The Building Blocks of a PaaS

Teixeira, Cláudio; Pinto, Joaquim Sousa; Azevedo, Ricardo Cabral de; Batista, Tiago; Monteiro, André

doi:10.1007/s10922-012-9260-2

Cited by 14 publications

(6 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Depending on the relationship between the provider and the consumer, there are four different cloud computing deployment models within organizations as shown in table 3. [59], [60], [61], [62], [63], [64], [65].…”

Section: C) Results Found From the Cloud Computing Deployment Modelsmentioning

confidence: 99%

Overview of Service and Deployment Models Offered by Cloud Computing, based on International Standard ISO/IEC 17788

Garcia-Quilachamin¹,

Alonso²,

Herrera-Tapia

2018

ijacsa

View full text Add to dashboard Cite

Cloud Computing offers services over the Internet to support business processes, based on deployment models and service, meet business requirements in an efficient and costeffective. A general context of the types of service models that it, as well as the models of deployment, are not known, so the following research questions are stated: Q1) How many studies refer to service models in Cloud Computing and Models of cloud computing deployment?, Q2) How the service models are classified in relation to the types of capabilities Application, Infrastructure y Platform in a Cloud?, and Q3) What types of cloud computing deployment models currently exist?. The objective of this paper is to investigate the service and deployment models that currently exist in cloud computing, for every which a process of systematic review of the literature has been used as a research methodology. The results show that 45 service models and 4 deployment models were found in Cloud Computing, this allows us to conclude that the offered models give a lot and diverse solutions for the business processes.

show abstract

Section: C) Results Found From the Cloud Computing Deployment Modelsmentioning

confidence: 99%

Overview of Service and Deployment Models Offered by Cloud Computing, based on International Standard ISO/IEC 17788

Garcia-Quilachamin¹,

Alonso²,

Herrera-Tapia

2018

ijacsa

View full text Add to dashboard Cite

show abstract

“…Еще один компонент в структуре единого пространства геомагнитных данных играет решающую роль в обеспечении высокой вычислительной скорости обращения к распределенным источникам геомагнитных данных. Таким компонентом является облако вычислительных ресурсов O , предоставляющее среду выполнения программного кода на базе модели PaaS (Platform as a Service, платформа как сервис) [20,21]. Облачная инфраструктура единого пространства геомагнитных данных обслуживает ряд виртуальных машин, каждой из которых выделяется один или несколько заданных вычислительных процессов по получению и обработке массивов данных.…”

Section: H a B  unclassified

Conception of Geomagnetic Data Integrated Space

Воробьев¹,

Vorobeva²,

Yusupova³

2019

Тр. СПИИРАН

View full text Add to dashboard Cite

. As is known, today the problem of geomagnetic field and its variations parameters monitoring is solved mainly by a network of magnetic observatories and variational stations, but a significant obstacle in the processing and analysis of the data thus obtained, along with their spatial anisotropy, are omissions or reliable inconsistency with the established format. Heterogeneity and anomalousness of the data excludes (significantly complicates) the possibility of their automatic integration and the application of frequency analysis tools to them. Known solutions for the integration of heterogeneous geomagnetic data are mainly based on the consolidation model and only partially solve the problem. The resulting data sets, as a rule, do not meet the requirements for real-time information systems, may include outliers, and omissions in the time series of geomagnetic data are eliminated by excluding missing or anomalous values from the final sample, which can obviously lead to both to the loss of relevant information, violation of the discretization step, and to heterogeneity of the time series. The paper proposes an approach to creating an integrated space of geomagnetic data based on a combination of consolidation and federalization models, including preliminary processing of the original time series with an optionally available procedure for their recovery and verification, focused on the use of cloud computing technologies and hierarchical format and processing speed of large amounts of data and, as a result, providing users with better and more homogeneous data.

show abstract

“…Cloud environment has been setup in the High End Computing Lab (HECL) for implementing Energy based Efficient Resource Scheduling Framework. For setting Cloud environment in labs, Cloud Foundry [26] has been installed on main server in HECL lab and all the other nodes are connected to main server for the verification of proposed framework. KVM (in Linux 4.3) and Xen (Linux 3.5) are used to measure the performance within a VMware by taking three different types of resources in this research.…”

Section: Implementing Proposed Frameworkmentioning

confidence: 99%

Energy based Efficient Resource Scheduling: A Step Towards Green Computing

Singh¹,

Chana

2014

IJEIC

View full text Add to dashboard Cite

Cloud Computing is an evolving area of efficient utilization of computing resources. Data centers accommodating Cloud applications ingest massive quantities of energy, contributing to high functioning expenditures and carbon footprints to the atmosphere. Hence, Green Cloud computing resolutions are required not only to save energy for the environment but also to decrease operating charges. In this paper, we emphasis on the development of energy based resource scheduling framework and present an algorithm that consider the synergy between various data center infrastructures (i.e., software, hardware, etc.), and performance. In specific, this paper proposes (a) architectural principles for energy efficient management of Clouds; (b) energy efficient resource allocation strategies and scheduling algorithm considering Quality of Service (QoS) outlooks. The performance of the proposed algorithm has been evaluated with the existing energy based scheduling algorithms. The experimental results demonstrate that this approach is effective in minimizing the cost and energy consumption of Cloud applications thus moving towards the achievement of Green Clouds.

show abstract

The Building Blocks of a PaaS

Cited by 14 publications

References 17 publications

Overview of Service and Deployment Models Offered by Cloud Computing, based on International Standard ISO/IEC 17788

Overview of Service and Deployment Models Offered by Cloud Computing, based on International Standard ISO/IEC 17788

Conception of Geomagnetic Data Integrated Space

Energy based Efficient Resource Scheduling: A Step Towards Green Computing

Contact Info

Product

Resources

About