Vol: 61(75) No: 1 / March 2016 |
Supporting Health Smart System Applications in Scientific Gateway Environment
MTA SZTAKI, H-1518 Budapest, Pf. 63., Hungary, phone: +36 (1) 666-5554, e-mail: firstname.lastname@example.org
Óbuda University, John von Neumann Faculty of Informatics, Biotech Lab, Bécsi str. 96/b., H-1034, Budapest, Hungary, phone: +36 (1) 666-5554, e-mail: email@example.com
MTA SZTAKI, H-1518 Budapest, Pf. 63., Hungary, phone: +36 (1) 666-5554, e-mail: firstname.lastname@example.org
Keywords: smart system application, execution optimization, execution time minimization, direct cloud
Smart system applications are spreading widely good example of such is health monitoring. Data captured from sensors arrive continuously and only efficient large scale data handling using data stream processing can cope with the transport requirements. Stream processing means that without intermediate storage the data should be processed as they are received from the sensors. Storing and transporting data takes a lot of time, and data transport time has significant impact on the overall execution of the health monitoring smart application. In this paper on one hand we have defined an execution model for smart system applications for common science gateway solutions with minimized data movement on the other hand we have developed a simulation environment and we have investigated the influencing factors of the time needed to execute the jobs. Our ultimate goal is to optimize the overall time needed to process data and submit the smart system applications. With help of statistical tools we have also performed computations and in case of different middlewares and storages we have carried out measurements. The results show that both the job submission time and the data transmission/delivery time is minimal for applications that use Direct Cloud access in Pgrade/gUSE/DCI-Bridge environment. We have also investigated the effects of the parallelization degree on the system efficiency and the results show that until a certain degree the parallelization also positively effects the system’s throughput.
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