scholarly journals An Adaptive Load Balancing Queue Based Resource Allocation Algorithm in Cloud Computing Environment

2017 ◽  
Vol 01 (02) ◽  
pp. 48-59
Author(s):  
jyoti patharia
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Load Balancing ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Resource Allocation Algorithm ◽  
Allocation Algorithm

An Optimal Resource Allocation Algorithm in Cloud Computing Environment

2015 ◽  
Vol 733 ◽  
pp. 779-783 ◽  
Author(s):  
Lu Dai ◽  
Jian Hua Li
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Optimization ◽  
Scientific Basis ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Resource Allocation Algorithm ◽  
Allocation Algorithm ◽  
Optimal Resource ◽  
And Performance

Resource allocation is a key technology of cloud computing. At present, the most of studies on resource allocation mainly focus on improving the overall performance by balancing the load of data center. This paper will design the experimental platform of resource allocation algorithm, energy optimization and performance analysis, obtain original achievements in scientific research ,for the resource allocation method based on immune algorithm and energy optimization in cloud computing to provide innovative ideas and scientific basis. This research has important significance for further study on resource allocation and energy optimization in cloud computing environment.

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.

Resource scheduling strategy for uncertain factors in mobile cloud computing

2021 ◽  
pp. 1-6
Author(s):  
Ying Chen
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Mobile Cloud Computing ◽  
Mobile Cloud ◽  
Computing Environment ◽  
Mobile Terminals ◽  
Cloud Computing Environment ◽  
Resource Allocation Algorithm ◽  
Cloud Resource ◽  
Resource Environment

At present, resource configuration of mobile cloud computing has received extensive attention from the outside world. Most of the similar resource scheduling configuration fails to comprehensively consider the dynamics of mobile terminals and the difference in user requested resources. Therefore, considering uncertainty in paging scheduling under mobile cloud resource environment from the perspective of consumers has become the key to solving the problem of resource allocation in the mobile cloud computing environment. This paper proposes an adaptive matching resource allocation algorithm based on uncertain factors under mobile cloud computing environment. Uncertain factors of the mobile terminal are derived via QoS attribute, and then user information and load characteristics of the user requested resources are analyzed through CLIQUE similarity matching. Afterwards, based on the mapping between similarity and resources, resource paging allocation can be carried out based on adaptive matching resource allocation algorithm. From the perspective of consumers, dynamics of mobile terminals and uncertainty of paging scheduling in the mobile cloud resource environment under different user requested resources can be considered to allow minimized delay and optimized paging strategies.

A Computing Capability Allocation Algorithm for Cloud Computing Environment

2013 ◽  
Vol 347-350 ◽  
pp. 2400-2406 ◽  
Author(s):  
Wen Xin Hu ◽  
Jun Zheng ◽  
Xia Yu Hua ◽  
Ya Qian Yang
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Response Time ◽  
Response Times ◽  
Cloud Environment ◽  
Computing Environment ◽  
Grid Environment ◽  
Cloud Computing Environment ◽  
Allocation Algorithm ◽  
Computing Infrastructure

For several special features in the environment of cloud computing, which may be quite different from the centralized computing infrastructure currently available, the existed method of resource allocation used in the grid computing environment may not be suitable for these changes. In our paper, a new allocation algorithm based on Ant Colony Optimization (ACO) is proposed to satisfy the needs of Infrastructure as a Service (IaaS) supported by the cloud computing environment. When started, this algorithm first predicts the capability of the potentially available resource nodes; then, it analyzes some factors such as network qualities and response times to acquire a set of optimal compute nodes; finally, the tasks would be allocated to these suitable nodes. This algorithm has shorter response time and better performance than some of other algorithms which are based on Grid environment when running in the simulate cloud environment. This result is verified by the simulation in the Gridsim environment elaborated in the following section.

Sign in / Sign up

Export Citation Format

Share Document