Cloud Computing: Distributed Internet Computing for IT and Scientific Research
By M. Dikaiakos, Dimitrios Katsaros, P. Mehra, George Pallis, Athena Vakali. IEEE Internet Computing, 2009
918 citations recorded by the source corpus.
Subfields: Information Systems
View Paper: https://www.semanticscholar.org/paper/4f6577ead70ef97d620dec986cf054ae4a05035d
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Information Systems
- Yuhua Xu
- Henry Shu-Hung Chung
- Shuo Feng
- Zhi-hui Zhan
- Nabendu Chaki
- Ulrike Gretzel
- Chulmo Koo
- Guoru Ding
Citations in the corpus
- A Survey on Security Issues in Cloud Computing (2011, background): “The internal infrastructure of the organization is needed to maintain a balance to handle the interoperability between different cloud platforms [22].”
- International Conference on Computational Intelligence: Modeling Techniques and Applications (CIMTA) 2013 A Genetic Algorithm (GA) based Load Balancing Strategy for Cloud Computing (2013, background): “accommodates provisioning and de-provisioning on demand and helps any organization in avoiding the capital costs of software and hardware [2].”
- Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches (2015, background): “Underestimating the provision of resources would lead to broken SLAs and , while overestimating the provision would lead to resource under-utilization and lost revenue [Dikaiakos et al. 2009].”
- From cloud computing to cloud manufacturing (2012, neutral): “inline commerce [6], conference origination [7], and”
- Conceptualization of Smart Tourism Destination Competitiveness (2016, background): “The cloud services enable the multiple entities, such as applications, programs, or data, to share the information by providing scalable access (Dikaiakos et al., 2009).”
- A survey of machine learning for big data processing (2016, background): “Cloud computing [78, 79] has already demonstrated admirable elasticity that bears the hope of realizing the needed scalability for machine learning algorithms.”
- Cloud Load Balancing Techniques : A Step Towards Green Computing (2012, background): “It helps to accommodate changes in demand and helps any organization in avoiding the capital costs of software and hardware [2] [3].”