How does Docker affect energy consumption? Evaluating workloads in and out of Docker containers
May 02, 2017 Β· Declared Dead Β· π Journal of Systems and Software
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Eddie Antonio Santos, Carson McLean, Christopher Solinas, Abram Hindle
arXiv ID
1705.01176
Category
cs.DC: Distributed Computing
Cross-listed
cs.PF
Citations
34
Venue
Journal of Systems and Software
Last Checked
6 months ago
Abstract
Context: Virtual machines provide isolation of services at the cost of hypervisors and more resource usage. This spurred the growth of systems like Docker that enable single hosts to isolate several applications, similar to VMs, within a low-overhead abstraction called containers. Motivation: Although containers tout low overhead performance, do they still have low energy consumption? Methodology: This work statistically compares ($t$-test, Wilcoxon) the energy consumption of three application workloads in Docker and on bare-metal Linux. Results: In all cases, there was a statistically significant ($t$-test and Wilcoxon $p < 0.05$) increase in energy consumption when running tests in Docker, mostly due to the performance of I/O system calls.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Distributed Computing
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Reproducing GW150914: the first observation of gravitational waves from a binary black hole merger
R.I.P.
π»
Ghosted
MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
R.I.P.
π»
Ghosted
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
R.I.P.
π»
Ghosted
Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing
R.I.P.
π»
Ghosted
iFogSim: A Toolkit for Modeling and Simulation of Resource Management Techniques in Internet of Things, Edge and Fog Computing Environments
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted