Performance and Energy Savings Trade-Off with Uncertainty-Aware Cloud Workload Forecasting

Diego Carraro; Andrea Rossi; Andrea Visentin; Steven Prestwich; Kenneth N. Brown

doi:10.1109/ICNP59255.2023.10355570

Performance and Energy Savings Trade-Off with Uncertainty-Aware Cloud Workload Forecasting

Diego Carraro
, Andrea Rossi
, Andrea Visentin
, Steven Prestwich
, Kenneth N. Brown

University College Cork

Research output: Chapter in Book/Report/Conference proceedings › Conference proceeding › peer-review

Abstract

Cloud computing has seen widespread adoption because it increases the productivity and efficiency of industries and allows for effective scalability of their business [1]. Guaranteeing performance levels is at the core of cloud services and requires huge computational resources, especially with the latest advances in technologies such as Artificial Intelligence and the Internet of Things [2]. Typically, customers subscribe to agreements where cloud providers ensure specific levels of reliability, availability and responsiveness to systems and applications and describe penalties if the service levels are not met. At the same time, massive computational resources are a cost for providers and have a significant environmental impact, which will increase in the future. It is estimated that the energy consumption of data centres (which host cloud services) will grow from 292 TWh in 2016 to 353 TWh in 2030 [3], and greenhouse gas emissions will increase over 14% in 2040, compared to a 1-1.6% increase in the 2007-2016 [4].

Original language	English
Title of host publication	2023 IEEE 31st International Conference on Network Protocols, ICNP 2023
Publisher	IEEE Computer Society
ISBN (Electronic)	9798350303223
DOIs	https://doi.org/10.1109/ICNP59255.2023.10355570
Publication status	Published - 2023
Event	31st IEEE International Conference on Network Protocols, ICNP 2023 - Reykjavik, Iceland Duration: 10 Oct 2023 → 13 Oct 2023

Publication series

Name	Proceedings - International Conference on Network Protocols, ICNP
ISSN (Print)	1092-1648

Conference

Conference	31st IEEE International Conference on Network Protocols, ICNP 2023
Country/Territory	Iceland
City	Reykjavik
Period	10/10/23 → 13/10/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy
SDG 9 Industry, Innovation, and Infrastructure
SDG 13 Climate Action

Keywords

Cloud Computing
Deep Learning
Energy Saving
Time Series Forecasting
Uncertainty
Workload Prediction

Access to Document

10.1109/ICNP59255.2023.10355570

Cite this

Carraro, D., Rossi, A., Visentin, A., Prestwich, S., & Brown, K. N. (2023). Performance and Energy Savings Trade-Off with Uncertainty-Aware Cloud Workload Forecasting. In 2023 IEEE 31st International Conference on Network Protocols, ICNP 2023 (Proceedings - International Conference on Network Protocols, ICNP). IEEE Computer Society. https://doi.org/10.1109/ICNP59255.2023.10355570

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title = "Performance and Energy Savings Trade-Off with Uncertainty-Aware Cloud Workload Forecasting",

abstract = "Cloud computing has seen widespread adoption because it increases the productivity and efficiency of industries and allows for effective scalability of their business [1]. Guaranteeing performance levels is at the core of cloud services and requires huge computational resources, especially with the latest advances in technologies such as Artificial Intelligence and the Internet of Things [2]. Typically, customers subscribe to agreements where cloud providers ensure specific levels of reliability, availability and responsiveness to systems and applications and describe penalties if the service levels are not met. At the same time, massive computational resources are a cost for providers and have a significant environmental impact, which will increase in the future. It is estimated that the energy consumption of data centres (which host cloud services) will grow from 292 TWh in 2016 to 353 TWh in 2030 [3], and greenhouse gas emissions will increase over 14\% in 2040, compared to a 1-1.6\% increase in the 2007-2016 [4].",

keywords = "Cloud Computing, Deep Learning, Energy Saving, Time Series Forecasting, Uncertainty, Workload Prediction",

