A Rule-Based Approach for Effective Resource Provisioning in Hybrid Cloud Environment

Grewal, Rajkamal Kaur; Pateriya, Pushpendra Kumar

doi:10.1007/978-3-642-35461-8_5

Cited by 33 publications

(19 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As the authors point out, it is possible to find studies that develop a Cloud service using agents for different specific purposes [26] [15]. Some notable examples of Cloud providers combining resources include works that share Cloud resources to offer Infrastructure as a Service (IaaS) [21] [40]. Other works apply SLA to distribute services [39][4], but they are designed as individual services and not as a whole Cloud system.…”

Section: Discussionmentioning

confidence: 99%

Real-time agreement and fulfilment of SLAs in Cloud Computing environments

Prieta

Heras

Palanca

et al. 2015

AIC

View full text Add to dashboard Cite

A Cloud Computing system must readjust its resources by taking into account the demand for its services. This raises the need for designing protocols that provide the individual components of the Cloud architecture with the ability to self-adapt and to reach agreements in order to deal with changes in the services demand. Furthermore, if the Cloud provider has signed a Service Level Agreement (SLA) with the clients of the services that it offers, the appropriate agreement mechanism has to ensure the provision of the service contracted within a specified time. This paper introduces real-time mechanisms for the agreement and fulfilment of SLAs in Cloud Computing environments. On the one hand, it presents a negotiation protocol inspired by the standard WSAgreement used in web services to manage the interactions between the client and the Cloud provider to agree the terms of the SLA of a service. On the other hand, it proposes the application of a real-time argumentation framework for redistributing resources and ensuring the fulfilment of these SLAs during peaks in the service demand.

show abstract

Section: Discussionmentioning

confidence: 99%

Real-time agreement and fulfilment of SLAs in Cloud Computing environments

Prieta

Heras

Palanca

et al. 2015

AIC

View full text Add to dashboard Cite

show abstract

“…For instance, [42] does not directly address deadline and cost aspects, but the solution proposed could be used to cover them with slight operational modifications.…”

Section: Surveymentioning

confidence: 99%

Cloud resource management: towards efficient execution of large-scale scientific applications and workflows on complex infrastructures

2017

View full text Add to dashboard Cite

Cloud computing evolved from the concept of utility computing, which is defined as the provision of computational and storage resources as a metered service. Another key characteristic of cloud computing is multitenancy, which enables resource and cost sharing among a large pool of users. Characteristics such as multitenancy and elasticity perfectly fit the requirements of modern data-intensive research and scientific endeavors. In parallel, as science relies on the analysis of very large data sets, data management and processing must be performed in a scalable and automated way. Workflows have emerged as a way to formalize and structure data analysis, thus becoming an increasingly popular paradigm for scientists to handle complex scientific processes. One of the key enablers of this conjunction of cloud computing and scientific workflows is resource management. However, several issues related to data-intensive loads, complex infrastructures such as hybrid and multicloud environments to support large-scale execution of workflows, performance fluctuations, and reliability, pose as challenges to truly position clouds as viable high-performance infrastructures for scientific computing. This paper presents a survey on cloud resource management that provides an extensive study of the field. A taxonomy is proposed to analyze the selected works and the analysis ultimately leads to the definition of gaps and future challenges to be addressed by research and development.

show abstract

“…Companies can use its own infrastructure (private cloud), which is insufficient to successfully handle peaks workloads, and solve these peaks using the resources of an external provider (public cloud). The economic savings that offer public cloud services are higher than those of private clouds, although the level of privacy is lower [6]. Therefore, an organization can optimize its efficiency using public cloud services for not critical or lower sensitive data transactions, and use only the private cloud in those cases where it is necessary [7].…”

Section: Network Protocols and Algorithmsmentioning

confidence: 99%

Resource Allocation on a Hybrid Cloud for Smart Grids

Briones

Pozuelo

Navarro

et al. 2016

NPA

View full text Add to dashboard Cite

The use of hybrid clouds enables companies to cover their demands of IT resources saving costs and gaining flexibility in the deployment of infrastructures by paying under demand these resources. However, considering a scenario with various services to be allocated in more than one cloud, it is necessary to find the distribution of services that minimizes the overall operating costs. This paper researches on the resource allocation methodology to be applied in a multi-cloud scenario based on the findings derived from the framework used for the FINESCE project. The purpose of this work is to define a methodology to assist on the hybrid cloud selection and configuration in the Smart Grid for both generic and highly-constrained scenarios in terms of latency and availability. Specifically, the presented method is aimed to determine which is the best cloud to allocate a resource by (1) optimizing the system with the information of the network and (2) minimizing the occurrence of collapsed or underused virtual machines. Also, to assess the performance of this method and any alternative proposals, a general set of metrics has been defined. These metrics have been refined taking into account the expertise of FINESCE partners in order to shape Smart Grid clouds and reduce the complexity of computation. Finally, using the data extracted from the FINESCE testbed, a decision tree is used to come up with the best resource allocation scheme.

show abstract

A Rule-Based Approach for Effective Resource Provisioning in Hybrid Cloud Environment

Cited by 33 publications

References 7 publications

Real-time agreement and fulfilment of SLAs in Cloud Computing environments

Real-time agreement and fulfilment of SLAs in Cloud Computing environments

Cloud resource management: towards efficient execution of large-scale scientific applications and workflows on complex infrastructures

Resource Allocation on a Hybrid Cloud for Smart Grids

Contact Info

Product

Resources

About