High-Performance Edge Computing: An Energy Data Lakes Case Study

dc.cclicenceN/Aen
dc.contributor.authorAlsalemi, Abdullah
dc.contributor.authorAmira, Abbes
dc.contributor.authorMalekmohamadi, Hossein
dc.contributor.authorDiao, Kegong
dc.date.acceptance2022-11-10
dc.date.accessioned2023-01-23T15:07:18Z
dc.date.available2023-01-23T15:07:18Z
dc.date.issued2023
dc.description.abstractWith the varying applications of Artificial Intelligence (AI) on an ever expanding global scale, performance metrics and standards of computing platforms have steady elevated. High-Performance Edge Computing (HPEC) can play an instrumental role in lifting a substantial load on cloud computing solutions when it comes to deploying Deep Learning (DL) algorithms for classifying big data. Notwithstanding, the collection, pre-processing, and post-processing of big data engenders many challenges and opportunities for optimizing HPEC performance for data classification and, in turn, yield better outcomes. This is where the concept of data lakes is employed, which are raw-formatted large masses of data that are plausibly more compatible with many algorithms than structured data stores. Therefore, in this book chapter, we aim to carry out a comparative study that examines the merits, performance and efficiency metrics, and limitations of two notable HPECs centered around a DL data lake classification framework. Comparing the HPEC platforms, with similar classification accuracy of ~90%, results show that adequate performance and impressive computational efficiency, as fast as 8.19 msec per classified GAF image is achieved on the Jetson Nano.en
dc.funderNo external funderen
dc.identifier.citationAlsalemi, A., Amira, A., Malekmohamadi, H. and Diao, K. (2023) High-Performance Edge Computing: An Energy Data Lakes Case Study. In: Handbook of Research on Integrating Machine Learning Into HPC-Based Simulations and Analytics.en
dc.identifier.urihttps://hdl.handle.net/2086/22458
dc.language.isoenen
dc.peerreviewedYesen
dc.publisherIGI Globalen
dc.subjectHigh performance edge computingen
dc.subjectenergy efficiencyen
dc.subjectartificial intelligenceen
dc.subjectdata lakeen
dc.subjectdeep learningen
dc.subjectinternet of energyen
dc.titleHigh-Performance Edge Computing: An Energy Data Lakes Case Studyen
dc.typeBook chapteren

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