Load Balancing of Virtual Machines in Cloud Computing Environment Using Improved Ant Colony 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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Provisioning of Virtual Machines in the Context of an Auto-Scaling Cloud Computing Environment

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.8912 ◽

2020 ◽

Vol 17 (6) ◽

pp. 2430-2434

Author(s):

R. S. Rajput ◽

Dinesh Goyal ◽

Rashid Hussain ◽

Pratham Singh

Keyword(s):

Cloud Computing ◽

Load Balancing ◽

Resource Utilization ◽

Virtual Machines ◽

Cloud Environment ◽

Computing Environment ◽

Cloud Computing Environment ◽

Auto Scaling ◽

Adequate Resource ◽

Cloud Resources

The cloud computing environment is accomplishing cloud workload by distributing between several nodes or shift to the higher resource so that no computing resource will be overloaded. However, several techniques are used for the management of computing workload in the cloud environment, but still, it is an exciting domain of investigation and research. Control of the workload and scaling of cloud resources are some essential aspects of the cloud computing environment. A well-organized load balancing plan ensures adequate resource utilization. The auto-scaling is a technique to include or terminate additional computing resources based on the scaling policies without involving humans efforts. In the present paper, we developed a method for optimal use of cloud resources by the implementation of a modified auto-scaling feature. We also incorporated an auto-scaling controller for the optimal use of cloud resources.

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A PSO-Based Algorithm for Load Balancing in Virtual Machines of Cloud Computing Environment

Lecture Notes in Computer Science - Advances in Swarm Intelligence ◽

10.1007/978-3-642-30976-2_17 ◽

2012 ◽

pp. 142-147 ◽

Cited By ~ 26

Author(s):

Zhanghui Liu ◽

Xiaoli Wang

Keyword(s):

Cloud Computing ◽

Load Balancing ◽

Virtual Machines ◽

Computing Environment ◽

Cloud Computing Environment

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A Systematic Literature Review on Load Balancing Algorithms of Virtual Machines in a Cloud Computing Environment

SSRN Electronic Journal ◽

10.2139/ssrn.3564355 ◽

2020 ◽

Author(s):

Anit Kumar ◽

Priyanka Chawla

Keyword(s):

Cloud Computing ◽

Load Balancing ◽

Literature Review ◽

Systematic Literature Review ◽

Virtual Machines ◽

Computing Environment ◽

Cloud Computing Environment

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Sharing VM Resources With Using Prediction of Future User Requests for an Efficient Load Balancing in Cloud Computing Environment

International Journal of Software Science and Computational Intelligence ◽

10.4018/ijssci.2021040103 ◽

2021 ◽

Vol 13 (2) ◽

pp. 37-64

Author(s):

Mousa Elrotub ◽

Ahmed Bali ◽

Abdelouahed Gherbi

Keyword(s):

Cloud Computing ◽

Load Balancing ◽

Performance Metrics ◽

Virtual Machines ◽

Machine Learning Techniques ◽

Data Repository ◽

Computing Environment ◽

Cloud Computing Environment ◽

Learning Techniques

The problem of balancing user requests in cloud computing is becoming more serious due to the variation of workloads. Load balancing and allocation processes still need more optimizing methodologies and models to improve performance and increase the quality of service. This article describes a solution to balance user workload efficiently by proposing a model that allows each virtual machine (VM) to maximize the serving number of requests based on its capacity. The model measures VMs' capacity as a percentage and maps groups of user requests to appropriate active virtual machines. Finding the expected patterns from a big data repository, such as log data, and using some machine learning techniques can make the prediction more efficiently. The work is implemented and evaluated using some performance metrics, and the results are compared with other research. The evaluation shows the efficiency of the proposed approach in distributing user workload and improving results.

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