Proceedings ArticleDOI
Utilizing green energy prediction to schedule mixed batch and service jobs in data centers
TLDR
This paper designs an adaptive data center job scheduler which utilizes short term prediction of solar and wind energy production, which enables the number of jobs to be scaled to the expected energy availability, thus reducingThe number of cancelled jobs by 4x and improving green energy usage efficiency by 3x over just utilizing the immediately available green energy.Abstract:
As brown energy costs grow, renewable energy becomes more widely used. Previous work focused on using immediately available green energy to supplement the non-renewable, or brown energy at the cost of canceling and rescheduling jobs whenever the green energy availability is too low [16]. In this paper we design an adaptive data center job scheduler which utilizes short term prediction of solar and wind energy production. This enables us to scale the number of jobs to the expected energy availability, thus reducing the number of cancelled jobs by 4x and improving green energy usage efficiency by 3x over just utilizing the immediately available green energy.read more
Citations
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Proceedings ArticleDOI
GreenHadoop: leveraging green energy in data-processing frameworks
TL;DR: GreenHadoop is proposed, a MapReduce framework for a datacenter powered by a photovoltaic solar array and the electrical grid (as a backup) and can significantly increase green energy consumption and decrease electricity cost, compared to Hadoop.
Proceedings ArticleDOI
Parasol and GreenSwitch: managing datacenters powered by renewable energy
TL;DR: The tradeoffs involved in building green datacenters today and in the future are discussed, and GreenSwitch, a model-based approach for dynamically scheduling the workload and selecting the source of energy to use is described.
Proceedings ArticleDOI
Pricing data center demand response
TL;DR: This paper proposes that prediction-based pricing is an appealing market design, and shows that it outperforms more traditional supply function bidding mechanisms in situations where market power is an issue, and provides analytic, worst-case bounds on the impact of prediction error on the efficiency of prediction- based pricing.
Journal ArticleDOI
A taxonomy and survey on Green Data Center Networks
Kashif Bilal,Saif Ur Rehman Malik,Osman Khalid,Abdul Hameed,Enrique Alvarez,Vidura Wijaysekara,Rizwana Irfan,Sarjan Shrestha,Debjyoti Dwivedy,Mazhar Ali,Usman Shahid Khan,Assad Abbas,Nauman Jalil,Samee U. Khan +13 more
TL;DR: This survey presents significant insights to the state-of-the-art research conducted pertaining to the DCN domain along with a detailed discussion of the energy efficiency aspects of the DCNs.
Proceedings ArticleDOI
Opportunities and challenges for data center demand response
TL;DR: The potential and challenges in an emerging area of research that has the potential to significantly ease the incorporation of renewable energy into the grid as weil as electric power peak-load shaving are surveyed.
References
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MapReduce: simplified data processing on large clusters
Jeffrey Dean,Sanjay Ghemawat +1 more
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.
Journal ArticleDOI
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.
Proceedings ArticleDOI
Pregel: a system for large-scale graph processing
Grzegorz Malewicz,Matthew H. Austern,Aart J. C. Bik,James C. Dehnert,Ilan Horn,Naty Leiser,Grzegorz Czajkowski +6 more
TL;DR: A model for processing large graphs that has been designed for efficient, scalable and fault-tolerant implementation on clusters of thousands of commodity computers, and its implied synchronicity makes reasoning about programs easier.
Proceedings ArticleDOI
Dryad: distributed data-parallel programs from sequential building blocks
TL;DR: The Dryad execution engine handles all the difficult problems of creating a large distributed, concurrent application: scheduling the use of computers and their CPUs, recovering from communication or computer failures, and transporting data between vertices.
Book
The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines
Luiz Andre Barroso,Urs Hoelzle +1 more
TL;DR: The architecture of WSCs is described, the main factors influencing their design, operation, and cost structure, and the characteristics of their software base are described.