Resource Allocation in Cloud using Multi Bidding Model with User Centric Behavior Analysis

2020 ◽  
Vol 13 (5) ◽  
pp. 1008-1019
Author(s):  
N. Vijayaraj ◽  
T. Senthil Murugan
Keyword(s):  
Resource Allocation ◽  
Behavior Analysis ◽  
User Behavior ◽  
Low Cost ◽  
Cloud Services ◽  
Online Auction ◽  
Analysis Model ◽  
Efficient Resource ◽  
User Centric ◽  
Cloud Resources

Background: Number of resource allocation and bidding schemes had been enormously arrived for on demand supply scheme of cloud services. But accessing and presenting the Cloud services depending on the reputation would not produce fair result in cloud computing. Since the cloud users not only looking for the efficient services but in major they look towards the cost. So here there is a way of introducing the bidding option system that includes efficient user centric behavior analysis model to render the cloud services and resource allocation with low cost. Objective: The allocation of resources is not flexible and dynamic for the users in the recent days. This gave me the key idea and generated as a problem statement for my proposed work. Methods: An online auction framework that ensures multi bidding mechanism which utilizes user centric behavioral analysis to produce the efficient and reliable usage of cloud resources according to the user choice. Results: we implement Efficient Resource Allocation using Multi Bidding Model with User Centric Behavior Analysis. Thus the algorithm is implemented and system is designed in such a way to provide better allocation of cloud resources which ensures bidding and user behavior. Conclusion: Thus the algorithm Efficient Resource Allocation using Multi Bidding Model with User Centric Behavior Analysis is implemented & system is designed in such a way to provide better allocation of cloud resources which ensures bidding, user behavior. The user bid data is trained accordingly such that to produce efficient resource utilization. Further the work can be taken towards data analytics and prediction of user behavior while allocating the cloud resources.

scholarly journals Minimizing the Cost for Resource Allocation from Multiple Cloud Providers

2021 ◽  
Vol 9 (11) ◽  
pp. 1611-1618
Author(s):  
Manasa Jonnagadla
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Service Providers ◽  
Low Cost ◽  
Hybrid Approach ◽  
Cloud Service ◽  
Cloud Services ◽  
Business Efficiency ◽  
Demand Variability ◽  
Cloud Resources

Abstract: Cloud computing provides streamlined tools for exceptional business efficiency. Cloud service providers typically offer two types of plans: reserved and on-demand. Restricted policies provide low-cost long-term contracting, while order contracts are expensive and ready for short periods. Cloud resources must be delivered wisely to meet current customer demands. Many current works rely on low-cost resource-reserved strategies, which may be under- or over-provisioning. Resource allocation has become a difficult issue due to unfairness causing high availability costs and cloud demand variability. That article suggests a hybrid approach to allocating cloud services to complex customer orders. The strategy was built in two stages: accommodation stages and a flexible structure. By treating each step as an optimization problem, we can reduce the overall implementation cost while maintaining service quality. Due to the uncertain nature of cloud requests, we set up a stochastic Optimization-based approach. Our technique is used to assign individual cloud resources and the results show its effectiveness. Keywords: Cloud computing, Resource allocation, Demand

scholarly journals Improvising Dynamic Cloud Resource Allocation to Optimise QoS and Cost Effectiveness

2021 ◽  
Vol 10 (3) ◽  
pp. 206-209
Author(s):  
Gudur Vamsi Krishna ◽  
K. F. Bharati
Keyword(s):  
Resource Allocation ◽  
Probability Distributions ◽  
Low Cost ◽  
Hybrid Approach ◽  
Cloud Services ◽  
Cloud Resource ◽  
Business Efficiency ◽  
Demand Variability ◽  
Set Up ◽  
Cloud Resources

Cloud computing offers streamlined instruments for outstanding business efficiency processes. Cloud distributors typically give two distinct forms of usage plans: Reserved as well as On-demand. Restricted policies provide inexpensive long-term contracting services, while order contracts were very expensive and ready for brief rather than long longer periods. In order to satisfy current customer demands with equal rates, cloud resources must be delivered wisely. Many current works depend mainly on low-cost resource-reserved strategies, which may be under-provisioning and over-provisioning rather than costly ondemand solutions. Since unfairness can cause enormous high availability costs and cloud demand variability in the distribution of cloud resources, resource allocation has become an extremely challenging issue. The hybrid approach to allocating cloud services according to complex customer orders is suggested in that article. The strategy was constructed as a two-step mechanism consisting of accommodation stages and then a versatile structure. In this way, by constructing each step primarily as an optimization problem, we minimize the total cost of implementation, thereby preserving service quality. By modeling client prerequisites as probability distributions are disseminated owing to the dubious presence of cloud requests, we set up a stochastic Optimization-based approach. Using various approaches, our technique is applied, and the results demonstrate its effectiveness when assigning individual cloud resources.

