scholarly journals Multi-cloud Platform-as-a-service Model, Functionalities and Approaches

2016 ◽  
Vol 97 ◽  
pp. 63-72 ◽  
Author(s):  
Ana Juan Ferrer ◽  
David García Pérez ◽  
Román Sosa González
Keyword(s):  
Cloud Platform ◽  
Service Model ◽  
Platform As A Service ◽  
Multi Cloud

scholarly journals Data Portability Among Providers o Platform as a Service

2013 ◽  
Vol 21 (Special-Issue) ◽  
pp. 7-11
Author(s):  
Darko Androcec
Keyword(s):  
Data Storage ◽  
Query Languages ◽  
Data Models ◽  
Service Model ◽  
Data Types ◽  
Data Manipulation ◽  
Platform As A Service ◽  
Cloud Providers ◽  
Data Portability ◽  
Lock In

Abstract Platform as a service model has certain obstacles, including data lock-in. It is expensive and time-consuming to move data to the alternative providers. This paper presents data storage options in platform as a service offers and identifies the most common data portability problems between various commercial providers of platform as a service. There are differences among their storage models, data types, remote APIs for data manipulation and query languages. Representing data models of platform as a service and data mappings by means of ontology can provide a common layer to achieve data portability among different cloud providers.

Securing Business IT on the Cloud

2015 ◽  
pp. 2022-2032
Author(s):  
Bina Ramamurthy
Keyword(s):  
Large Scale ◽  
Service Providers ◽  
Cloud Service ◽  
Access Point ◽  
Cloud Provider ◽  
Service Model ◽  
Platform As A Service ◽  
Security Challenges ◽  
Modern Business ◽  
Amazon Web Services

In this chapter, the author examines the various approaches taken by the popular cloud providers Amazon Web Services (AWS), Google App Engine (GAE), and Windows Azure (Azure) to secure the cloud. AWS offers Infrastructure as a Service model, GAE is representative of the Software as a Service, and Azure represents the Platform as a Service model. Irrespective of the model, a cloud provider offers a variety of services from a simple large-scale storage service to a complete infrastructure for supporting the operations of a modern business. The author discusses some of the security aspects that a cloud customer must be aware of in selecting a cloud service provider for their needs. This discussion includes the major threats posed by multi-tenancy in the cloud. Another important aspect to consider in the security context is machine virtualization. Securing these services involves a whole range of measures from access-point protection at the client end to securing virtual co-tenants on the same physical machine hosted by a cloud. In this chapter, the author highlights the major offerings of the three cloud service providers mentioned above. She discusses the details of some important security challenges and solutions and illustrates them using screen shots of representative security configurations.

Execution of Long-Duration Multi-Cloud Serverless Functions Using Selective Migration-Based Approach

2020 ◽  
Vol 10 (4) ◽  
pp. 70-97
Author(s):  
Boubaker Soltani ◽  
Afifa Ghenai ◽  
Nadia Zeghib
Keyword(s):  
Short Duration ◽  
Time Limit ◽  
Cloud Platform ◽  
New Paradigm ◽  
Selective Migration ◽  
Loosely Coupled ◽  
Software Deployment ◽  
Long Duration ◽  
Monolithic Approach ◽  
Multi Cloud

A relatively new paradigm for the Cloud-based software deployment is serverless computing. By adopting stateless loosely-coupled functions, the system can obtain many compositions for several purposes. Contrarily to monolithic approach, serverless computing facilitates the evolution of the applications, since the functions may be independently scheduled for reconstitution. Nevertheless, serverless computing dictates that function execution should be within a short duration (five minutes max in most Cloud platforms), after which the function is abruptly ended even if it has not completed its task. This leads to prevent functions requiring longer time from being adopted as Serverless functions. This paper deals with this drawback. It proposes a migration-based approach that promotes the execution of long-duration serverless functions: each running function that reaches the maximum time limit is repeatedly transferred to another cloud platform where it is carried on. At each migration step, the destination cloud is selected regarding the most relevant criteria.

Resource management in cloud platform as a service systems: Analysis and opportunities

2017 ◽  
Vol 132 ◽  
pp. 98-118 ◽  
Author(s):  
Stefania Costache ◽  
Djawida Dib ◽  
Nikos Parlavantzas ◽  
Christine Morin
Keyword(s):  
Resource Management ◽  
Systems Analysis ◽  
Service Systems ◽  
Cloud Platform ◽  
Platform As A Service

scholarly journals PaaS manager: A platform-as-a-service aggregation framework

2014 ◽  
Vol 11 (4) ◽  
pp. 1209-1228 ◽  
Author(s):  
David Cunha ◽  
Pedro Neves ◽  
Pedro Sousa
Keyword(s):  
Service Model ◽  
Application Development ◽  
Platform As A Service ◽  
Management Processes ◽  
Development Processes ◽  
Application Programming ◽  
Service Aggregation ◽  
Dedicated Networks ◽  
Lock In ◽  
Programming Interfaces

The advent of Cloud Computing opened new opportunities in several areas, namely in the application development processes. As consequence, nowadays, PaaS (Platform-as-a-Service) service model allows simpler and flexible deployment strategies of applications, avoiding the need for dedicated networks, servers, storage, and other services. Within this context, several PaaS providers exist in the market, but each one having specific characteristics, proprietary technologies and Application Programming Interfaces (APIs). Based on such assumptions, this work addresses the challenge of devising a PaaS aggregation solution with the objective of unifying the information and management processes of applications created in PaaS environments. The proposed solution, denominated as PaaS Manager, take the form of a PaaS API aggregator aiming to struggle the existing lock-in in the PaaS market. In this perspective, this paper describes the specification, development and test of the proposed PaaS Manager solution. As result of this framework, end-users are able to select the most appropriate PaaS platform for an application, interacting with any supported vendor through a unique deployment and management interface.

scholarly journals A Framework for Implementing Prediction Algorithm over Cloud Data as a Procedure for Cloud Data Mining

2021 ◽  
Vol 2 (2) ◽  
pp. 1-8
Author(s):  
Safwan A. S. Al-Shaibani ◽  
Keyword(s):  
Data Storage ◽  
Model Building ◽  
Cloud Services ◽  
Prediction Algorithm ◽  
Easy Access ◽  
Cloud Platform ◽  
Recent Past ◽  
Cloud Data ◽  
Platform As A Service ◽  
Post Surgery

The cloud has become an important phrase in data storage for many reasons. Cloud services and applications are widespread in many industries including healthcare due to easy access. The limitless quantity of data available on the clouds has triggered the interest of many researchers in the recent past. It has forced us to deploy machine learning for analyzing the data to get insights as well as model building. In this paper, we have built a service on Heroku Cloud which is a cloud platform as a service (PaaS) and has 15 thousand records with 25 features. The data belongs to healthcare and is related to post-surgery complications. The boost prediction algorithm was applied for analysis and implementation was done in python. The results helped us to determine and tune some of the hyperparameters which have correlations with complications and the reported accuracy of training and testing was found to be 91% and 88% respectively.

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