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Deep learning and optimization enabled multi-objective for task scheduling in cloud computing.

Dinesh Komarasamy¹, Siva Malar Ramaganthan², Dharani Molapalayam Kandaswamy³

¹Department of Computer Science and Engineering, Kongu Engineering College, Erode, India.

Network (Bristol, England)

|August 20, 2024

Summary

This study introduces a novel cloud computing task scheduling model using hybrid fractional flamingo beetle optimization (FFBO) and deep learning (DL) for enhanced efficiency. The FFBO-DL model optimizes task allocation based on reliability, cost, energy, and makespan, achieving superior performance.

Keywords:

Task scheduling cloud computing (CC)deep learning (DL)dung beetle optimization (DBO)

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Area of Science:

Cloud Computing
Artificial Intelligence
Optimization Algorithms

Background:

Task scheduling in cloud computing is crucial for efficient resource allocation.
Existing methods often struggle to balance multiple objectives like cost, energy, and performance.
Deep learning and advanced optimization techniques offer potential for improved scheduling.

Purpose of the Study:

To propose a novel hybrid model for multi-objective task scheduling in cloud computing.
To integrate deep learning for energy prediction and optimization algorithms for resource allocation.
To enhance overall system efficiency by considering task and virtual machine parameters.

Main Methods:

A hybrid fractional flamingo beetle optimization (FFBO) algorithm, combining dung beetle optimization (DBO), flamingo search algorithm (FSA), and fractional calculus (FC).
Deep Residual Network (DRN) for predicting energy consumption.
Deep Feedforward Neural Network fused Long Short-Term Memory (DFNN-LSTM) for task scheduling.
Consideration of task parameters (EFT, EST, task length, priority, running time) and VM parameters (CPU, memory, bandwidth, capacity).

Main Results:

The proposed DFNN-LSTM+FFBO model demonstrated superior performance in key metrics.
Achieved makespan of 0.188, energy consumption of 0.950J, and resource utilization of 0.238.
Indicates significant improvements over existing task scheduling approaches.

Conclusions:

The integrated DFNN-LSTM+FFBO model effectively addresses multi-objective task scheduling in cloud computing.
The hybrid approach shows promise for optimizing cloud resource management.
Further research can explore scalability and real-world deployment.