Resource monitoring and state prediction for mobile cloud platform

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dc.contributor.authorYang, Xuesong
dc.contributor.supervisorGanti, Sudhakar
dc.date.accessioned2017-04-24T20:42:42Z
dc.date.available2017-04-24T20:42:42Z
dc.date.copyright2017en_US
dc.date.issued2017-04-24
dc.degree.departmentDepartment of Computer Science
dc.degree.levelMaster of Applied Science M.A.Sc.en_US
dc.description.abstractMobile Cloud Computing (MCC) is a novel technology that combines mobile device with cloud computing. However, it has some inherent shortcomings compared with traditional cloud computing. For example, there will be unstable communications, intermittent connections, and limitation of power supply. Also unpredictably resources can join and leave cloud environment which makes mobile cloud operation and management more and more complex. In order to obtain stable resource participation of mobile devices, a combined push-pull method [11] has been proposed for collecting and analyzing dynamic information of mobile devices. At monitored nodes, a periodically push model is used to timely send the loading resources to the monitoring components. At monitoring nodes, a pull model is triggered by the change degree. If the change degree is greater than the threshold or the update loading information is missed during the time interval, the monitoring component immediately sends a query to monitored node for the latest loading information. The experimental results show that the model can effectively improve the monitoring performance for mobile environment and reduce communication overhead.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/7974
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectResource Monitoringen_US
dc.subjectState Predictionen_US
dc.subjectMobile Cloud Computingen_US
dc.subjectPush modelen_US
dc.subjectPull modelen_US
dc.subjectPush-Pull Hybrid Modelen_US
dc.subjectChange Degreeen_US
dc.subjectUser Tolerant Degreeen_US
dc.subjectExponentially Weighted Moving Averageen_US
dc.titleResource monitoring and state prediction for mobile cloud platformen_US
dc.typeprojecten_US

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