<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In recent days, resource allocation is considered to be a complex task in cloud systems. The heuristics models will allocate the resources efficiently in different machines. Then, the fitness function estimation plays a vital role in cloud load balancing, which is mainly used to minimize power consumption. The optimization technique is one of the most suitable options for solving load-balancing problems. This work mainly focuses on analyzing the impacts of using the Genetic Algorithm and Ant Colony Optimization (GAACO) technique for obtaining the optimal solution to efficiently balance the loads across the cloud systems. In addition to that, the GA and ACO are the kinds of object heuristic algorithms being proposed in the work to increase the number of servers that are operated with better energy efficiency. In this work, the main contribution of the GAACO algorithm is to reduce energy consumption, makespan time, response time, and degree of imbalance.</p>
</abstract>

In recent days, resource allocation is considered to be a complex task in cloud systems. The heuristics models will allocate the resources efficiently in different machines. Then, the fitness function estimation plays a vital role in cloud load balancing, which is mainly used to minimize power consumption. The optimization technique is one of the most suitable options for solving load-balancing problems. This work mainly focuses on analyzing the impacts of using the Genetic Algorithm and Ant Colony Optimization (GAACO) technique for obtaining the optimal solution to efficiently balance the loads across the cloud systems. In addition to that, the GA and ACO are the kinds of object heuristic algorithms being proposed in the work to increase the number of servers that are operated with better energy efficiency. In this work, the main contribution of the GAACO algorithm is to reduce energy consumption, makespan time, response time, and degree of imbalance.

An Energy-Aware QoS Load Balance Scheduling Using Hybrid GAACO Algorithm for Cloud

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

In recent days, resource allocation is considered to be a complex task in cloud systems. The heuristics models will allocate the resources efficiently in...

An Energy-Aware QoS Load Balance Scheduling Using Hybrid GAACO Algorithm for Cloud

Online veröffentlicht: 25. März 2023

Seitenbereich: 161 - 177

Eingereicht: 17. Okt. 2022

Akzeptiert: 11. März 2023

DOI: https://doi.org/10.2478/cait-2023-0009

Schlüsselwörter
Load balancing, energy consumption, Genetic Algorithm, Ant Colony Optimization Algorithm, cloud computing

© 2023 Arivumathi Ilankumaran et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

An Energy-Aware QoS Load Balance Scheduling Using Hybrid GAACO Algorithm for Cloud

Online veröffentlicht: 25. März 2023

Seitenbereich: 161 - 177

Eingereicht: 17. Okt. 2022

Akzeptiert: 11. März 2023

DOI: https://doi.org/10.2478/cait-2023-0009

SchlüsselwörterLoad balancing, energy consumption, Genetic Algorithm, Ant Colony Optimization Algorithm, cloud computing

© 2023 Arivumathi Ilankumaran et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Load balancing, energy consumption, Genetic Algorithm, Ant Colony Optimization Algorithm, cloud computing