Resource Allocation in Cloud Computing Using Genetic Algorithm and Neural Network

TL;DR

This paper presents a hybrid genetic algorithm and neural network approach for resource allocation in cloud computing, improving scheduling efficiency, fairness, and reducing costs compared to existing methods.

Contribution

The paper introduces a novel hybrid algorithm combining neural network classification with genetic algorithm scheduling for cloud resource management.

Findings

Outperforms existing methods by 3.2% in execution time

Reduces costs by 13.3%

Improves response time by 12.1%

Abstract

Cloud computing is one of the most used distributed systems for data processing and data storage. Due to the continuous increase in the size of the data processed by cloud computing, scheduling multiple tasks to maintain efficiency while reducing idle becomes more and more challenging. Efficient cloud-based scheduling is also highly sought by modern transportation systems to improve their security. In this paper, we propose a hybrid algorithm that leverages genetic algorithms and neural networks to improve scheduling. Our method classifies tasks with the Neural Network Task Classification (N2TC) and sends the selected tasks to the Genetic Algorithm Task Assignment (GATA) to allocate resources. It is fairness aware to prevent starvation and considers the execution time, response time, cost, and system efficiency. Evaluations show that our approach outperforms the state-of-the-art method by 3.2% at execution time, 13.3% in costs, and 12.1% at response time.