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On the Energy Consumption Forecasting of Data Centers Based on Weather Conditions: Remote Sensing and Machine Learning Approach
Division of Technology Management & Wholesale Market, CYTA Hellas, Greece.
Computer Science Department, University of Crete, Greece.
Division of Technology Management & Wholesale Market, CYTA Hellas, Greece.
Division of Technology Management & Wholesale Market, CYTA Hellas, Greece.
2018 (English)In: 2018 11th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP), IEEE , 2018, p. 1-6Conference paper, Published paper (Refereed)
Abstract [en]

The energy consumption of Data Centers (DCs) is a very important figure for the telecommunications operators, not only in terms of cost, but also in terms of operational reliability. A reliable weather forecast would result in a more efficient management of the available energy and would make it easier to take advantage of the modern types of power-grid based on renewable energy resources. In this paper, we exploit the capabilities provided by the FIESTA-IoT platform in order to investigate the correlation between the weather conditions and the energy consumption in DCs. Then, by using multi-variable linear regression process we model this correlation between the energy consumption and the dominant weather condition parameters in order to effectively forecast the energy consumption based on the weather forecast. This procedure could be part of a wider resources optimization process in the core network towards an end-to-end (e2e) access/core network optimization of resources utilization. We have validated our results through live measurements from the RealDC testbed. Results from our proposed approach indicate that forecasting of energy consumption based on weather conditions could help not only DC operators in managing their cooling systems and power usage, but also electricity companies in optimizing their power distribution systems.

Place, publisher, year, edition, pages
IEEE , 2018. p. 1-6
Keywords [en]
computer centres, energy consumption, Internet of Things, learning (artificial intelligence), optimisation, power distribution, power grids, regression analysis, remote sensing, weather forecasting, energy consumption forecasting, data centers, weather conditions, machine learning approach, reliable weather forecast, renewable energy resources, dominant weather condition parameters, DC, telecommunications operators, operational reliability, power-grid, realDC testbed, e2e access-core network optimization, end-to-end access-core network optimization, multivariable linear regression process, FIESTA-IoT platform, Cooling, Predictive models, Data models, FIESTA-IoT, energy efficiency, weather forecast, linear regression, machine learning, power grid
National Category
Communication Systems
Identifiers
URN: urn:nbn:se:liu:diva-174241DOI: 10.1109/CSNDSP.2018.8471785OAI: oai:DiVA.org:liu-174241DiVA, id: diva2:1537845
Conference
2018 11th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP)
Note

Funding agencies: Part of this work has been funded by the FIESTA-IoT project, with GA number: 643943. The REAL-DC testbed has been utilized for gathering the data that have been used in this work. Georgios Smpokos is a researcher at CYTA-Hellas and funded by the WiVi-2020 project. Mohamed Elshatshat contributed to this work during his secondment to CYTA-Hellas from ICS-FORTH and funded by the WiVi-2020 project. The WiVi-2020 project has received funding from the European Unions Seventh Framework Programme (FP7/2007-2013) under grant agreement no 324515 and from the European Unions Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 642743.

Available from: 2021-03-17 Created: 2021-03-17 Last updated: 2021-03-17Bibliographically approved
In thesis
1. Performance Analysis in Wireless HetNets: Traffic, Energy, and Secrecy Considerations
Open this publication in new window or tab >>Performance Analysis in Wireless HetNets: Traffic, Energy, and Secrecy Considerations
2021 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

To this day, most of the communication networks are characterized by a "monolithic" operating approach. Network elements are configured and operate without any reconfiguration for long time periods. Softwarization, whereby dedicated elements are being replaced by more general-purpose devices, has been lately challenging this existing approach. Virtualizing the infrastructure through the softwarization can provide significant benefits to end users and operators, supporting more flexible service deployment, providing real time monitoring and operational changes. 

In Paper I we study a novel allocation technique and traffic optimization process for the access network. Cellular network technologies (i.e. UMTS, LTE, LTE-A) will coexist with non-cellular small cells and offload traffic from cellular to non-cellular networks mainly operating in 3GPP Wi-Fi (IEEE 802.11 standards). This is a scenario for indoor wireless access implementations where offloading mechanisms can improve the QoS offered by the operators, and reduce the traffic handled by the access fronthaul. The analysis of a novel optimization algorithm exhibited a holistic solution for access-core interworking where LWA (LTE-WiFi Aggregation) offers improved performance for the end users. 

In order to optimize core network operations factors such as the operational costs should be addressed. Following this approach in Paper II we analysed how environmental factors (e.g. temperature, humidity) can affect the power consumption of core network data centers (cooling systems). By applying machine learning techniques using data from a data center, we were able to forecast the power consumption based on to atmospheric weather conditions and analyse its accuracy. 

Optimizing the access network operations and the interworking (resource allocation, scheduling, offloading) can lead to highly configurable and secure operations. These have been factors of great concern as wireless connectivity increases in denser populated areas. In Paper III we examine the physical layer secrecy aspects of a collaborative small cell network in the presence of parallel connections and caching capabilities at the edge nodes. Using tools from the probability theory, we examined how the power allocation for the transmissions can ensure secrecy in the presence of an eavesdropper. 

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2021. p. 19
Series
Linköping Studies in Science and Technology. Licentiate Thesis, ISSN 0280-7971 ; 1903
National Category
Communication Systems
Identifiers
urn:nbn:se:liu:diva-174244 (URN)10.3384/lic.diva-174244 (DOI)9789179296698 (ISBN)
Presentation
2021-04-23, Online, Campus Norrköping, Norrköping, 10:00 (English)
Opponent
Supervisors
Note

Funding agencies: Europen Union's Horizon 2020 Marie Sk lodowska-Curie Actionsproject WiVi-2020 (H2020-MSCA-ITN-2014-EID 642743-WiVi-2020)

Available from: 2021-03-17 Created: 2021-03-17 Last updated: 2021-04-13Bibliographically approved

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