Scalable Data-agnostic Processing Model with a Priori Scheduling for the Cloud
Embargo Lift Date
MetadataShow full item record
Cloud computing is identified to be a promising solution to performing big data analytics. However, the maximization of cloud utilization incorporated with optimizing intranode, internode, and memory management is still an open-ended challenge. This thesis presents a novel resource allocation model for cloud to load-balance data-agnostic tasks, minimizing intranode and internode delays, and decreasing memory consumption where these processes are involved in big data analytics. In conclusion, the proposed model outperforms existing techniques.
Showing items related by title, author, creator and subject.
Alhamad, Mohammed (2011)Cloud computing has changed the strategy used for providing distributed services to many business and government agents. Cloud computing delivers scalable and on-demand services to most users in different domains. However, ...
Pichan, A.; Lazarescu, Mihai; Soh, S. (2015)Cloud computing is arguably one of the most significant advances in information technology (IT) services today. Several cloud service providers (CSPs) have offered services that have produced various transformative changes ...
Lopez, J.; Cunningham, M.; Jones, P.; Marshall, J.; Bronfman, L.; Lo, N.; Walsh, Andrew (2016)We have analysed the chemical and kinematic properties of the 20 and 50 km s−1 molecular clouds in the Central Molecular Zone of the Milky Way Galaxy, as well as those of the molecular ridge bridging these two clouds. Our ...