Cloud-Computing-Based Resource Allocation Research on the Perspective of Improved Ant Colony Algorithm

2015 ◽  
Author(s):  
Weihua Hu ◽  
Ke Li ◽  
Junjun Xu ◽  
Qian Bao
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Improved Ant Colony Algorithm

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

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.

scholarly journals Application on the Problem of the Improved Ant Colony Algorithm on Cloud Computing Scheduling

2018 ◽  
Vol 11 (5) ◽  
pp. 79-90
Author(s):  
Zhi-hui Shang ◽  
Jian-wei Zhang ◽  
Xiao-hua Wang ◽  
Hong-jin Li ◽  
Xu Luo
Keyword(s):  
Cloud Computing ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Improved Ant Colony Algorithm

Improved Ant Colony Algorithm on Scheduling Optimization of Cloud Computing Resources

2014 ◽  
Vol 678 ◽  
pp. 75-78
Author(s):  
Xiao Xi Hu ◽  
Xian Wei Zhou
Keyword(s):  
Cloud Computing ◽  
Data Center ◽  
Ant Colony Algorithm ◽  
Response Speed ◽  
Ant Colony ◽  
Prediction Algorithm ◽  
Trend Prediction ◽  
Scheduling Optimization ◽  
Scheduling Optimization Algorithm ◽  
Improved Ant Colony Algorithm

To address the problem of high occupancy of resources and slow response of current scheduling of cloud computing resources, this paper proposes a scheduling optimization algorithm based improved ant colony algorithm. It makes resource reservation through migration of virtual machine, uses dynamic trend prediction algorithm to forecast the load changes of data center, and puts forward the concrete complement to adjust reduction. Experiments show that the combination algorithm proposed in this paper are efficient to improve the performance of data center, accelerate the response speed and increase the precision.

scholarly journals Design of Intelligent Building Scheduling System for Internet of Things and Cloud Computing

2021 ◽  
Vol 22 (2) ◽  
Author(s):  
Tiangang Wang ◽  
Zhe Mi
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Real Time ◽  
High Speed ◽  
Ant Colony Algorithm ◽  
Resource Scheduling ◽  
Ant Colony ◽  
Local Optimum ◽  
The Real ◽  
Improved Ant Colony Algorithm

The cloud computing (CC) and Internet of Things (IoT) are widely utilized and provided for intelligent perception and on-demand utilization like industries and public areas. The full sharing, free circulation and various manufacturing resources allocation are investigated in manufacturing. In order to ensure the real-time and effectiveness of resource storage scheduling in Internet of things information system, there are many kinds and quantities of building equipment. An improved ant colony algorithm is presented to remove the shortcomings of the existing ant colony algorithm with slow speed and fall into local optimum. The improved ant colony algorithm is transplanted into cloud computing environment. The advantages of fast computing and high speed storage of cloud computing can realize the real-time resource scheduling of building equipment. The experimental results present that the improved ant colony algorithm can obviously improve the efficiency of resource scheduling in cloud computing environment.All the experiments are performed on the MATLAB.

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