Research of Recommended Service of Mobile Terminal Based on User Behavior Analysis

2011 ◽  
Vol 109 ◽  
pp. 577-581
Author(s):  
Wei Liu ◽  
Dong Mei Mu ◽  
Dao Li Huang ◽  
Ji Hao
Keyword(s):  
Behavior Analysis ◽  
Recommendation System ◽  
User Behavior ◽  
Mobile Terminal ◽  
Network Access ◽  
Analysis Model ◽  
User Behavior Analysis ◽  
User Clustering ◽  
Transfer Of Information ◽  
User Recommendation

Due to its portability, mobile terminals (mobile phones and similar devices) have become an transfer of information, between people as well as an important tool for network access. Based on user behavior analysis, using the information of data warehouse will be a reasonable quantification of qualitative indicators, draw the user a variety of potential semantic behavior, and user clustering and dimension reduction, the establishment of a recommendation based on user behavior analysis model . This paper based on user behavior analysis, first extract the user factors into the model are data on these factors reduce the dimensions of the conclusion that the targeted user recommendation system, and such user back into the model test to verify the target User's accuracy.

Design of user behavior analysis model of e-commerce website based on Spark

2020 ◽  
Author(s):  
Xiumei Wen ◽  
Yuxuan Han ◽  
Jianglong Fu ◽  
Panying Li ◽  
Fanxing Meng
Keyword(s):  
Behavior Analysis ◽  
User Behavior ◽  
Analysis Model ◽  
User Behavior Analysis

Design and Implementation of a Online Network User Behavior Analysis System

2012 ◽  
Vol 566 ◽  
pp. 707-711
Author(s):  
Song Li Hou ◽  
Yuan Li
Keyword(s):  
Behavior Analysis ◽  
Real Time ◽  
User Behavior ◽  
Analysis Model ◽  
Real Time Monitoring ◽  
Online Analysis ◽  
User Behavior Analysis ◽  
Design And Implementation ◽  
Internal Network ◽  
Analysis System

By research of the current network traffic idenfication methods and typical network user behavior analysis methods，a online network user behavior analysis model has been designed and implemented. In order to achieve internal network user behavior real-time monitoring and online analysis purposes.

scholarly journals An Approch based on Genetic Algorithm for Multi-tenant Resource Allocation in SaaS Applications

2017 ◽  
Vol 6 (3) ◽  
pp. 124
Author(s):  
Elaheh Kheiri ◽  
Mostafa Ghobaei Arani ◽  
Alireza Taghizadeh
Keyword(s):  
Genetic Algorithm ◽  
Resource Allocation ◽  
Service Quality ◽  
System Performance ◽  
Response Rate ◽  
Cloud Services ◽  
Service Performance ◽  
Software As A Service ◽  
Efficient Resource ◽  
Simulation Results

In recent years, the use of cloud services has been significantly expanded. The providers of software as a service employ multi-tenant architectures to deliver services to their users. In these multi-tenant applications the resource allocation would suffer from over-utilization or under-utilization issues. Considering the significant effects of resource allocation on the service performance and cost, in this paper we have proposed an approach based on genetic algorithm for resource allocation which guarantees service quality through providing adequate resources. The proposed approach also improves system performance, meets the requirements of users and provides maximum resource efficiency. Simulation results show that the proposed approach has better response rate and availability comparing to other approaches, while provides an efficient resource usage.

EFFICIENT RESOURCE ALLOCATION AND QOS ENHANCEMENTS OF IOT WITH FOG NETWORK

2019 ◽  
Vol 01 (02) ◽  
pp. 21-30 ◽  
Author(s):  
Senthil Kumar T.
Keyword(s):  
Resource Allocation ◽  
Internet Of Things ◽  
Network Performance ◽  
Cloud Services ◽  
The Internet ◽  
Network Simulator ◽  
Early Access ◽  
Efficient Resource ◽  
Fog Network ◽  
The Internet Of Things

The fog network that is the complementary for the cloud services, bring down the services of the cloud to its edge device with the easy and the early access of the information’s for the task that are time sensitive for the internet of things. The enormous big data flow through the internet of things from various tasks in the variety of application has paved way to seek the efficient ways of resource allocation of the tasks in the fog network. So efficient way of resources allocation entailed to enhances the quality of service for the internet of things and improve the network performance, is proposed in the paper. The efficient resource allocation with reduced energy consumption and maximum resources utilization in the fog network is performed for the information’s gained over the internet of things. The performance of the proposed method is validated using the network simulator to gain knowledge on the proficiency of the proposed method of resource allocation in the fog.

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