Journal article
Alessandro Carrega, Giancarlo Portomauro, Matteo Repetto, Giorgio Robino
IEEE, Cloud Computing, vol. 5(6), 2018, pp. 38-47
IEEE, Cloud Computing, vol. 5(6), 2018, pp. 38-47
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APA
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Carrega, A., Portomauro, G., Repetto, M., & Robino, G. (2018). Boosting Energy Efficiency and Quality of Service through Orchestration Tools. Cloud Computing, 5(6), 38–47. https://doi.org/10.1109/MCC.2018.064181119
Chicago/Turabian
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Carrega, Alessandro, Giancarlo Portomauro, Matteo Repetto, and Giorgio Robino. “Boosting Energy Efficiency and Quality of Service through Orchestration Tools.” Edited by IEEE. Cloud Computing 5, no. 6 (2018): 38–47.
MLA
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Carrega, Alessandro, et al. “Boosting Energy Efficiency and Quality of Service through Orchestration Tools.” Cloud Computing, edited by IEEE, vol. 5, no. 6, 2018, pp. 38–47, doi:10.1109/MCC.2018.064181119.
BibTeX Click to copy
@article{carrega2018a,
title = {Boosting Energy Efficiency and Quality of Service through Orchestration Tools},
year = {2018},
issue = {6},
journal = {Cloud Computing},
pages = {38-47},
volume = {5},
doi = {10.1109/MCC.2018.064181119},
author = {Carrega, Alessandro and Portomauro, Giancarlo and Repetto, Matteo and Robino, Giorgio},
editor = {IEEE}
}
🔬 Boosting Energy Efficiency and Quality of Service through Cloud Orchestration
Modern cloud data centers face a fundamental engineering challenge: reducing infrastructure energy consumption while maintaining the performance, availability, and responsiveness required by increasingly demanding applications.
The research article Boosting Energy Efficiency and Quality of Service through Orchestration Tools examines the potential of orchestration-oriented approaches to address this challenge.
The central concept is to move beyond static infrastructure management toward coordinated, application-aware resource optimization, considering energy efficiency and Quality of Service (QoS) as interdependent objectives.
The infographic illustrates several architectural mechanisms relevant to this approach:
🔹 Resource Orchestration: Coordinating application requirements with the allocation and management of computing resources.
🔹 Workload Consolidation: Grouping workloads to improve infrastructure utilization and reduce unnecessary resource activity.
🔹 Programmable Networking: Using software-controlled networking mechanisms to support flexible infrastructure configuration and traffic management.
🔹 Power-Aware Infrastructure Management: Incorporating energy-related information into operational decisions involving servers and networking equipment.
🔹 QoS-Aware Optimization: Considering performance, availability, and service responsiveness when evaluating energy-saving strategies.
📊 Key engineering considerations
✅ Energy efficiency versus QoS: Lower resource consumption must be evaluated against possible impacts on service quality.
✅ Infrastructure utilization: Workload consolidation offers opportunities to reduce the number of unnecessarily active resources.
✅ Orchestration flexibility: Application-aware management can support adaptation to changing computing demands.
✅ Integrated resource control: Coordinating computing, networking, and power management is a promising direction for improving infrastructure efficiency.
✅ Operational trade-offs: Consolidation, resource activation, and workload migration may introduce performance overhead that must be considered.
🎯 Central research theme: Orchestration tools can provide a foundation for balancing energy efficiency and service quality in programmable cloud data center infrastructures.
Scientific clarification: These architectural mechanisms are an explanatory interpretation of the research theme. The available PDF contains only the first page of the article, so its full methodology and experimental findings could not be verified. The numerical values and charts shown in the technical infographic should be treated as illustrative, not as measurements established by this publication.
🌱⚡ Can Cloud Data Centers Become Greener Without Sacrificing Performance? ☁️🚀
Imagine a cloud infrastructure where computing resources intelligently adapt to application demands, reducing unnecessary energy consumption while ensuring that users continue receiving high-quality services.
Traditional cloud data centers often rely on overprovisioning to guarantee service availability and performance. However, keeping servers and virtual machines active even when they are barely used can result in considerable energy waste.
Our research explores a smarter paradigm: using orchestration tools to improve energy efficiency while preserving Quality of Service (QoS).
The idea is to make cloud infrastructures more aware of application needs and resource utilization, allowing management systems to coordinate computing resources more efficiently.
⚙️ Intelligent orchestration: Coordinate resource allocation according to application requirements.
☁️ Workload consolidation: Reduce unnecessary infrastructure activity by grouping workloads where appropriate.
🌱 Energy-aware management: Exploit power-saving opportunities without overlooking service requirements.
🛡️ Quality of Service: Consider availability, responsiveness, and performance alongside energy consumption.
The manga-inspired infographic illustrates the central challenge: finding the right balance between a sustainable cloud infrastructure and the service quality users expect.
🎯 The vision: Smarter orchestration can help transform cloud data centers into more adaptive, energy-conscious computing environments.
