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Related Experiment Videos

Energy-aware priority-based task scheduling in cloud data centers using bacterial foraging optimization.

Farzad Hosseinlou¹, Ali Ghaffari^2,3,4, Abbas Mirzaei⁵

¹Department of Computer Engineering, Ta.C., Islamic Azad University, Tabriz, Iran. farzad.hosseinloo@iau.ac.ir.

Scientific Reports

|May 26, 2026

Summary

This summary is machine-generated.

This study introduces a novel Hyper-Heuristic Bacterial Foraging Optimization (BFO-HH) algorithm for energy-efficient task scheduling in cloud data centers. BFO-HH significantly reduces energy consumption, operational costs, and improves Quality of Service (QoS).

Keywords:

Bacterial foraging optimization Cloud computing Energy efficiency Hyper-heuristic Operational cost Task scheduling

Related Experiment Videos

Area of Science:

Cloud Computing
Data Center Energy Efficiency
Algorithm Optimization

Background:

Cloud data centers face escalating energy consumption challenges.
Efficient task scheduling is crucial for minimizing operational costs and environmental impact.
Existing algorithms struggle to balance energy efficiency with Quality of Service (QoS).

Purpose of the Study:

To develop an energy-efficient, priority-aware task scheduling algorithm for cloud data centers.
To minimize operational costs, reduce energy consumption, and enhance QoS.
To introduce a dynamic heuristic selection and combination approach.

Main Methods:

Implementation of the Hyper-Heuristic Bacterial Foraging Optimization (BFO-HH) algorithm.
Dynamic selection and combination of four low-level heuristics: Task Selection, Virtual Machine Migration, Load Balancing, and Resource Consolidation.
Experiments conducted using CloudSim 3.0.3 with heterogeneous synthetic workloads (20-200 cloudlets).

Main Results:

BFO-HH demonstrated superior performance compared to Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Artificial Bee Colony (ABC).
For 200 tasks, BFO-HH achieved 9.9% less energy consumption, 9.3% shorter makespan, 28% fewer SLA violations, 7% higher resource utilization, and 14% lower operational costs.
Improvements were statistically significant, supported by standard deviations and a 95% confidence interval.

Conclusions:

The proposed BFO-HH algorithm offers a significant advancement in energy-efficient task scheduling for cloud environments.
BFO-HH effectively balances energy savings, cost reduction, and QoS improvement.
The dynamic heuristic combination strategy enhances the algorithm's adaptability and performance across various workloads.