Dionysis Athanasopoulos
ABSTRACT
Given that cloud machines are usually remotely located from the devices of the end-users of the front-end of mobile apps, the end-users can face delays. The Fog has been introduced to augment mobile apps with machines for data analytics that are close/at the network edge. However, edge machines are resource constrained and hence, the execution of heavy data-analytics on edge machines is not always feasible. Thus, light versions of data-analytics algorithms should be deployed on edge machines. But, how can software engineers develop mobile apps that autonomically switch between the Fog and the Cloud? To answer this, we found the composite pattern of the Autonomic Integrator that extends the back-end of mobile apps to use alternative data-analytics algorithms. The pattern first includes the definition of the conceptual model of an extensible back-end that integrates back-end instances deployed on the Fog and the Cloud. Secondly, the pattern includes the conceptual model of an autonomic component. The autonomic component decides at runtime the switching of the front-end to a back-end instance that has the lowest response-time. Finally, the pattern covers the integration between the extensible back-end and the autonomic component.
- J. Andersson, L. Baresi, N. Bencomo, R. de Lemos, A. Gorla, P. Inverardi, and T. Vogel. 2010. Software Engineering Processes for Self-Adaptive Systems. In International Seminar on Software Engineering for Self-Adaptive Systems. 51–75.Google Scholar
- D. Athanasopoulos. 2017. The Aspect of Data Translation in Service Similarity. In International Conference on Web Services. 188–195.Google Scholar
- D. Athanasopoulos. 2017. Service Decoupler: Full Dynamic Decoupling in Service Invocation. In European Conference on Pattern Languages of Programs. 10:1–10:9.Google Scholar
- D. Athanasopoulos, M. McEwen, and A. Rainer. 2019. Mobile Apps with Dynamic Bindings Between the Fog and the Cloud. In International Conference on Service-Oriented Computing. 539–554.Google Scholar
- D. Athanasopoulos, A. Zarras, and V. Issarny. 2009. Service Substitution Revisited. In IEEE/ACM Automated Software Engineering. 555–559.Google Scholar
- D. Athanasopoulos and A. V. Zarras. 2019. Mining Abstract XML Data-Types. ACM Transactions on the Web 13, 1 (2019), 2:1–2:37.Google ScholarDigital Library
- D. Athanasopoulos, A. V. Zarras, P. Vassiliadis, and V. Issarny. 2011. Mining service abstractions. In International Conference on Software Engineering. 944–947.Google Scholar
- C. Barna, H. Khazaei, M. Fokaefs, and M. Litoiu. 2017. Delivering Elastic Containerized Cloud Applications to Enable DevOps. In International Symposium on Software Engineering for Adaptive and Self-Managing Systems. 65–75.Google Scholar
- C. Barna, M. Litoiu, M. Fokaefs, M. Shtern, and J. Wigglesworth. 2018. Runtime Performance Management for Cloud Applications with Adaptive Controllers. In International Conference on Performance Engineering. 176–183.Google Scholar
- L. Cavallaro and E. Di Nitto. 2008. An Approach to Adapt Service Requests to Actual Service Interfaces. In Software Engineering for Adaptive and Self-Managing Systems. 129–136.Google Scholar
- M. Davydov. 2005. Ease Web Services Invocation with Dynamic Decoupling. In IBM.Google Scholar
- T. Erl. 2016. Service-Oriented Architecture: Analysis and Design for Services and Microservices (second editioned.). Prentice Hall.Google Scholar
- J. O. Kephart and D. M. Chess. 2003. The Vision of Autonomic Computing. IEEE Computer 36, 1 (2003), 41–50.Google ScholarDigital Library
- W. Kongdenfha, H. R. M. Nezhad, B. Benatallah, and R. Saint-Paul. 2014. Web Service Adaptation: Mismatch Patterns and Semi-Automated Approach to Mismatch Identification and Adapter Development. In Web Services Foundations. 245–272.Google Scholar
