Dynamic performance-Energy tradeoff consolidation with contention-aware resource provisioning in containerized clouds
Rewer M Canosa-Reyes1, Andrei Tchernykh1,2,3, Jorge M Cortés-Mendoza2
1Computer Science Department, CICESE Research Center, Ensenada, BC, Mexico.
Plos One
|January 20, 2022
Summary
New job allocation strategies optimize cloud container performance and energy efficiency. These lightweight runtime solutions minimize resource contention, improving quality of service and reducing completion times.
Area of Science:
- Computer Science
- Cloud Computing
- Resource Management
Background:
- Containers offer portable and efficient application deployment in cloud infrastructure.
- Effective resource allocation is critical for quality of service, performance, and energy efficiency.
- Inappropriate allocation leads to resource contention, impacting performance and energy usage.
Purpose of the Study:
- To develop online job allocation strategies for optimizing containerized cloud environments.
- To address contention for shared on-chip resources.
- To enhance quality of service, energy savings, and job completion time.
Main Methods:
- Framing job allocation as a multilevel dynamic bin-packing problem.
- Implementing two and three-level scheduling policies: container selection, capacity distribution, and contention-aware allocation.
- Utilizing an energy model considering joint application execution and the job concentration paradigm.
Main Results:
- Experimental analysis of 86 scheduling heuristics with memory and CPU-intensive workloads.
- Proposed strategies significantly outperform classical solutions.
- Achieved improvements: 21.73-43.44% in quality of service, 44.06-92.11% in energy savings, and 16.38-24.17% in completion time.
Conclusions:
- The proposed job allocation strategies offer a lightweight, runtime solution for cloud infrastructures.
- These strategies effectively minimize contention and energy consumption while maximizing resource utilization.
- Results demonstrate significant cost-efficiency and performance gains for cloud resource allocation.
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