TPMCD: Hybrid Container–VM Optimization Framework for Cost and Throughput in Cloud Datacenters (2025)
Hybrid container–VM scheduling framework for cost, throughput and execution-time optimization in cloud datacenters (TPMCD, 2025).
https://doi.org/10.1016/j.jnca.2025.104132
Published in: Journal of Network and Computer Applications (Elsevier)
Modern cloud datacenters operate under extreme pressure:
- Increasing workload volumes
- Multi-tenant competition
- SLA constraints
- Rising energy consumption
- Escalating infrastructure costs
Containers offer lower overhead than virtual machines, but improper configuration, scaling, and scheduling can still lead to significant waste, redundancy, and energy inefficiency.
TPMCD introduces a sensitivity-driven hybrid container–VM scheduling and clustering framework that simultaneously optimizes:
• Cost
• Throughput
• Real Execution Time
• Number of Active Nodes
The method integrates clustering, intelligent threshold detection, scoring mechanisms, and hybrid configuration of containers and virtual machines.
Cloud scheduling is inherently:
- NP-Complete
- Multi-objective
- Dynamic under fluctuating workloads
- SLA-constrained
- Resource-competitive
Challenges include:
• Overloaded hosts
• Underutilized containers
• Excess VM provisioning
• Load imbalance
• Data redundancy
• Increased carbon footprint
Traditional methods treat:
- Containers and VMs independently
- Size-based classification only
- Static thresholding
- Single-objective optimization
TPMCD breaks that limitation.
TPMCD (ThroughPut and Cost Optimizing Method for Clustering Tasks and Hybrid Containers in Cloud Data Centers) introduces:
Tasks are classified into low, medium, and high-load categories based on dynamic sensitivity metrics.
Simultaneous optimization of:
- Container allocation
- Virtual machine assignment
- Resource scoring
- Communication efficiency
Dynamic threshold adjustment prevents:
- Overloading
- Underloading
- Critical imbalance points
Each task is assigned based on:
- Weight
- Size
- Sensitivity
- Execution priority
- Confidence interval estimation
Reduces redundancy and improves real execution time.
TPMCD integrates:
- Linear programming models
- Sensitivity-based VM range mapping
- Underload / overload detection formulas
- Resource free-capacity estimation
- Multi-objective cost functions
Objective Function simultaneously balances:
Minimize:
- Total Cost
- Real Execution Time
- Node Utilization Overhead
Maximize:
- Throughput
- SLA Compliance
Simulation Environment:
- Python 3.6
- CloudSim 6.0
Compared to baseline scheduling methods:
• 7% average cost reduction
• 4% throughput improvement
• 9.5% real execution time reduction
• 3% fewer active nodes compared to KC method
Performance advantage increases under:
- Dynamic workloads
- High task concurrency
- Multi-tenant cloud scenarios
Data centers significantly contribute to global energy consumption and greenhouse emissions.
TPMCD reduces:
- Redundant containers
- Excess VM rental
- Resource wastage
- Energy consumption
Hybrid container–VM regulation provides:
- Better elasticity
- Controlled autoscaling
- Reduced idle overhead
- Kubernetes scheduling optimization
- Container orchestration systems
- Hybrid cloud infrastructures
- Enterprise SLA-based deployments
- Scientific workflow processing
- DevOps batch scheduling
- Green cloud migration strategies
Pipeline:
- Task classification via sensitivity rate
- Container assignment
- VM mapping within sensitivity ranges
- Threshold-based load adjustment
- Re-clustering for optimization
- Confidence interval validation
Cloud-native computing is container-driven.
Without intelligent scheduling:
- Cloud waste increases
- Carbon cost escalates
- SLA violations grow
- Infrastructure ROI decreases
TPMCD offers a scalable, hybrid, mathematically grounded framework for sustainable and cost-efficient cloud operation.
Title: TPMCD: A method to optimizing cost and throughput for clustering tasks and hybrid containers in the cloud data center
Author: A. Ghorbannia Delavar
Journal: Journal of Network and Computer Applications
DOI: 10.1016/j.jnca.2025.104132
Year: 2025
Ghorbannia Delavar, A. (2025). TPMCD: A method to optimizing cost and throughput for clustering tasks and hybrid containers in the cloud data center. Journal of Network and Computer Applications. https://doi.org/10.1016/j.jnca.2025.104132
@article{TPMCD2025, title={TPMCD: A method to optimizing cost and throughput for clustering tasks and hybrid containers in the cloud data center}, author={Ghorbannia Delavar, Arash}, journal={Journal of Network and Computer Applications}, year={2025}, doi={10.1016/j.jnca.2025.104132} }
Department of Computer Science
Payame Noor University, Tehran, Iran
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• Cloud Computing
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• Sustainable Datacenters
• Container Optimization
• Multi-objective Resource Allocation