skip to main content
10.1145/3422392.3422467acmotherconferencesArticle/Chapter ViewAbstractPublication PagessbesConference Proceedingsconference-collections
short-paper

Generating Adaptation Plans Based on Quality Models for Cloud Platforms

Published:21 December 2020Publication History

ABSTRACT

Cloud computing brought up several benefits concerning cost and scale, offering support services for infrastructure provisioning targeting data processing and storage according to application demands. However, it is not trivial to ensure the trustworthiness of associated resources, i.e., the trust of a client in a cloud service and its provider. Hence, one of the main barriers is to warrant the nonfunctional properties of trustworthiness during runtime. This paper presents a new infrastructure to generate adaptation plans based on quality models to ensure different trustworthiness properties. On detecting the degradation of cloud resources regarding the monitored properties, an adaptation plan is generated and executed during runtime to ensure that cloud resources can work under proper trustworthiness levels. The proposed solution intends to be general, possibly being applied to several trustworthiness properties simultaneously. Finally, we evaluated the solution in a feasibility study under a scenario considering data privacy and performance as trustworthiness properties.

References

  1. 2017. Apache Kafka. Retrieved July 12, 2020 from https://kafka.apache.orgGoogle ScholarGoogle Scholar
  2. 2020. Apache Mesos. Retrieved July 12, 2020 from http://mesos.apache.orgGoogle ScholarGoogle Scholar
  3. 2020. AWS Auto Scaling. Retrieved July 12, 2020 from https://aws.amazon.com/autoscaling/Google ScholarGoogle Scholar
  4. 2020. Drools - Business Rules Management System. Retrieved July 12, 2020 from www.drools.orgGoogle ScholarGoogle Scholar
  5. 2020. Kubernetes Horizontal Pod Autoscaler. Retrieved July 12, 2020 from https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/Google ScholarGoogle Scholar
  6. 2020. Swarm Mode Overview. Retrieved July 12, 2020 from https://docs.docker.com/engine/swarm/Google ScholarGoogle Scholar
  7. Carlos M Aderaldo, Nabor C Mendonça, Bradley Schmerl, and David Garlan. 2019. Kubow: An architecture-based self-adaptation service for cloud native applications. In Proceedings of the 13th European Conference on Software Architecture-Volume 2. 42--45.Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. ATMOSPHERE. 2018. Adaptive, Trustworthy, Manageable, Orchestrated, Secure, Privacy-assuring Hybrid, Ecosystem for Resilient Cloud Computing. URL: https://www.atmosphere-eubrazil.eu/project (2018).Google ScholarGoogle Scholar
  9. Tania Basso, Hebert de Oliveira Silva, Leonardo Montecchi, Breno Bernard Nicolau de França, and Regina Lúcia de Oliveira Moraes. 2019. Towards trustworthy cloud service selection: monitoring and assessing data privacy. In Anais do XX Workshop de Testes e Tolerância a Falhas. SBC, 6--19.Google ScholarGoogle Scholar
  10. T. Basso, R. Moraes, N. Antunes, M. Vieira, W. Santos, and W. Meira. 2017. PRI-VAaaS: Privacy Approach for a Distributed Cloud-Based Data Analytics Platforms. In 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID). 1108--1116.Google ScholarGoogle Scholar
  11. Tania Basso, Hebert Silva, Leonardo Montecchi, Breno de França, and Regina Moraes. 2019. Towards trustworthy cloud service selection: monitoring and assessing data privacy. In Anais do XX Workshop de Testes e Tolerância a Falhas (Gramado). SBC, Porto Alegre, RS, Brasil, 6--19. https://doi.org/10.5753/wtf.2019.7711Google ScholarGoogle ScholarCross RefCross Ref
  12. J. L. da Silva, M. M. Assis, A. Braga, and R. Moraes. 2019. Deploying Privacy as a Service within a Cloud-Based Framework. In 2019 9th Latin-American Symposium on Dependable Computing (LADC). 1--4.Google ScholarGoogle Scholar
  13. L. Florio and E. D. Nitto. 2016. Gru:An Approach to Introduce Decentralized Autonomic Behavior in Microservices Architectures. In 2016 IEEE International Conference on Autonomic Computing (ICAC). 357--362.Google ScholarGoogle Scholar
  14. David Garlan, Shang-Wen Cheng, An-Cheng Huang, Bradley Schmerl, and Peter Steenkiste. 2004. Rainbow: Architecture-Based Self Adaptation with Reusable Infrastructure Rainbow: Architecture- Based Self-Adaptation with Reusable Infrastructure. Computer 37, 10 (2004), 46--54. https://doi.org/10.1109/MC.2004.175Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. IBM. 2006. An architectural blueprint for autonomic computing. Technical Report. IBM. 1--6 pages.Google ScholarGoogle Scholar
