Journal Article10.1109/COMST.2015.2481183
Data Center Energy Consumption Modeling: A Survey
995
TL;DR: An in-depth study of the existing literature on data center power modeling, covering more than 200 models, organized in a hierarchical structure with two main branches focusing on hardware-centric and software-centric power models.
read more
Abstract: Data centers are critical, energy-hungry infrastructures that run large-scale Internet-based services. Energy consumption models are pivotal in designing and optimizing energy-efficient operations to curb excessive energy consumption in data centers. In this paper, we survey the state-of-the-art techniques used for energy consumption modeling and prediction for data centers and their components. We conduct an in-depth study of the existing literature on data center power modeling, covering more than 200 models. We organize these models in a hierarchical structure with two main branches focusing on hardware-centric and software-centric power models. Under hardware-centric approaches we start from the digital circuit level and move on to describe higher-level energy consumption models at the hardware component level, server level, data center level, and finally systems of systems level. Under the software-centric approaches we investigate power models developed for operating systems, virtual machines and software applications. This systematic approach allows us to identify multiple issues prevalent in power modeling of different levels of data center systems, including: i) few modeling efforts targeted at power consumption of the entire data center ii) many state-of-the-art power models are based on a few CPU or server metrics, and iii) the effectiveness and accuracy of these power models remain open questions. Based on these observations, we conclude the survey by describing key challenges for future research on constructing effective and accurate data center power models.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Uncertainty-Aware Optimisation for Sustainable Multimedia Event Processing in Big Data Streams
Felipe Arruda Pontes,Michael Schukat,Edward Curry +2 more
- 15 Dec 2023
TL;DR: This study addresses the environmental impact of growing Big Data stream applications and focuses on optimising MEP systems to mitigate this issue by using uncertainty-aware solutions for the service selection problem in order to improve the Quality of Service (QoS) within MEP systems.
Colocation Datacenter Customer Power Usage Forecasting Using Synthetic Data and Integration of Macroeconomic Indicators
Neda Zarayeneh,Malarvizhi Sankaranarayanasamy,Pegah Mavaie,Omanshu Thapliyal,Prasun Singh,Ravigopal Vennelakanti +5 more
- 15 Dec 2023
TL;DR: This work proposes an innovative power forecasting system that takes into account both internal and external demand signals, leading to more accurate and realistic predictions, and synthesized a comprehensive dataset tailored to the data center environment using the small dataset provided by S&P Global.
•Posted Content
Are Deep Policy Gradient Algorithms Truly Policy Gradient Algorithms
Andrew Ilyas,Logan Engstrom,Shibani Santurkar,Dimitris Tsipras,Firdaus Janoos,Larry Rudolph,Aleksander Madry +6 more
- 06 Nov 2018
TL;DR: A fine-grained analysis of state-of-the-art methods based on key aspects of this framework: gradient estimation, value prediction, optimization landscapes, and trust region enforcement is proposed.
Calculating the Carbon Footprint of Streaming Media: Beyond the Myth of Efficiency
Stephen Makonin,Laura Marks,Radek Przedpelski,Alejandro Rodriquez-Silva,Ramy ElMallah +4 more
- 14 Jun 2022
TL;DR: This model works by calculating the environmental impact of watching one hour of Netflix and showing the carbon footprint of a stream and the impact of unused energy in data centers, and proposes a holistic end-to-end model that balances the high-level and highly detailed.
Key-Value Stores on Flash Storage Devices: A Survey
TL;DR: In this paper , a literature survey aims to highlight the changes proposed in the last decade to optimise key-value stores for flash devices and predict what role these devices might play for keyvalue stores in the future.
References
•Book
Data Mining: Practical Machine Learning Tools and Techniques
Ian H. Witten,Eibe Frank,Mark Hall +2 more
- 25 Oct 1999
TL;DR: This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
25.4K
MapReduce: simplified data processing on large clusters
Jeffrey Dean,Sanjay Ghemawat +1 more
- 06 Dec 2004
TL;DR: This paper presents the implementation of MapReduce, a programming model and an associated implementation for processing and generating large data sets that runs on a large cluster of commodity machines and is highly scalable.
MapReduce: simplified data processing on large clusters
Jeffrey Dean,Sanjay Ghemawat +1 more
TL;DR: This presentation explains how the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks.
A fast learning algorithm for deep belief nets
TL;DR: A fast, greedy algorithm is derived that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory.
Related Papers (5)
Xiaobo Fan,Wolf-Dietrich Weber,Luiz Andre Barroso +2 more
- 09 Jun 2007
Luiz Andre Barroso,Urs Hölzle +1 more