.

Degree: MS, Electrical and Computer Engineering, 2016, Rutgers University

 The computational demand of high-performance computing (HPC) applications has brought major changes to the HPC system architecture. As a result, it is now possible to… (more)

Subjects/Keywords: Combustion; High performance computing

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APA (6th Edition):

Qin, Y. (2016). Exploring power-performance-quality tradeoffs for exascale combustion simulation. (Masters Thesis). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/50136/

Chicago Manual of Style (16th Edition):

Qin, Yubo. “Exploring power-performance-quality tradeoffs for exascale combustion simulation.” 2016. Masters Thesis, Rutgers University. Accessed October 24, 2019. https://rucore.libraries.rutgers.edu/rutgers-lib/50136/.

MLA Handbook (7th Edition):

Qin, Yubo. “Exploring power-performance-quality tradeoffs for exascale combustion simulation.” 2016. Web. 24 Oct 2019.

Vancouver:

Qin Y. Exploring power-performance-quality tradeoffs for exascale combustion simulation. [Internet] [Masters thesis]. Rutgers University; 2016. [cited 2019 Oct 24]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/50136/.

Council of Science Editors:

Qin Y. Exploring power-performance-quality tradeoffs for exascale combustion simulation. [Masters Thesis]. Rutgers University; 2016. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/50136/


Rutgers University

2. Kumar, Manonit. Characterizing the GlusterFS file system for software defined network research.

Degree: MS, Electrical and Computer Engineering, 2015, Rutgers University

 With the rapid rise of online resources such as those o ered by Google, Dropbox and Mi- crosoft cloud computing is playing a bigger part… (more)

Subjects/Keywords: Cloud computing; Electronic data processing – Distributed processing; Computer network architectures

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APA (6th Edition):

Kumar, M. (2015). Characterizing the GlusterFS file system for software defined network research. (Masters Thesis). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/46377/

Chicago Manual of Style (16th Edition):

Kumar, Manonit. “Characterizing the GlusterFS file system for software defined network research.” 2015. Masters Thesis, Rutgers University. Accessed October 24, 2019. https://rucore.libraries.rutgers.edu/rutgers-lib/46377/.

MLA Handbook (7th Edition):

Kumar, Manonit. “Characterizing the GlusterFS file system for software defined network research.” 2015. Web. 24 Oct 2019.

Vancouver:

Kumar M. Characterizing the GlusterFS file system for software defined network research. [Internet] [Masters thesis]. Rutgers University; 2015. [cited 2019 Oct 24]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/46377/.

Council of Science Editors:

Kumar M. Characterizing the GlusterFS file system for software defined network research. [Masters Thesis]. Rutgers University; 2015. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/46377/


Rutgers University

3. Pelaez, Alejandro, 1988-. Using data analytics for reliability and autonomic management of large-scale systems.

Degree: MS, Electrical and Computer Engineering, 2015, Rutgers University

 Large-scale clusters are growing at a rapid pace, and the resulting amount of monitoring data produced in these systems is also increasing. The goal of… (more)

Subjects/Keywords: Reliability; Machine learning; Data mining

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APA (6th Edition):

Pelaez, Alejandro, 1. (2015). Using data analytics for reliability and autonomic management of large-scale systems. (Masters Thesis). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/48622/

Chicago Manual of Style (16th Edition):

Pelaez, Alejandro, 1988-. “Using data analytics for reliability and autonomic management of large-scale systems.” 2015. Masters Thesis, Rutgers University. Accessed October 24, 2019. https://rucore.libraries.rutgers.edu/rutgers-lib/48622/.

MLA Handbook (7th Edition):

Pelaez, Alejandro, 1988-. “Using data analytics for reliability and autonomic management of large-scale systems.” 2015. Web. 24 Oct 2019.

Vancouver:

Pelaez, Alejandro 1. Using data analytics for reliability and autonomic management of large-scale systems. [Internet] [Masters thesis]. Rutgers University; 2015. [cited 2019 Oct 24]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/48622/.

Council of Science Editors:

Pelaez, Alejandro 1. Using data analytics for reliability and autonomic management of large-scale systems. [Masters Thesis]. Rutgers University; 2015. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/48622/


Rutgers University

4. Jin, Tong, 1986-. Autonomic data management for extreme scale coupled scientific workflows.

Degree: PhD, Electrical and Computer Engineering, 2016, Rutgers University

 Advanced coupled scientific simulation workflows running at extreme scales are providing new capabilities and new opportunities for high fidelity modeling and insights in a wide… (more)

Subjects/Keywords: High performance computing; Electronic data processing; Data flow computing

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APA (6th Edition):

Jin, Tong, 1. (2016). Autonomic data management for extreme scale coupled scientific workflows. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/49242/

Chicago Manual of Style (16th Edition):

Jin, Tong, 1986-. “Autonomic data management for extreme scale coupled scientific workflows.” 2016. Doctoral Dissertation, Rutgers University. Accessed October 24, 2019. https://rucore.libraries.rutgers.edu/rutgers-lib/49242/.

