Cloud computing: state-of-the-art and research challenges

Zhang, Qi; Lü, Cheng; Boutaba, Raouf

doi:10.1007/s13174-010-0007-6

Cited by 2,831 publications

(1,539 citation statements)

References 21 publications

Supporting

Mentioning

1,331

Contrasting

Unclassified

109

Order By: Relevance

“…At the moment that cloud computing applied in the field of CRM; security play the main challenge deserve studying [4]. This paper showed the pre-requirement of cloud computing deployment, growths and CIA security themes which rising when deploying cloud CRM.…”

Section: Introductionmentioning

confidence: 89%

Cloud CRM: State-of-the-Art and Security Challenges

Shaqrah¹

2016

ijacsa

View full text Add to dashboard Cite

Abstract-Security undoubtedly play the main role of cloud CRM deployment, since the agile firms utilized cloud services in the providers infrastructures to perform acute CRM operations. In this paper researcher emphasis on the cloud CRM themes, security threads the most concern. Some aspects of security discussed concern on deployment the cloud CRM like: Access customers' database and control; secure data transfer over the cloud; trust among the enterprise and cloud service provider; confidentiality, integrity, availability triad; and security hazard, future studies and practice are presented at the end.

show abstract

Section: Introductionmentioning

confidence: 89%

Cloud CRM: State-of-the-Art and Security Challenges

Shaqrah¹

2016

ijacsa

View full text Add to dashboard Cite

show abstract

“…Cloud computing is a promising approach in which resources are provided as services that can be leased and released by users through the Internet in an on-demand fashion [1]. One of the widely used cloud computing service models is Infrastructure as a Service (IaaS) [2] where raw computing resources are provided in the form of Virtual Machines (VMs) to cloud consumers charged for the resources consumed.…”

Section: Introductionmentioning

confidence: 99%

“…It allows cloud providers to move away VMs *Correspondence: dminarolli@fti.edu.al 1 Department of Computer Engineering, Polytechnic University of Tirana, Tirana, Albania Full list of author information is available at the end of the article from overloaded hosts to keep VM performance to SLA levels and to dynamically consolidate VMs to fewer hosts to save energy when the load is low. Using live migration and applying online algorithms that make live migration decisions, it is possible to manage cloud resources efficiently by adapting resource allocation to VM loads, keeping VM performance levels according to SLAs and lowering energy consumption of the infrastructure.…”

Section: Introductionmentioning

confidence: 99%

Tackling uncertainty in long-term predictions for host overload and underload detection in cloud computing

2017

View full text Add to dashboard Cite

Dynamic workloads in cloud computing can be managed through live migration of virtual machines from overloaded or underloaded hosts to other hosts to save energy and/or mitigate performance-related Service Level Agreement (SLA) violations. The challenging issue is how to detect when a host is overloaded to initiate live migration actions in time. In this paper, a new approach to make long-term predictions of resource demands of virtual machines for host overload detection is presented. To take into account the uncertainty of long-term predictions, a probability distribution model of the prediction error is built. Based on the probability distribution of the prediction error, a decision-theoretic approach is proposed to make live migration decision that take into account live migration overheads. Experimental results using the CloudSim simulator and PlanetLab workloads show that the proposed approach achieves better performance and higher stability compared to other approaches that do not take into account the uncertainty of long-term predictions and the live migration overhead.

show abstract

“…Cloud computing has been proposed as a new computing paradigm which can offer practical solutions for solving the limitation of restricted amount of resources and significantly reduce the cost of purchasing, maintaining and managing the physical resources [3,4]. Specifically, it is the further development of distributed computing, parallel computing and grid computing [5].…”

Section: Introductionmentioning

confidence: 99%

A Simplified Particle Swarm Optimization for Job Scheduling in Cloud Computing

Attiya¹,

Zhang²

2017

IJCA

View full text Add to dashboard Cite

Recent advances in various areas such as networking, information and communication technologies have greatly boosted the potential capabilities of cloud computing and made it become more prevalent in recent years. Cloud computing is a promising computing paradigm that facilitates the delivery of IT infrastructure, platforms, and applications of any kind to consumers as services over the internet. Although cloud computing systems nowadays provide better ways to accomplish the job requests in terms of responsiveness and scalability under various workloads, scheduling of jobs or tasks in cloud environment is still NPcomplete and complex in nature due to the dynamicity of resources and on-demand user application requirements. In this paper, a simplified version of particle swarm optimization (PSO) algorithm is proposed to solve the job scheduling problem in cloud computing environment. To evaluate the performance of the proposed approach, this study compares the proposed PSO strategy with genetic algorithm (GA), by having both of them implemented on CloudSim toolkit. The results obtained demonstrate that the presented PSO algorithm can significantly reduce the makespan of job scheduling problem compared with the other metaheuristic algorithm evaluated in this paper.

show abstract

Cloud computing: state-of-the-art and research challenges

Cited by 2,831 publications

References 21 publications

Cloud CRM: State-of-the-Art and Security Challenges

Cloud CRM: State-of-the-Art and Security Challenges

Tackling uncertainty in long-term predictions for host overload and underload detection in cloud computing

A Simplified Particle Swarm Optimization for Job Scheduling in Cloud Computing

Contact Info

Product

Resources

About