Platform as a Service (PaaS) is a cloud model, designed for the software developers and it helps to streamline the development process by the process of shifting of specific aspects of the system management to the service provider (Kim et al. 2016). Use of PaaS is often done to mobile and web applications with the components that are pre-configured and are maintained by service provider, which indulges the programming language, database and the servers.
This report deals with the services provided by various service providers who provide PaaS as the application development tool. It also contains a comparison of one of the Google App Engine with Bluemix. Screenshots have been provided to make the topic more detailed. The report has been divided in sections to provide a clear concept of the topic.
There are various service providers, who offer PaaS as the tool for web or mobile application development. The list of various PaaS service providers are-
Google app engine was chosen here for comparison with Bluemix as both the brands have name and fame in the industry and the services provided by both of them are appreciated by the customers. There is limited gap between both the applications and thus it was chosen.
Services offered for smart applications development
The services provided by Google app engine for IoT are that it delivers virtual machines running in the innovative data centers of Google and a worldwide fiber network. The VM of the computer engine boots with a very high speed, has local disk options, comes with high performance persistent, and delivers consistent performance. It has automated container management and it is a powerful cluster manager for running the Docker containers.
Bluemix offers SilverHook Powerboats for the implementation of IoT applications in the device.
Artificial intelligence
Google app engine-the accuracy is impressive. The neural nets are trained by the use of a vast array of machines, which are equipped with the GPU chips. One such example is the Google translate app.
Bluemix- According to Fox et al. (2014), the various services provided are expanding the training of data, diverse classifiers, new feature selection process, improved lexicon support and expanded support for emoticons, slang and emojis.
Google app engine- the cloud services over here are designed for distributed web applications. The Google app engine also manages various implementation details with the PaaS services. Some of the PaaS options such as the RedHat OpenShift offer some IaaS-like features, which allow the user to customize the environment to a greater degree (Kumar 2016). But it is different with Google app engine as, with the Google Paas, user can manage infrastructure and also the runtime environments that are guaranteed to scale as far as the user’s applications follow playbook of Google app engine (Fox et al. 2015).
Bluemix- the layers if the PaaS system for Bluemix are external, integration and internal business application. The external layer is for the trading partners, e-commerce, mobile devices and internet of things. The integration layer includes the ESB, BPM, IBM WebSphere Cast Iron, IBM WebSphere application server, IBM Sterling commerce, business activity monitoring and the predictive data analytic capability (Ray 2017). The internal business application layer contains the lifecycle management of the product, the customer relationship, the enterprise resource planning, the data warehouse and the digital operation.
In Google app engine using java eclipse
The tools used for this purpose are JDK 1.6, Google App Engine Java SDK 1.6.3.1 and Eclipse 3.7 + Google plugin for eclipse
Step 1- the google plugin for eclipse needs to be installed
Step 2- a new web application project needs to be created
Step 3- the generated project directory needs to be reviewed
The extra file over here is “appengine-web.xml“, Google App Engine has to run this and deploy the application.
Coding has to be done in this phase.
Step 4- the project needs to be run as web application by right clicking on it.
Step 5- the user needs to access the https://localhost:8888/ URL in order to view the result.
Step 6- the Google app engine needs to be deployed.
The account needs to be registered on https://appengine.google.com/, and an application id has to be generated for the web application.
Step 1- creation of a project for the app
Login to IBM Bluemix DevOps Services.
In the navigation top, click on My Projects
Click on CREATE PROJECT
On the page for creation of the project-
Step 2- create a simple webpage
Step 3- create a manifest
Step 4- the deployment phase
Click on the deploy button on the web IDE.
When the app is deployed, click on the open the application URL button and go to web IDE
The app gets deployed.
Google app engine – deployment rises due to stack overflow (Powell and Hollingsworth 2014). Anyone can extract the dwarf info from the binary files. The native code execution can be achieved.
Bluemix-the volumes are slow and only a root user can access the data. There are many blockage factors for the applications, which store the data (Sandhya and Kanth 2016). The developed pipeline is very slow and thus it causes deployment problems.
Bluemix offers the free trail, payment on whatever data the user uses and subscriptions.
These options are available for the Google app engine as well.
Conclusion
Hence, from the above discussion, it can be concluded that the use of PaaS is done frequently to develop mobile plus the web applications with components which are pre-configured and also are maintained by those who are the service provider which indulges programming language, database and the servers. Between Google app engine and IBM Bluemix, Google app engine is better to use because of the various features provided by it to the users.
References
Fox, G.C., Qiu, J., Kamburugamuve, S., Jha, S. and Luckow, A., 2015, May. Hpc-abds high performance computing enhanced apache big data stack. In Cluster, Cloud and Grid Computing (CCGrid), 2015 15th IEEE/ACM International Symposium on (pp. 1057-1066). IEEE.
Kim, M., Mohindra, A., Muthusamy, V., Ranchal, R., Salapura, V., Slominski, A. and Khalaf, R., 2016. Building scalable, secure, multi-tenant cloud services on IBM Bluemix. IBM Journal of Research and Development, 60(2-3), pp.8-1.
Kumar, P., 2016. Internet of Things (IoT) development platforms-A case of IBM Bluemix (Doctoral dissertation, Masterarbeit, Koblenz, Universität Koblenz-Landau, Campus Koblenz, 2016).
Ray, P.P., 2017. A Survey of IoT Cloud Platforms. Future Computing and Informatics Journal.
Sandhya, P. and Kanth, G.K., 2016. Sensor Network Accessing Cloud Services for Data Collection and Sharing Using Arduino Yun. International Journal of Scientific Research, 4(9).
Vijaya, A. and Neelanarayanan, V., 2015. Framework for Platform Agnostic Enterprise Application Development Supporting Multiple Clouds. Procedia Computer Science, 50, pp.73-80.
Zhang, C., 2017. Apply on Instance of IBM Watson Cognitive Computing System.
Powell, D. and Hollingsworth, J., 2014, January. Flipping the Classroom by Using Cloud Services and Third Party Online Courses in Traditional Class Settings Enables All Computer Science Students an Equal Education. In Proceedings of the International Conference on Frontiers in Education: Computer Science and Computer Engineering (FECS) (p. 1). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp).
Jamjoom, H., Williams, D. and Sharma, U., 2014, August. Don’t call them middleboxes, call them middlepipes. In Proceedings of the third workshop on Hot topics in software defined networking (pp. 19-24). ACM.
Fox, G.C., Jha, S., Qiu, J. and Luckow, A., 2014, October. Towards an understanding of facets and exemplars of big data applications. In Proceedings of the 20 Years of Beowulf Workshop on Honor of Thomas Sterling’s 65th Birthday (pp. 7-16). ACM.
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