Heuristic for resources allocation on utility computing infrastructures

Silva, João Nuno; Veiga, Luís; Ferreira, Paulo

doi:10.1145/1462704.1462713

Cited by 52 publications

(22 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Of course, with the shutdown the number of servers is saved significant amount of energy compared to when all servers in a site to be on. The percentage of this saving is obtained from equation (13). (13) …”

Section: Number Of On Servers = Round (Value Of Prediction Requiremenmentioning

confidence: 99%

Energy Consumption Management using Fuzzy Logic in Distributed Systems Environment

Haririan¹,

Rahmani²

2016

IJGDC

View full text Add to dashboard Cite

show abstract

Section: Number Of On Servers = Round (Value Of Prediction Requiremenmentioning

confidence: 99%

Energy Consumption Management using Fuzzy Logic in Distributed Systems Environment

Haririan¹,

Rahmani²

2016

IJGDC

View full text Add to dashboard Cite

show abstract

“…The cloud computing services are classified in three ways named as Software as a service (SaaS), Platform as a service (PaaS), and Infrastructure as a service (IaaS) [3] . SaaS applications are deployed over the internet for the clients in a single instance multi-tenant model and are accessed by various devices having internet capability through web browser or program interface [4]. It is one of the fastest growing services in cloud.…”

Section: Introductionmentioning

confidence: 99%

Time Effective Workflow Scheduling using Genetic Algorithm in Cloud Computing

Nagar¹,

Gupta²,

Singh³

2018

IJITCS

View full text Add to dashboard Cite

Abstract-Cloud computing is service based technology on internet which facilitates users to access plenty of resources on demand from anywhere and anytime in a metered manner i.e. pay per usage without paying much heed to the maintenance and implementation details of application. As cloud technology is evolving day by day it is being confronted by numerous challenges, such as time and cost under deadline constraints. Research work done so far mainly focused on reducing cost as well as execution time. In order to minimize cost and execution time previously existing workflow scheduling model known as predict earliest finish time is used. In this research work we have proposed a new PEFT genetic algorithm approach to further reduce the execution time on this model. A strategy is developed to let GA focus on to optimize chromosomes objective to get best suitable mutated children. After obtaining a feasible solution, the genetic algorithm focuses on optimizing the execution time. Experimental results show that our algorithm can find better solution within lesser time.

show abstract

“…However, the utility of a user in cloud environment is the usage value of services or resources, and it describes how the user is satisfied with the proposed services or resources while occupying and using them [3,4]. In order to maximize the total utility of all users in cloud environment, it is necessary to analyze and model user utility first and then optimize it to get a maximum [5].…”

Section: Introductionmentioning

confidence: 99%

User Utility Oriented Queuing Model for Resource Allocation in Cloud Environment

Zhang

Liu

2015

Journal of Electrical and Computer Engineering

View full text Add to dashboard Cite

Resource allocation is one of the most important research topics in servers. In the cloud environment, there are massive hardware resources of different kinds, and many kinds of services are usually run on virtual machines of the cloud server. In addition, cloud environment is commercialized, and economical factor should also be considered. In order to deal with commercialization and virtualization of cloud environment, we proposed a user utility oriented queuing model for task scheduling. Firstly, we modeled task scheduling in cloud environment as an M/M/1 queuing system. Secondly, we classified the utility into time utility and cost utility and built a linear programming model to maximize total utility for both of them. Finally, we proposed a utility oriented algorithm to maximize the total utility. Massive experiments validate the effectiveness of our proposed model.

show abstract

Heuristic for resources allocation on utility computing infrastructures

Cited by 52 publications

References 9 publications

Energy Consumption Management using Fuzzy Logic in Distributed Systems Environment

Energy Consumption Management using Fuzzy Logic in Distributed Systems Environment

Time Effective Workflow Scheduling using Genetic Algorithm in Cloud Computing

User Utility Oriented Queuing Model for Resource Allocation in Cloud Environment

Contact Info

Product

Resources

About