TY - GEN
T1 - Fitting realistic data centre workloads a data science approach
AU - Postema, Björn F.
AU - Geuze, Niels J.
AU - Haverkort, Boudewijn R.
N1 - Publisher Copyright:
© 2018 Copyright held by the owner/author(s).
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018/6/12
Y1 - 2018/6/12
N2 - Data centres are playing a pivotal role in all cloud-based services (e-commerce, social networks, financial services, e-government, etc.). The performance of data centres is crucial for the acceptance of all these services by end-users. It is important to carefully design data centres with both performance and energy considerations in mind, as data centres are also known to use large amounts of electrical energy. For that purpose we have developed a modular simulation model (based on Anylogic) that can be used to study performance-energy trade-offs in data centre design. Key to such studies is the availability of a workload model. In this paper we present a workload characterisation model and algorithm using modern-day data science techniques, building on top of Jupyter Notebook and the ProFiDo platform. We present the method and show its versatility on a case study with real-world traces of 20 million entries, provided by the Dutch company better.be.
AB - Data centres are playing a pivotal role in all cloud-based services (e-commerce, social networks, financial services, e-government, etc.). The performance of data centres is crucial for the acceptance of all these services by end-users. It is important to carefully design data centres with both performance and energy considerations in mind, as data centres are also known to use large amounts of electrical energy. For that purpose we have developed a modular simulation model (based on Anylogic) that can be used to study performance-energy trade-offs in data centre design. Key to such studies is the availability of a workload model. In this paper we present a workload characterisation model and algorithm using modern-day data science techniques, building on top of Jupyter Notebook and the ProFiDo platform. We present the method and show its versatility on a case study with real-world traces of 20 million entries, provided by the Dutch company better.be.
KW - Data centre
KW - Data science
KW - Distribution fitting
KW - Mixture normal distribution
KW - Workload modelling
UR - http://www.scopus.com/inward/record.url?scp=85050194379&partnerID=8YFLogxK
U2 - 10.1145/3208903.3213520
DO - 10.1145/3208903.3213520
M3 - Conference contribution
AN - SCOPUS:85050194379
T3 - e-Energy 2018 - Proceedings of the 9th ACM International Conference on Future Energy Systems
SP - 486
EP - 491
BT - e-Energy 2018 - Proceedings of the 9th ACM International Conference on Future Energy Systems
PB - Association for Computing Machinery
T2 - 9th ACM International Conference on Future Energy Systems, e-Energy 2018
Y2 - 12 June 2018 through 15 June 2018
ER -