Construction of load balancing scheduling model for cloud computing task based on chaotic ant colony algorithm

In the context of abnormal network environment, cloud computing needs to rationally schedule resources in order to meet users’ needs. In this paper, an improved trust-driven load balancing scheduling model based on hybrid genetic ant colony is proposed to optimize resources allocation. Each subtask is assigned to a virtual resource. After the task is classified, the initial solution of the resource is calculated using genetic theory, and the optimal solution is obtained by using the ant colony theory, and the optimal resource node is acquired. The benefit function is utilized to calculate the trust requirements of the task for resources, and reasonable resources are obtained by mapping according to different trust values. The average trust benefit of the task on the resource pool is calculated, and the task-resource pairs larger than the average benefit are counted and filtered. According to the matching degree of benefit value of the resource and task, the task is scheduled to the resource with the lowest resource load, and the optimization of load balancing scheduling process is implemented. Experimental results show that using the improved model in this paper can achieve the purpose of resource load balancing.

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Optimized Resource Allocation Algorithm for Crowd-Creation Space Computing Based on Cloud Computing Environment

10.54216/jisiot.040101 ◽

2021 ◽

pp. 08-25

Author(s):

Mustafa El .. ◽

◽

Aaras Y Y.Kraidi

Keyword(s):

Resource Allocation ◽

Cloud Computing ◽

Simulated Annealing ◽

Task Allocation ◽

Ant Colony Algorithm ◽

Simulated Annealing Algorithm ◽

Ant Colony ◽

Computing Environment ◽

Cloud Computing Environment ◽

Resource Allocation Algorithm

The crowd-creation space is a manifestation of the development of innovation theory to a certain stage. With the creation of the crowd-creation space, the problem of optimizing the resource allocation of the crowd-creation space has become a research hotspot. The emergence of cloud computing provides a new idea for solving the problem of resource allocation. Common cloud computing resource allocation algorithms include genetic algorithms, simulated annealing algorithms, and ant colony algorithms. These algorithms have their obvious shortcomings, which are not conducive to solving the problem of optimal resource allocation for crowd-creation space computing. Based on this, this paper proposes an In the cloud computing environment, the algorithm for optimizing resource allocation for crowd-creation space computing adopts a combination of genetic algorithm and ant colony algorithm and optimizes it by citing some mechanisms of simulated annealing algorithm. The algorithm in this paper is an improved genetic ant colony algorithm (HGAACO). In this paper, the feasibility of the algorithm is verified through experiments. The experimental results show that with 20 tasks, the ant colony algorithm task allocation time is 93ms, the genetic ant colony algorithm time is 90ms, and the improved algorithm task allocation time proposed in this paper is 74ms, obviously superior. The algorithm proposed in this paper has a certain reference value for solving the creative space computing optimization resource allocation.

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An Ant-Colony-Based Meta-Heuristic Approach for Load Balancing in Cloud Computing

Research Anthology on Architectures, Frameworks, and Integration Strategies for Distributed and Cloud Computing ◽

10.4018/978-1-7998-5339-8.ch041 ◽

2021 ◽

pp. 873-903

Author(s):

Santanu Dam ◽

Gopa Mandal ◽

Kousik Dasgupta ◽

Parmartha Dutta

Keyword(s):

Cloud Computing ◽

Load Balancing ◽

Computational Intelligence ◽

Optimal Solution ◽

Ant Colony ◽

Multi Agent Systems ◽

Distributed Environment ◽

Intelligent Behavior ◽

Computational Intelligence Technique ◽

Multi Agent

This book chapter proposes use of Ant Colony Optimization (ACO), a novel computational intelligence technique for balancing loads of virtual machine in cloud computing. Computational intelligence(CI), includes study of designing bio-inspired artificial agents for finding out probable optimal solution. So the central goal of CI can be said as, basic understanding of the principal, which helps to mimic intelligent behavior from the nature for artifact systems. Basic strands of ACO is to design an intelligent multi-agent systems imputed by the collective behavior of ants. From the perspective of operation research, it's a meta-heuristic. Cloud computing is a one of the emerging technology. It's enables applications to run on virtualized resources over the distributed environment. Despite these still some problems need to be take care, which includes load balancing. The proposed algorithm tries to balance loads and optimize the response time by distributing dynamic workload in to the entire system evenly.

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