Containergy—A Container-Based Energy and Performance Profiling Tool for Next Generation Workloads
Run-time profiling of software applications is key to energy efficiency. Even the most optimized hardware combined to an optimally designed software may become inefficient if operated poorly. Moreover, the diversification of modern computing platforms and broadening of their run-time configuration s...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-05-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/9/2162 |
| _version_ | 1827717648541024256 |
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| author | Wellington Silva-de-Souza Arman Iranfar Anderson Bráulio Marina Zapater Samuel Xavier-de-Souza Katzalin Olcoz David Atienza |
| author_facet | Wellington Silva-de-Souza Arman Iranfar Anderson Bráulio Marina Zapater Samuel Xavier-de-Souza Katzalin Olcoz David Atienza |
| author_sort | Wellington Silva-de-Souza |
| collection | DOAJ |
| description | Run-time profiling of software applications is key to energy efficiency. Even the most optimized hardware combined to an optimally designed software may become inefficient if operated poorly. Moreover, the diversification of modern computing platforms and broadening of their run-time configuration space make the task of optimally operating software ever more complex. With the growing financial and environmental impact of data center operation and cloud-based applications, optimal software operation becomes increasingly more relevant to existing and next-generation workloads. In order to guide software operation towards energy savings, energy and performance data must be gathered to provide a meaningful assessment of the application behavior under different system configurations, which is not appropriately addressed in existing tools. In this work we present Containergy, a new performance evaluation and profiling tool that uses software containers to perform application run-time assessment, providing energy and performance profiling data with negligible overhead (below 2%). It is focused on energy efficiency for next generation workloads. Practical experiments with emerging workloads, such as video transcoding and machine-learning image classification, are presented. The profiling results are analyzed in terms of performance and energy savings under a Quality-of-Service (QoS) perspective. For video transcoding, we verified that wrong choices in the configuration space can lead to an increase above 300% in energy consumption for the same task and operational levels. Considering the image classification case study, the results show that the choice of the machine-learning algorithm and model affect significantly the energy efficiency. Profiling datasets of AlexNet and SqueezeNet, which present similar accuracy, indicate that the latter represents 55.8% in energy saving compared to the former. |
| first_indexed | 2024-03-10T20:06:25Z |
| format | Article |
| id | doaj.art-4fa4275d74ab4d45b032f16676726cd9 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T20:06:25Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-4fa4275d74ab4d45b032f16676726cd92023-11-19T23:14:12ZengMDPI AGEnergies1996-10732020-05-01139216210.3390/en13092162Containergy—A Container-Based Energy and Performance Profiling Tool for Next Generation WorkloadsWellington Silva-de-Souza0Arman Iranfar1Anderson Bráulio2Marina Zapater3Samuel Xavier-de-Souza4Katzalin Olcoz5David Atienza6Department of Computer Engineering and Automation, Universidade Federal do Rio Grande do Norte, Natal 59078-970, BrazilSwiss Federal Institute of Technology Lausanne, 1015 Lausanne, SwitzerlandInstituto Federal da Paraíba, João Pessoa 58015-020, BrazilSwiss Federal Institute of Technology Lausanne, 1015 Lausanne, SwitzerlandDepartment of Computer Engineering and Automation, Universidade Federal do Rio Grande do Norte, Natal 59078-970, BrazilDepartment of Computer Architecture and Automation, Universidad Complutense de Madrid, 28040 Madrid, SpainSwiss Federal Institute of Technology Lausanne, 1015 Lausanne, SwitzerlandRun-time profiling of software applications is key to energy efficiency. Even the most optimized hardware combined to an optimally designed software may become inefficient if operated poorly. Moreover, the diversification of modern computing platforms and broadening of their run-time configuration space make the task of optimally operating software ever more complex. With the growing financial and environmental impact of data center operation and cloud-based applications, optimal software operation becomes increasingly more relevant to existing and next-generation workloads. In order to guide software operation towards energy savings, energy and performance data must be gathered to provide a meaningful assessment of the application behavior under different system configurations, which is not appropriately addressed in existing tools. In this work we present Containergy, a new performance evaluation and profiling tool that uses software containers to perform application run-time assessment, providing energy and performance profiling data with negligible overhead (below 2%). It is focused on energy efficiency for next generation workloads. Practical experiments with emerging workloads, such as video transcoding and machine-learning image classification, are presented. The profiling results are analyzed in terms of performance and energy savings under a Quality-of-Service (QoS) perspective. For video transcoding, we verified that wrong choices in the configuration space can lead to an increase above 300% in energy consumption for the same task and operational levels. Considering the image classification case study, the results show that the choice of the machine-learning algorithm and model affect significantly the energy efficiency. Profiling datasets of AlexNet and SqueezeNet, which present similar accuracy, indicate that the latter represents 55.8% in energy saving compared to the former.https://www.mdpi.com/1996-1073/13/9/2162performance profilingenergy profilingsoftware containersperformance countersDVFS |
| spellingShingle | Wellington Silva-de-Souza Arman Iranfar Anderson Bráulio Marina Zapater Samuel Xavier-de-Souza Katzalin Olcoz David Atienza Containergy—A Container-Based Energy and Performance Profiling Tool for Next Generation Workloads Energies performance profiling energy profiling software containers performance counters DVFS |
| title | Containergy—A Container-Based Energy and Performance Profiling Tool for Next Generation Workloads |
| title_full | Containergy—A Container-Based Energy and Performance Profiling Tool for Next Generation Workloads |
| title_fullStr | Containergy—A Container-Based Energy and Performance Profiling Tool for Next Generation Workloads |
| title_full_unstemmed | Containergy—A Container-Based Energy and Performance Profiling Tool for Next Generation Workloads |
| title_short | Containergy—A Container-Based Energy and Performance Profiling Tool for Next Generation Workloads |
| title_sort | containergy a container based energy and performance profiling tool for next generation workloads |
| topic | performance profiling energy profiling software containers performance counters DVFS |
| url | https://www.mdpi.com/1996-1073/13/9/2162 |
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