Autor/Autorin: Mugonza Robert
Robert Mugonza has worked in MUST for the past 7 years under the university. He holds a MSc (Hons) Advanced Computer Science and BSc. (Hons) Computer Science. Brain storming on something has always been his hobby and taking on new challenges in new perspectives in the other one.
Understanding of behavioural aspects of workloads has existed since the birth of grid computing in the early 80´s. It is currently and widely important as studies to support fast solutions for efficient energy computing solutions is underway. Cloud computing, is a business concept for online convenient, on-demand scalable internet services as in, has quickly grown as a high profile computing paradigm that shares with ubiquitous, grid, mobile and utility computing. In this project, we study the 1st ever Google data centre cloud traces with interest of furthering our understanding of server utilisation and resource consumption within data centre clouds. To achieve this, we explore the 1st ever trace logs of the Google data centres via a combination of both coarse grain and fine grain analysis that include discovering interesting trace log statistical properties, characterising the data trace, and studying the impacts of the unrealistic task behaviour onto the data centre energy efficiency.
Understanding of behavioural aspects of workloads has existed since the birth of grid computing in the early 80's. It is currently and widely important as studies to support fast solutions for efficient energy computing solutions is underway. Cloud computing, is a business concept for online convenient, on-demand scalable internet services as in, has quickly grown as a high profile computing paradigm that shares with ubiquitous, grid, mobile and utility computing. In this project, we study the 1st ever Google data centre cloud traces with interest of furthering our understanding of server utilisation and resource consumption within data centre clouds. To achieve this, we explore the 1st ever trace logs of the Google data centres via a combination of both coarse grain and fine grain analysis that include discovering interesting trace log statistical properties, characterising the data trace, and studying the impacts of the unrealistic task behaviour onto the data centre energy efficiency.
Mugonza, Robert
Robert Mugonza has worked in MUST for the past 7 years under the university. He holds a MSc (Hons) Advanced Computer Science and BSc. (Hons) Computer Science. Brain storming on something has always been his hobby and taking on new challenges in new perspectives in the other one.
Über den Autor
Robert Mugonza trabalhou na MUST durante os últimos 7 anos na universidade. Possui um MSc (Hons) Advanced Computer Science e um BSc (Hons) Computer Science. O seu hobby tem sido sempre o de enfrentar novos desafios e novas perspectivas no outro.
Klappentext
Understanding of behavioural aspects of workloads has existed since the birth of grid computing in the early 80¿s. It is currently and widely important as studies to support fast solutions for efficient energy computing solutions is underway. Cloud computing, is a business concept for online convenient, on-demand scalable internet services as in, has quickly grown as a high profile computing paradigm that shares with ubiquitous, grid, mobile and utility computing. In this project, we study the 1st ever Google data centre cloud traces with interest of furthering our understanding of server utilisation and resource consumption within data centre clouds. To achieve this, we explore the 1st ever trace logs of the Google data centres via a combination of both coarse grain and fine grain analysis that include discovering interesting trace log statistical properties, characterising the data trace, and studying the impacts of the unrealistic task behaviour onto the data centre energy efficiency.
