An Analysis of Energy-Efficient Approaches Used for Virtual Machines and Data Centres
2017 (English) In: 2017 IEEE 14th International Conference on e-Business Engineering (ICEBE), IEEE, 2017, p. 91-96, article id 8119135Conference paper, Published paper (Refereed)
Abstract [en]
For online business markets with a large customer base, the use of market-platforms is leading to a rapid generation of a huge amount of data. Such businesses face challenges to satisfy their users. A quantitative research approach has been used to examine big data in online markets, but there is also a need for qualitative research in this area, so as to understand the relationship between big data, online markets. The present research presents an analysis of the various case study approaches that are employed by researchers in this area. We also analyze trends in case study techniques in this area. The research problem is taken on as a research case for the present study. The results of the study should contribute the implementation of big-data in online markets research.
Place, publisher, year, edition, pages IEEE, 2017. p. 91-96, article id 8119135
Keywords [en]
Energy consumption, Energy efficiency, Resource management, Virtual machining, Cloud computing, Algorithm design and analysis, Virtual machines, Data centres
National Category
Energy Systems Computer Systems
Identifiers URN: urn:nbn:se:hj:diva-38167 DOI: 10.1109/ICEBE.2017.23 ISI: 000426981100013 Scopus ID: 2-s2.0-85041668650 ISBN: 978-1-5386-1412-9 (electronic) ISBN: 978-1-5386-1413-6 (print) OAI: oai:DiVA.org:hj-38167 DiVA, id: diva2:1165791
Conference 2017 IEEE 14th International Conference on e-Business Engineering (ICEBE), 4-6 Nov. 2017, Shanghai, China
2017-12-132017-12-132021-03-03 Bibliographically approved