MLA Handbook (7th Edition):

Jin, Tong, 1986-. “Autonomic data management for extreme scale coupled scientific workflows.” 2016. Web. 24 Oct 2019.

Vancouver:

Jin, Tong 1. Autonomic data management for extreme scale coupled scientific workflows. [Internet] [Doctoral dissertation]. Rutgers University; 2016. [cited 2019 Oct 24]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/49242/.

Council of Science Editors:

Jin, Tong 1. Autonomic data management for extreme scale coupled scientific workflows. [Doctoral Dissertation]. Rutgers University; 2016. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/49242/


Rutgers University

5. Gadre, Hrishikesh. Investigating MapReduce framework extensions for efficient processing of geographically scattered datasets.

Degree: MS, Electrical and Computer Engineering, 2011, Rutgers University

 We observe two important trends brought about by the evolution of Internet in recent years. Firstly to improve end-to-end application performance in presence of bottlenecks… (more)

Subjects/Keywords: Information storage and retrieval systems; Data processing service centers

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APA (6th Edition):

Gadre, H. (2011). Investigating MapReduce framework extensions for efficient processing of geographically scattered datasets. (Masters Thesis). Rutgers University. Retrieved from http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000063404

Chicago Manual of Style (16th Edition):

Gadre, Hrishikesh. “Investigating MapReduce framework extensions for efficient processing of geographically scattered datasets.” 2011. Masters Thesis, Rutgers University. Accessed October 24, 2019. http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000063404.

MLA Handbook (7th Edition):

Gadre, Hrishikesh. “Investigating MapReduce framework extensions for efficient processing of geographically scattered datasets.” 2011. Web. 24 Oct 2019.

Vancouver:

Gadre H. Investigating MapReduce framework extensions for efficient processing of geographically scattered datasets. [Internet] [Masters thesis]. Rutgers University; 2011. [cited 2019 Oct 24]. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000063404.

Council of Science Editors:

Gadre H. Investigating MapReduce framework extensions for efficient processing of geographically scattered datasets. [Masters Thesis]. Rutgers University; 2011. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000063404


Rutgers University

6. Eun, Kyung Lee. Proactive thermal-aware management in cloud datacenters.

Degree: PhD, Electrical and Computer Engineering, 2015, Rutgers University

The complexity of modern datacenters is growing at an alarming rate due to the rising popularity of the cloud-computing paradigm as an effective means to… (more)

Subjects/Keywords: Cloud computing; Data processing service centers – Heating and ventilation; Data processing service centers – Cooling; Data processing service centers – Energy conservation

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APA (6th Edition):

Eun, K. L. (2015). Proactive thermal-aware management in cloud datacenters. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/46378/

Chicago Manual of Style (16th Edition):

Eun, Kyung Lee. “Proactive thermal-aware management in cloud datacenters.” 2015. Doctoral Dissertation, Rutgers University. Accessed October 24, 2019. https://rucore.libraries.rutgers.edu/rutgers-lib/46378/.

MLA Handbook (7th Edition):

Eun, Kyung Lee. “Proactive thermal-aware management in cloud datacenters.” 2015. Web. 24 Oct 2019.

Vancouver:

Eun KL. Proactive thermal-aware management in cloud datacenters. [Internet] [Doctoral dissertation]. Rutgers University; 2015. [cited 2019 Oct 24]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/46378/.

Council of Science Editors:

Eun KL. Proactive thermal-aware management in cloud datacenters. [Doctoral Dissertation]. Rutgers University; 2015. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/46378/


Rutgers University

7. Aktas, Mehmet Fatih, 1989-. Scheduling and flexible control of wide-area data transport services for end-to-end application workflows.

Degree: MS, Electrical and Computer Engineering, 2015, Rutgers University

 Emerging end-to-end scientific applications that integrate high-end experiments and instruments with large scale simulations and end-user displays, require complex couplings and data sharing between distributed… (more)

Subjects/Keywords: Software-defined networking (Computer network technology); Wide area networks (Computer networks)

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APA (6th Edition):

Aktas, Mehmet Fatih, 1. (2015). Scheduling and flexible control of wide-area data transport services for end-to-end application workflows. (Masters Thesis). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/47247/

Chicago Manual of Style (16th Edition):

Aktas, Mehmet Fatih, 1989-. “Scheduling and flexible control of wide-area data transport services for end-to-end application workflows.” 2015. Masters Thesis, Rutgers University. Accessed October 24, 2019. https://rucore.libraries.rutgers.edu/rutgers-lib/47247/.