- F. Li, J. Fröhlich, D. Schall, M. Lachenmayr, C. Stückjürgen, S. Meixner, and F. Buschmann. 2018. Microservice Patterns for the Life Cycle of Industrial Edge Software. In European Conference on Pattern Languages of Programs. ACM, 4:1–4:11.Google Scholar
- X. Liu and H. Liu. 2012. Automatic Abstract Service Generation from Web Service Communities. In International Conference on Web Services. 154–161.Google Scholar
- R. C. Martin. 2002. Agile Software Development: Principles, Patterns, and Practices. Prentice Hall.Google Scholar
- N. Mostafa, I. Al Ridhawi, and M. Aloqaily. 2018. Fog resource selection using historical executions. In International Conference on Fog and Mobile Edge Computing. 272–276.Google Scholar
- C. Perera, Y. Qin, J. C. Estrella, S. Reiff-Marganiec, and A. V. Vasilakos. 2017. Fog Computing for Sustainable Smart Cities: A Survey. Comput. Surveys 50, 3 (2017), 32:1–32:43.Google Scholar
- P. Plebani, D. García-Pérez, M. Anderson, D. Bermbach, C. Cappiello, R. I. Kat, F. Pallas, B. Pernici, S. Tai, and M. Vitali. 2017. Information Logistics and Fog Computing: The DITAS Approach. In International Conference on Advanced Information Systems Engineering. 129–136.Google Scholar
- S. Ponnekanti and A. Fox. 2004. Interoperability Among Independently Evolving Web Services. In International Middleware Conference.Google ScholarDigital Library
- L. Popa, Y. Velegrakis, R. J. Miller, M. A. Hernández, and R. Fagin. 2002. Translating Web Data. In International Conference on Very Large Data Bases. 598–609.Google Scholar
- T. Ruokolainen and L. Kutvonen. 2006. Service Typing in Collaborative Systems. In International Conference on Interoperability for Enterprise Software and Applications. 343–353.Google Scholar
- A. Seitz, F. Thiele, and B. Bruegge. 2018. Fogxy: An Architectural Pattern for Fog Computing. In European Conference on Pattern Languages of Programs. ACM, 33:1–33:8.Google Scholar
- A. J. Smola and S.V.N. Vishwanathan. 2008. Introduction to Machine Learning. Cambridge University Press.Google Scholar
- Y. Taher, D. Benslimane, M-C. Fauvet, and Z. Maamar. 2006. Towards an Approach for Web Services Substitution. In International Database Engineering and Applications Symposium. 166–173.Google Scholar
- P. Tan, M. Steinbach, and V. Kumar. 2006. Introduction to Data Mining. Pearson Addison Wesley.Google Scholar
- B. Varghese, M. Villari, O. Rana, P. James, T. Shah, M. Fazio, and R. Ranjan. 2018. Realizing Edge Marketplaces: Challenges and Opportunities. IEEE Cloud Computing 5, 6 (2018), 9–20.Google ScholarCross Ref
Index Terms
- Composite Pattern for Autonomic Switching of Service Back-Ends between the Fog and the Cloud
Recommendations
A Pattern for Fog Computing
VikingPLoP '16: Proceedings of the 10th Travelling Conference on Pattern Languages of ProgramsFog Computing is a new variety of the cloud computing paradigm that brings virtualized cloud services to the edge of the network to control the devices in the IoT. We present a pattern for fog computing which describes its architecture, including its ...
Cloud, Fog, or Mist in IoT? That Is the Question
Special Issue on Fog, Edge, and Cloud IntegrationInternet of Things (IoT) has been commercially explored as Platforms as a Services (PaaS). The standard solution for this kind of service is to combine the Cloud computing infrastructure with IoT software, services, and protocols also known as CoT (...
Survey on DDoS Attacks and Defense Mechanisms in Cloud and Fog Computing
This article describes how cloud computing has emerged as a strong competitor against traditional IT platforms by offering low-cost and "pay-as-you-go" computing potential and on-demand provisioning of services. Governments, as well as organizations, ...
Comments