Studying the Energy Consumption of Stream ...
Document type :
Communication dans un congrès avec actes
Title :
Studying the Energy Consumption of Stream Processing Engines in the Cloud
Author(s) :
KP, Govind [Auteur]
Design and Implementation of Autonomous Distributed Systems [MYRIADS]
Pierre, Guillaume [Auteur]
Design and Implementation of Autonomous Distributed Systems [MYRIADS]
Rouvoy, Romain [Auteur]
Institut universitaire de France [IUF]
Self-adaptation for distributed services and large software systems [SPIRALS]
Design and Implementation of Autonomous Distributed Systems [MYRIADS]
Pierre, Guillaume [Auteur]
Design and Implementation of Autonomous Distributed Systems [MYRIADS]
Rouvoy, Romain [Auteur]

Institut universitaire de France [IUF]
Self-adaptation for distributed services and large software systems [SPIRALS]
Conference title :
IC2E 2023 - 11th IEEE International Conference on Cloud Engineering
Conference organizers(s) :
IEEE
City :
Boston (MA)
Country :
Etats-Unis d'Amérique
Start date of the conference :
2023-09-25
Publisher :
IEEE
English keyword(s) :
Green computing
Data stream processing
Energy consumption
Reproducibility
Data stream processing
Energy consumption
Reproducibility
HAL domain(s) :
Informatique [cs]
English abstract : [en]
Reducing the energy consumption of the global IT industry requires one to understand and optimize the large software infrastructures the modern data economy relies on. Among them are the data stream processing systems that ...
Show more >Reducing the energy consumption of the global IT industry requires one to understand and optimize the large software infrastructures the modern data economy relies on. Among them are the data stream processing systems that are deployed in cloud data centers by companies, such as Twitter, to process billion of events per day in real time. However, studying the energy consumption of such infrastructures is difficult because they rely on a complex virtualized software ecosystem where attributing energy consumption to individual software components is a challenge, and because the space of possible configurations is large. We present GreenFlow, a principled methodology and tool designed to automate the deployment of energy measurement experiments for data stream processing systems in cloud environments. GreenFlow is designed to deliver reproducible results while remaining flexible enough to support a wide range of experiments. We illustrate its usage and show in particular that consolidating a DSP system in the smallest number of servers that are capable of processing it is an effective way to reduce energy consumption.Show less >
Show more >Reducing the energy consumption of the global IT industry requires one to understand and optimize the large software infrastructures the modern data economy relies on. Among them are the data stream processing systems that are deployed in cloud data centers by companies, such as Twitter, to process billion of events per day in real time. However, studying the energy consumption of such infrastructures is difficult because they rely on a complex virtualized software ecosystem where attributing energy consumption to individual software components is a challenge, and because the space of possible configurations is large. We present GreenFlow, a principled methodology and tool designed to automate the deployment of energy measurement experiments for data stream processing systems in cloud environments. GreenFlow is designed to deliver reproducible results while remaining flexible enough to support a wide range of experiments. We illustrate its usage and show in particular that consolidating a DSP system in the smallest number of servers that are capable of processing it is an effective way to reduce energy consumption.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
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