MLA Handbook (7th Edition):

Aktas, Mehmet Fatih, 1989-. “Scheduling and flexible control of wide-area data transport services for end-to-end application workflows.” 2015. Web. 24 Oct 2019.

Vancouver:

Aktas, Mehmet Fatih 1. Scheduling and flexible control of wide-area data transport services for end-to-end application workflows. [Internet] [Masters thesis]. Rutgers University; 2015. [cited 2019 Oct 24]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/47247/.

Council of Science Editors:

Aktas, Mehmet Fatih 1. Scheduling and flexible control of wide-area data transport services for end-to-end application workflows. [Masters Thesis]. Rutgers University; 2015. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/47247/


Rutgers University

8. Zhang, Fan, 1985-. Programming and runtime support for enabling data-intensive coupled scientific simulation workflows.

Degree: PhD, Electrical and Computer Engineering, 2015, Rutgers University

Emerging coupled scientific simulation workflows are composed of multiple component applications that interact and exchange data at runtime. Coupled simulation workflow enables multi-physics multi-model code… (more)

Subjects/Keywords: Quantitative research; Computer simulation

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APA (6th Edition):

Zhang, Fan, 1. (2015). Programming and runtime support for enabling data-intensive coupled scientific simulation workflows. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/47618/

Chicago Manual of Style (16th Edition):

Zhang, Fan, 1985-. “Programming and runtime support for enabling data-intensive coupled scientific simulation workflows.” 2015. Doctoral Dissertation, Rutgers University. Accessed October 24, 2019. https://rucore.libraries.rutgers.edu/rutgers-lib/47618/.

MLA Handbook (7th Edition):

Zhang, Fan, 1985-. “Programming and runtime support for enabling data-intensive coupled scientific simulation workflows.” 2015. Web. 24 Oct 2019.

Vancouver:

Zhang, Fan 1. Programming and runtime support for enabling data-intensive coupled scientific simulation workflows. [Internet] [Doctoral dissertation]. Rutgers University; 2015. [cited 2019 Oct 24]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/47618/.

Council of Science Editors:

Zhang, Fan 1. Programming and runtime support for enabling data-intensive coupled scientific simulation workflows. [Doctoral Dissertation]. Rutgers University; 2015. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/47618/


Rutgers University

9. Matyasfalvi, Gyorgy, 1983-. OPOS: object-parallel optimization software.

Degree: PhD, Operations Research, 2018, Rutgers University

This dissertation describes OPOS, a C++ software library and framework for developing massively parallel continuous optimization software. We show that classical iterative optimization algorithms such… (more)

Subjects/Keywords: Program transformation (Computer programming)

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APA (6th Edition):

Matyasfalvi, Gyorgy, 1. (2018). OPOS: object-parallel optimization software. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/59168/

Chicago Manual of Style (16th Edition):

Matyasfalvi, Gyorgy, 1983-. “OPOS: object-parallel optimization software.” 2018. Doctoral Dissertation, Rutgers University. Accessed October 24, 2019. https://rucore.libraries.rutgers.edu/rutgers-lib/59168/.

MLA Handbook (7th Edition):

Matyasfalvi, Gyorgy, 1983-. “OPOS: object-parallel optimization software.” 2018. Web. 24 Oct 2019.

Vancouver:

Matyasfalvi, Gyorgy 1. OPOS: object-parallel optimization software. [Internet] [Doctoral dissertation]. Rutgers University; 2018. [cited 2019 Oct 24]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/59168/.

Council of Science Editors:

Matyasfalvi, Gyorgy 1. OPOS: object-parallel optimization software. [Doctoral Dissertation]. Rutgers University; 2018. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/59168/


Rutgers University

10. Liu, Li, 1985-. Creating overview visualizations for data understanding.

Degree: PhD, Electrical and Computer Engineering, 2019, Rutgers University

 One of the major challenges in data visualization is the presentation problem: having too much information to display at a time in one screen. The… (more)

Subjects/Keywords: Information visualization

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APA (6th Edition):

Liu, Li, 1. (2019). Creating overview visualizations for data understanding. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/60035/

Chicago Manual of Style (16th Edition):

Liu, Li, 1985-. “Creating overview visualizations for data understanding.” 2019. Doctoral Dissertation, Rutgers University. Accessed October 24, 2019. https://rucore.libraries.rutgers.edu/rutgers-lib/60035/.

MLA Handbook (7th Edition):

Liu, Li, 1985-. “Creating overview visualizations for data understanding.” 2019. Web. 24 Oct 2019.

