Hybrid Bat-Genetic metaheuristic for energy-aware workflow scheduling in cloud datacenters (tri-objective optimization: energy, throughput, execution time).
https://doi.org/10.1016/j.future.2024.107624
Published in: Future Generation Computer Systems (Elsevier)
Cloud datacenters currently account for nearly 1% of global electricity consumption and approximately 2% of worldwide carbon emissions.
As scientific and enterprise workflows grow in scale and dependency, optimizing energy consumption without sacrificing throughput or execution time becomes a critical challenge.
MHSC introduces a sensitivity-driven hybrid metaheuristic framework that merges Genetic Algorithm (GA) and Bat Algorithm to achieve tri-objective optimization in cloud workflow scheduling.
The method simultaneously optimizes:
- Energy Consumption
- Execution Time
- Throughput
Cloud workflow scheduling is:
- NP-Complete
- Multi-objective
- Energy-sensitive
- Dependency-constrained
Traditional schedulers optimize one parameter at the expense of others.
Energy ↓ often means Execution Time ↑
Throughput ↑ often means Energy ↑
A balanced solution requires intelligent global search beyond single-objective heuristics.
MHSC integrates:
- Hybrid Bat + Genetic search
- Sensitivity rate computing
- Linear programming for inertia generation
- Intelligent threshold detection
- Workflow input clustering
- Deadline-aware task mapping
- Neighborhood-based quality link prediction
The hybridization enables:
- Faster escape from local optima
- Reduced iteration counts
- Improved convergence speed
- Balanced tri-objective trade-off
Compared to baseline approaches:
- Energy consumption improved by 6.4%
- Execution time reduced by 8.1%
- Throughput increased under high node dependency
- Better scalability as workflow size grows
Performance advantage increases as task dependencies increase.
The MHSC pipeline:
- Input classification based on deadline constraints
- Hybrid Genetic distribution of workflow tasks
- Bat-based inertia and exploration control
- Sensitivity-driven threshold adaptation
- Multi-objective fitness evaluation
Objective Function balances:
- Energy Model
- Execution Time Model
- Throughput Maximization
Green computing is no longer optional.
Datacenters face:
- Energy cost escalation
- Carbon regulation pressure
- SLA constraints
- Resource scalability demands
MHSC provides a sustainable scheduling framework for:
- Scientific workflows (astronomy, bioinformatics, physics)
- Enterprise cloud systems
- DevOps pipelines
- HPC cloud platforms
Cloud Computing • Workflow Scheduling • Metaheuristic • Bat Algorithm • Genetic Algorithm • Multi-objective Optimization • Green Computing • Energy-aware Systems • Sustainable Datacenters • High Performance Computing
Title: MHSC: A meta-heuristic method to optimize throughput and energy using sensitivity rate computing
Authors:
A. Ghorbannia Delavar
R. Akraminejad
F. Kazemipour
Journal: Future Generation Computer Systems
DOI: 10.1016/j.future.2024.107624
Ghorbannia Delavar, A., Akraminejad, R., & Kazemipour, F. (2025). MHSC: A meta-heuristic method to optimize throughput and energy using sensitivity rate computing. Future Generation Computer Systems. https://doi.org/10.1016/j.future.2024.107624
@article{MHSC2025, title={MHSC: A meta-heuristic method to optimize throughput and energy using sensitivity rate computing}, author={Ghorbannia Delavar, Arash and Akraminejad, Reza and Kazemipour, Farhad}, journal={Future Generation Computer Systems}, year={2025}, doi={10.1016/j.future.2024.107624} }
Department of Computer Engineering
Payame Noor University, Tehran, Iran
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