PaaS Platforms Research Articles

STAGER: Semantic-Based Framework for Generating Adapters of Service-Based Generic-API for Portable Cloud Applications

In PaaS model, providers have different proprietary APIs, which make developers locked inside a specific platform and not able to easily port their applications among different platforms. So, vendor lock-in problem appeared. One solution to this problem is to use generic APIs with specific adapters. However, any update in a PaaS specific-API makes its corresponding adapter is unusable which causes, what we call, API synchronization problem. Therefore, STAGER (SemanTic-based GenERation of Generic-API Adapters) framework is proposed. STAGER framework provides a semi-automatic adapter generation process, which generates specific adapters of generic APIs for PaaS services (e.g., blob storage and datastore services) for target PaaS platforms. The adapter generation process is based on semantic annotations of the generic APIs and their corresponding PaaS specific-APIs. In order to evaluate STAGER framework, two generic APIs for blob storage and NoSQL datastore services have been proposed. STAGER framework is used to generate the adapters of these generic APIs for two PaaS platforms: Google App Engine (GAE) and Windows Azure. Although there is some overhead for semantically annotating the PaaS APIs, the evaluation results prove the feasibility of STAGER framework and promote the usage of the generated adapters for implementing portable cloud applications.

Multi-PaaS Oriented IoT Dual Middleware Model in 5G Era

The PaaS platform based on cloud computing technology provides a large number of professional and flexible services in the fields of intelligent voice and computer vision. The large-scale application of 5G technology enables the software and hardware of the Internet of Things to use the PaaS service to improve the intelligence level of the system and reduce development costs. However, the problem of service invocation and service selection in the process of combining the IoT system with multiple PaaS platforms directly affects the interaction between the IoT perception layer and the application layer. This paper proposes a 5G multi-PaaS-oriented IoT dual middleware model. The unified communication protocol DMCP is designed. DMCP implements unified parsing of messages based on variable-length data items, which reduces the protocol conversion between the perception layer terminal device and multiple PaaS platforms. Complexity and high communication efficiency. Designed the PaaS service selection algorithm DMPS, DMPS comprehensively according to the equipment demand preferences and the overall performance of PaaS services, to achieve accurate service selection in the multi-PaaS platform at the application layer. Finally, examples and comparative tests verify the feasibility and effectiveness of the proposed model, protocol and algorithm.

Model-Based Application Deployment on Cloud Computing

Cloud Computing refers to a set of technologies and systems that provide various types of resources (computing, storage, software, etc.) on demand, through the Internet or Intranet. Thanks to these advantages many Cloud providers are available and is increasing. These cloud providers offer different PaaS platforms that must each be configured in its own appropriate way to deploy applications in the cloud. Cloud Computing is based on heterogeneity principles, which allows many configurations and sizing choices. This implies that the developer must master all deployment methods in order to benefit from all suppliers. The development and deployment of applications in the Cloud offers a new scientific challenge in terms of expression and taking into account variability. The purpose of the author's work is to propose a deployment method and implement it to automate the process of deploying applications in a cloud environment based on model-driven engineering, to configure and provision applications to be deployed in the cloud.

Cloud restriction solver: A refactoring-based approach to migrate applications to the cloud

ContextThe migration of legacy systems to the Platform as a Service (PaaS) model provides several benefits, but also brings new challenges, such as dealing with the restrictions imposed by the service provider. Furthermore, factors such as time, training and the extensive reengineering activities make the migration process time consuming and error prone. Although there exist several techniques for partial or total migration of legacy applications to the cloud, only a few specifically address the resolution of these constraints. ObjectiveThis paper proposes a novel semi-automatic approach, called Cloud Restriction Solver (CRS), for migrating applications to a PaaS environment that avoids the cloud restrictions through user-defined refactorings. MethodsThe approach is supported by two open and extensible tools. The first one, called CRSAnalyzer, identifies the pieces of code that violate the restrictions of the chosen PaaS platform, while the second one, CRSRefactor, changes those pieces by equivalent cloud-enabled services. ResultsThe applicability of the proposed approach is presented by showing its instantiation for Google App Engine as an Eclipse plugin and by migrating three Java applications to that PaaS successfully. In addition, an instantiation for IBM Bluemix has been created and used to compare the migration of the same application using the developed tools for both cloud providers. ConclusionThe proposed approach fosters software reuse, is cloud-independent, and facilitates the migration of applications to PaaS platforms.