author = "Diego Carraro and Andrea Rossi and Andrea Visentin and Steven Prestwich and Brown, \{Kenneth N.\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 31st IEEE International Conference on Network Protocols, ICNP 2023 ; Conference date: 10-10-2023 Through 13-10-2023",

year = "2023",

doi = "10.1109/ICNP59255.2023.10355570",

language = "English",

series = "Proceedings - International Conference on Network Protocols, ICNP",

publisher = "IEEE Computer Society",

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Carraro, D, Rossi, A, Visentin, A , Prestwich, S & Brown, KN 2023, Performance and Energy Savings Trade-Off with Uncertainty-Aware Cloud Workload Forecasting. in 2023 IEEE 31st International Conference on Network Protocols, ICNP 2023. Proceedings - International Conference on Network Protocols, ICNP, IEEE Computer Society, 31st IEEE International Conference on Network Protocols, ICNP 2023, Reykjavik, Iceland, 10/10/23. https://doi.org/10.1109/ICNP59255.2023.10355570

Performance and Energy Savings Trade-Off with Uncertainty-Aware Cloud Workload Forecasting. / Carraro, Diego; Rossi, Andrea; Visentin, Andrea et al.
2023 IEEE 31st International Conference on Network Protocols, ICNP 2023. IEEE Computer Society, 2023. (Proceedings - International Conference on Network Protocols, ICNP).

Research output: Chapter in Book/Report/Conference proceedings › Conference proceeding › peer-review

TY - GEN

T1 - Performance and Energy Savings Trade-Off with Uncertainty-Aware Cloud Workload Forecasting

AU - Carraro, Diego

AU - Rossi, Andrea

AU - Visentin, Andrea

AU - Prestwich, Steven

AU - Brown, Kenneth N.

PY - 2023

Y1 - 2023

N2 - Cloud computing has seen widespread adoption because it increases the productivity and efficiency of industries and allows for effective scalability of their business [1]. Guaranteeing performance levels is at the core of cloud services and requires huge computational resources, especially with the latest advances in technologies such as Artificial Intelligence and the Internet of Things [2]. Typically, customers subscribe to agreements where cloud providers ensure specific levels of reliability, availability and responsiveness to systems and applications and describe penalties if the service levels are not met. At the same time, massive computational resources are a cost for providers and have a significant environmental impact, which will increase in the future. It is estimated that the energy consumption of data centres (which host cloud services) will grow from 292 TWh in 2016 to 353 TWh in 2030 [3], and greenhouse gas emissions will increase over 14% in 2040, compared to a 1-1.6% increase in the 2007-2016 [4].

AB - Cloud computing has seen widespread adoption because it increases the productivity and efficiency of industries and allows for effective scalability of their business [1]. Guaranteeing performance levels is at the core of cloud services and requires huge computational resources, especially with the latest advances in technologies such as Artificial Intelligence and the Internet of Things [2]. Typically, customers subscribe to agreements where cloud providers ensure specific levels of reliability, availability and responsiveness to systems and applications and describe penalties if the service levels are not met. At the same time, massive computational resources are a cost for providers and have a significant environmental impact, which will increase in the future. It is estimated that the energy consumption of data centres (which host cloud services) will grow from 292 TWh in 2016 to 353 TWh in 2030 [3], and greenhouse gas emissions will increase over 14% in 2040, compared to a 1-1.6% increase in the 2007-2016 [4].

KW - Cloud Computing

KW - Deep Learning

KW - Energy Saving

KW - Time Series Forecasting

KW - Uncertainty

KW - Workload Prediction

UR - https://www.scopus.com/pages/publications/85182508073

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DO - 10.1109/ICNP59255.2023.10355570

M3 - Conference proceeding

AN - SCOPUS:85182508073

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ER -

Performance and Energy Savings Trade-Off with Uncertainty-Aware Cloud Workload Forecasting

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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Cite this