{"id":541,"date":"2019-11-12T15:13:41","date_gmt":"2019-11-12T07:13:41","guid":{"rendered":"http:\/\/nljrc.pk.njau.edu.cn\/?p=541"},"modified":"2019-11-12T15:13:41","modified_gmt":"2019-11-12T07:13:41","slug":"lecture-lecturer-john-panneerselvam-university-of-derby","status":"publish","type":"post","link":"http:\/\/172.30.27.2\/index.php\/lecture-lecturer-john-panneerselvam-university-of-derby\/","title":{"rendered":"Lecture \u2013 Lecturer. John Panneerselvam, University of Derby"},"content":{"rendered":"

Prescriptive Analytics for Energy Efficient Datacentres<\/span><\/p>\n

2018-12-26<\/span><\/div>\n
No. A504, Yuxian Building, College of Engineering, Nanjing Agricultural University<\/span><\/div>\n
Contents of the Lecture\uff1a<\/span><\/div>\n
\n

Given the evolution of Cloud Computing in recent years, users and clients adopting Cloud Computing for both personal and business needs have increased at an unprecedented scale. This has naturally led to the increased deployments and implementations of Cloud datacentres across the globe. As a consequence of this increasing adoption of Cloud Computing, Cloud datacentres are witnessed to be massive energy consumers and environmental polluters. Whilst the energy implications of Cloud datacentres are being addressed from various research perspectives, predicting the future trend and behaviours of workloads at the datacentres thereby reducing the active server resources is one particular dimension of green computing gaining the interests of researchers and Cloud providers. However, this includes various practical and analytical challenges imposed by the increased dynamism of Cloud systems. The behavioural characteristics of Cloud workloads and users are still not perfectly clear which restrains the reliability of the prediction accuracy of existing research works in this context. To this end, this talk presents our descriptive analytics that uncovers the hidden caused of excess energy expenditures in datacentre execution along with our developed novel resource optimisation framework that aims to avail the most optimum level of resources for executing jobs with reduced server energy expenditures and job terminations. This optimisation framework encompasses a resource estimation module to predict the anticipated resource consumption level for the arrived jobs and a classification module to classify tasks based on their resource intensiveness.<\/p>\n

<\/p>\n

About the Reporter\uff1a<\/span><\/p>\n

Liming (Luke) Chen is Professor of Data Analytics in the School of Computing at Ulster University, UK. He received his BEng and MEng from Beijing Institute of Technology (BIT), China, and his Ph.D in Artificial Intelligence from De Montfort University, UK. His current research interests include data analytics, pervasive computing, artificial intelligence, user-centred intelligent systems and their applications in smart healthcare. Liming is an IET Fellow, an IEEE Senior Member, a co-founder and co-director of the UK-China Gait and Health Innovation Institute, the DMU-USTB (University of Science and Technology Beijing, China) Joint Research Lab. on Smart Healthcare, and the IEEE CIS \u201dUser-centred Smart Systems\u201d Task Force. He is currently the coordinator of the EU Horizon2020 MSCA ITN ACROSSING project, and has serves as the principal investigator for the EU AAL PIA project, the MobileSage project and FP7 MICHELANGELO project, and a number of projects funded by industry and third countries. Liming has over 200 publications in internationally recognised journals, book series and conferences. He is the general chair or program chair for IEEE Smart World Congress 2019, IEEE UIC2017, IEEE HealthCom2017, SAI Computing 2017, IEEE UIC2016, IntelliSys2016, MoMM2015\/2014\/2013, SAI2015\/2013, IWAAL2014, UCAMI2013, and an organising chair of many workshops such as Romart-City2016 and SAGAware2015\/2012, associate editor of IEEE THMS, assistant EIC for IJPCC and guest editors for IEEE THMS, PMC and IJDSN. He has delivered over 20 talks, keynotes and seminars in various forums, conferences, industry and academic events.<\/span><\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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