Vancouver:

Liu, Li 1. Creating overview visualizations for data understanding. [Internet] [Doctoral dissertation]. Rutgers University; 2019. [cited 2019 Oct 24]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/60035/.

Council of Science Editors:

Liu, Li 1. Creating overview visualizations for data understanding. [Doctoral Dissertation]. Rutgers University; 2019. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/60035/

11. Elangovan, Karthik, 1988-. Adaptive memory power management techniques for HPC workloads.

Degree: MS, Electrical and Computer Engineering, 2012, Rutgers University

 The memory subsystem is responsible for a large fraction of the energy consumed by compute nodes in High Performance Computing (HPC) systems. The rapid increase… (more)

Subjects/Keywords: High performance computing; Magnetic memory (Computers)

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APA (6th Edition):

Elangovan, Karthik, 1. (2012). Adaptive memory power management techniques for HPC workloads. (Masters Thesis). Rutgers University. Retrieved from http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000064079

Chicago Manual of Style (16th Edition):

Elangovan, Karthik, 1988-. “Adaptive memory power management techniques for HPC workloads.” 2012. Masters Thesis, Rutgers University. Accessed October 24, 2019. http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000064079.

MLA Handbook (7th Edition):

Elangovan, Karthik, 1988-. “Adaptive memory power management techniques for HPC workloads.” 2012. Web. 24 Oct 2019.

Vancouver:

Elangovan, Karthik 1. Adaptive memory power management techniques for HPC workloads. [Internet] [Masters thesis]. Rutgers University; 2012. [cited 2019 Oct 24]. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000064079.

Council of Science Editors:

Elangovan, Karthik 1. Adaptive memory power management techniques for HPC workloads. [Masters Thesis]. Rutgers University; 2012. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000064079

12. Doddaghatta Shashidhar, Sharat Chandra, 1986-. Study of application aware techniques for system and runtime power management.

Degree: MS, Electrical and Computer Engineering, 2012, Rutgers University

 Energy efficiency of large-scale data centers is becoming a major concern not only for reasons of energy conservation, failures, and cost reduction, but also because… (more)

Subjects/Keywords: High performance computing – Energy consumption; High performance computing – Management

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APA (6th Edition):

Doddaghatta Shashidhar, Sharat Chandra, 1. (2012). Study of application aware techniques for system and runtime power management. (Masters Thesis). Rutgers University. Retrieved from http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000066725

Chicago Manual of Style (16th Edition):

Doddaghatta Shashidhar, Sharat Chandra, 1986-. “Study of application aware techniques for system and runtime power management.” 2012. Masters Thesis, Rutgers University. Accessed October 24, 2019. http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000066725.

MLA Handbook (7th Edition):

Doddaghatta Shashidhar, Sharat Chandra, 1986-. “Study of application aware techniques for system and runtime power management.” 2012. Web. 24 Oct 2019.

Vancouver:

Doddaghatta Shashidhar, Sharat Chandra 1. Study of application aware techniques for system and runtime power management. [Internet] [Masters thesis]. Rutgers University; 2012. [cited 2019 Oct 24]. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000066725.

Council of Science Editors:

Doddaghatta Shashidhar, Sharat Chandra 1. Study of application aware techniques for system and runtime power management. [Masters Thesis]. Rutgers University; 2012. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000066725

13. Aithal, Suhas R, 1988-. Scalable data analytics for ensemble learning.

Degree: MS, Electrical and Computer Engineering, 2013, Rutgers University

 Data Anlaytic techniques have enhanced human ability to solve a lot of data related problems. It has opened a window through which an analyst can… (more)

Subjects/Keywords: Machine learning; Computer science – Mathematics

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APA (6th Edition):

Aithal, Suhas R, 1. (2013). Scalable data analytics for ensemble learning. (Masters Thesis). Rutgers University. Retrieved from http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000068808

Chicago Manual of Style (16th Edition):

Aithal, Suhas R, 1988-. “Scalable data analytics for ensemble learning.” 2013. Masters Thesis, Rutgers University. Accessed October 24, 2019. http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000068808.

MLA Handbook (7th Edition):

Aithal, Suhas R, 1988-. “Scalable data analytics for ensemble learning.” 2013. Web. 24 Oct 2019.

Vancouver:

Aithal, Suhas R 1. Scalable data analytics for ensemble learning. [Internet] [Masters thesis]. Rutgers University; 2013. [cited 2019 Oct 24]. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000068808.

Council of Science Editors:

Aithal, Suhas R 1. Scalable data analytics for ensemble learning. [Masters Thesis]. Rutgers University; 2013. Available from: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000068808

.