NoMISHAP: A Novel Middleware Support for High Availability in Multicloud PaaS

The availability of multiple Platform as a Service (PaaS) environments, characterized by different actors, supported service models, and available components ease the engineering of new applications and facilitate their fast composition and elastic delivery. However, as demonstrated by the several outages that affected all main cloud providers along the last decade, when these applications come to a production stage there are still several open issues, especially for applications that must meet high availability requirements. This paper proposes NoMISHAP, a Novel Middleware Support for High Availability in multicloud PaaS. NoMISHAP provides the needed middleware support to develop of a new class of highly available services over multiple PaaS environments: it includes service component adapters to ease service migration between different PaaS and supports all needed monitoring, brokerage, and binding/deployment operations to enable transparent transitions between PaaS environments as failures occur at runtime. We deeply assessed NoMISHAP in a realistic scenario over Heroku, Bluemix, and Openshift PaaS platforms. Collected results show both the effectiveness of our solution and quantify the overhead, with the goal of demonstrating the suitability of middleware-based multicloud PaaS solutions to grant high availability.

A Business Driven Scalable Cloud Computing Service Platform (PaaSXpert)

클라우드 컴퓨팅은 협업, 고성능, 비용 절감, 고가용성, 자원 활용 및 재해 복구 등의 여러 가지 중요한 이점을 가지고 있는 최신 기술이다. 클라우드 컴퓨팅의 새로운 시대는 한 번의 마우스 클릭으로 사용자에게 서비스를 제공하는 PaaS (서비스로서의 플랫폼)이다. PaaS 플랫폼은 개발자를 위해 런타임과 프레임 워크 같은 개발 환경을 제공함으로써 클라우드 컴퓨팅 IaaS (서비스로서의 인프라) 부분에서 장점을 가지고 있다. 본 논문에서, 클라우드 파운드리의 오픈 커뮤니티 클라우드 플랫폼을 기반으로 한 효율적인 PaaS 플랫폼인 PaaSXpert의 구축 내용을 설명한다. 클라우드 파운드리는 누구나 애플리케이션을 구축, 배포, 실행, 확장할 수 있게 해 주는 PaaS 플랫폼이다. PaaSXpert는 클라우드 파운드리를 기반으로 구축된 고가용성 분산 시스템이다. PaaSXpert가 확장한 기능으로는 개발자 도구, 정부와 기업을 위한 사용자 포털, 모니터링 도구가 있다. 본 연구에서, 소비자가 자신의 애플리케이션을 여러 분야 (IoT, 빅데이터 및 비즈니스 응용 프로그램 등)에 배포할 수 있는 PaaS 플랫폼 생태계와 SaaS (마켓플레이스)에 대해서 설명한다. PaaS 플랫폼 PaaSXpert의 장점과 구성 요소에 대하여 설명한다. 시뮬레이션 분석을 통하여 본 PaaS 플랫폼이 비용 절감, 성능, 사용자를 위한 서비스 측면에서 기존에 존재하는 플랫폼들보다 더 효율적임을 보였다.

Towards standard PaaS implementation APIs

Platform as a service (PaaS) supports application developers with the ability to implement and deploy their applications in the cloud. Several heterogeneous PaaS platforms are available, such as Google App Engine (GAE), Windows Azure, Cloud Foundry, and OpenShift. Each PaaS provider has its own proprietary implementation and deployment APIs. The heterogeneity of these APIs makes developers worry about their application portability and interoperability. The work in this paper concerns about the heterogeneity of different PaaS implementation APIs. Standard PaaS implementation APIs, called Std-PaaS APIs, have been proposed to solve the application portability problem. Std-PaaS APIs allow developers to develop generic cloud application by writing their applications once and deploying many times on heterogeneous PaaS providers. Std-PaaS APIs have been evaluated using two case studies, in which generic APIs for cloud persistentstorage service and NoSQL datastore service have been developed and used to developed applications to be deployed onto GAE and Windows Azure.

Towards standard PaaS implementation APIs

Platform as a service (PaaS) supports application developers with the ability to implement and deploy their applications in the cloud. Several heterogeneous PaaS platforms are available, such as Google App Engine (GAE), Windows Azure, Cloud Foundry, and OpenShift. Each PaaS provider has its own proprietary implementation and deployment APIs. The heterogeneity of these APIs makes developers worry about their application portability and interoperability. The work in this paper concerns about the heterogeneity of different PaaS implementation APIs. Standard PaaS implementation APIs, called Std-PaaS APIs, have been proposed to solve the application portability problem. Std-PaaS APIs allow developers to develop generic cloud application by writing their applications once and deploying many times on heterogeneous PaaS providers. Std-PaaS APIs have been evaluated using two case studies, in which generic APIs for cloud persistentstorage service and NoSQL datastore service have been developed and used to developed applications to be deployed onto GAE and Windows Azure.

Implementing generic PaaS deployment API: repackaging and deploying applications on heterogeneous PaaS platforms

The cloud platform-as-a-service (PaaS) model provides developers with the ability to deploy and manage their applications remotely in the cloud and pay only for actual usage hours. Currently, there is no standard API for PaaS deployment and management; each PaaS provider [e.g., Google AppEngine (GAE), OpenShift (OS), Cloud Foundry (CF), and Windows Azure] has its own proprietary APIs. This lack of standardisation adds a layer of complexity to application deployment and migration between heterogeneous PaaS platforms because of API incompatibility. A standard (generic) PaaS deployment API overcomes the previously mentioned PaaS API heterogeneity. A generic open-source API, namely the COAPS API, has been proposed to support deployment and management of applications on CF and OS PaaS platforms. This work implements COAPS deployment API to include the GAE PaaS. Whereas both CF and OS PaaS platforms use the same application packaging, deploying the same application on GAE requires application repackaging. We evaluated our work using a case study in which the same application is automatically deployed on CF and GAE.

Implementing generic PaaS deployment API: repackaging and deploying applications on heterogeneous PaaS platforms

Implementing generic PaaS deployment API: repackaging and deploying applications on heterogeneous PaaS platforms

Development of cloud application efficiency evaluation criterion

Using cloud computing allows to significantly improve the infrastructure support, reduce deployment time and faster adapt to changes in load with periodic peaks. The latter problem is solved by cloud application scaling that allows to change the number of involved computing resources depending on the cloud application use intensity. To solve the problem of selecting the best scaling strategy, a comparison mechanism of different scaling strategies with each other is needed. Such a comparison can be performed by calculating the developed efficiency criterion, which combines the assessment of used computing and reputational resources. The developed criterion allows to calculate the cloud application efficiency based on information about the progress of a network request, the number of users and the cost of maintenance of cloud infrastructure. The criterion allows to compare and combine different metrics of cloud applications and can be used to compare the efficiency of cloud applications on the PaaS platform under different hosting settings using metrics that are specific to the PaaS platforms. The paper shows that the efficiency of the brainstorming system Braintank combined with the information technology for cloud application scaling is by 10.5 % higher compared to other scaling technologies with a significance level of 0.001. Using the information technology for cloud application scaling has allowed to increase the values of the efficiency evaluation criterion from 8 to 12% on the simulator that reproduced the load on the World Cup website.

SeaClouds

The adaptive management of complex applications deployed across multiple heterogeneous PaaS platforms is one of the problems that have emerged with the cloud revolution. The recently started EU research project SeaClouds aims at providing seamless adaptive multi-cloud management of complex applications by supporting the distribution, monitoring and migration of application modules over multiple heterogeneous PaaS platforms. We present the context, motivations and objectives of SeaClouds, its relation with other cloud initiatives, and its initial architecture.

Comparing PaaS offerings in light of SaaS development

Software vendors increasingly aim to apply the Software-as-a-Service (SaaS) delivery model instead of the traditional on-premise model. Platforms-as-a-Service (PaaS), such as Google App Engine and Windows Azure, deliver a computing platform and solution stack as a service, but they also aim to facilitate the development of cloud applications (SaaS). Such PaaS offerings should enable third parties to build and deliver multi-tenant SaaS applications while shielding the complexity of the underpinning middleware and infrastructure. This paper compares, on the basis of a practical case study, three different and representative PaaS platforms with respect to their support for SaaS application development. We have reengineered an on-premise enterprise application into a SaaS application and we have subsequently deployed it in three PaaS-based cloud environments. We have investigated the following qualities of the PaaS platforms from the perspective of SaaS development: portability of the application code base, available support for creating and managing multi-tenant-aware applications, and quality of the tool support.