  16. IBM. 2006. An architectural blueprint for autonomic computing. IBM White Paper 31 (2006), 1--6.Google ScholarGoogle Scholar
  17. Jeffrey Kephart and David Chess. 2003. The vision of autonomic computing. Computer 36, 1 (2003), 41--50.Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Christian Krupitzer, Felix Maximilian Roth, Sebastian Vansyckel, Gregor Schiele, and Christian Becker. 2015. A survey on engineering approaches for self-adaptive systems. Pervasive and Mobile Computing (2015). https://doi.org/10.1016/j.pmcj.2014.09.009Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Philippe Lalanda, Julie McCann, and Ada Diaconescu. 2013. Autonomic Comp. Springer. https://doi.org/10.1007/978-1-4471-5007-7Google ScholarGoogle ScholarCross RefCross Ref
  20. M. Martínez, D. D. Andrés, and J. Ruiz. 2014. Gaining Confidence on Dependability Benchmarks' Conclusions through "Back-to-Back" Testing (Practical Experience Report). In 2014 Tenth European Dependable Computing Conference. 130--137. https://doi.org/10.1109/EDCC.2014.20Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Nádia Medeiros, Naghmeh Ivaki, Pedro Costa, and Marco Vieira. 2017. Towards an Approach for Trustworthiness Assessment of Software as a Service. In 2017 IEEE International Conference on Edge Computing (EDGE). 220--223. https://doi.org/10.1109/IEEE.EDGE.2017.39Google ScholarGoogle ScholarCross RefCross Ref
  22. Peter Mell and Timothy Grance. 2011. The NIST Definition of Cloud Computing Recommendations of the National Institute of Standards and Technology. National Institute of Standards and Technology, Information Technology Laboratory 145 (2011), 7. https://doi.org/10.1136/emj.2010.096966 arXiv:2305-0543Google ScholarGoogle ScholarCross RefCross Ref
  23. Nazila Mohammadi, Sachar Paulus, Mohamed Bishr, Andreas Metzger, Holger Könnecke, Sandro Hartenstein, and Klaus Pohl. 2013. An Analysis of Software Quality Attributes and their Contribution to Trustworthiness. Proceedings of the 3rd International Conference on Cloud Computing and Services Science (CLOSER 2013) (2013).Google ScholarGoogle Scholar
  24. José D'Abruzzo Pereira, Rui Silva, Nuno Antunes, Jorge LM Silva, Breno de França, Regina Moraes, and Marco Vieira. 2020. A Platform to Enable Self-Adaptive Cloud Applications Using Trustworthiness Properties. In Proc. of the 15th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS).Google ScholarGoogle Scholar
  25. Maria Alejandra Rodriguez and Rajkumar Buyya. 2018. Containers Orchestration with Cost-Efficient Autoscaling in Cloud Computing Environments. ArXiv abs/1812.00300 (2018).Google ScholarGoogle Scholar
  26. Adalberto R Sampaio, Julia Rubin, Ivan Beschastnikh, and Nelson S Rosa. 2019. Improving microservice-based applications with runtime placement adaptation. Journal of Internet Services and Applications 10, 1 (2019), 1--30.Google ScholarGoogle ScholarCross RefCross Ref
  27. Marco Vieira, Nuno Antunes, Regina Moraes, Breno de França, Boris Giterman, Alexandre Braga, and Tania Basso. 2018. ATMOSPHERE D3.1 Trustworthiness Framework and Metrics Design. Project deliverable D3.1. ATMOSPHERE. https://www.atmosphere-eubrazil.eu/d31-trustworthiness-framework-and-metrics-designGoogle ScholarGoogle Scholar

Index Terms

  1. Generating Adaptation Plans Based on Quality Models for Cloud Platforms
              Index terms have been assigned to the content through auto-classification.

              Recommendations

              Comments

              Login options

              Check if you have access through your login credentials or your institution to get full access on this article.

              Sign in
              • Published in

                cover image ACM Other conferences
                SBES '20: Proceedings of the XXXIV Brazilian Symposium on Software Engineering
                October 2020
                901 pages
                ISBN:9781450387538
                DOI:10.1145/3422392

                Copyright © 2020 ACM

                Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

                Publisher

                Association for Computing Machinery

                New York, NY, United States

                Publication History

                • Published: 21 December 2020

                Permissions

                Request permissions about this article.

                Request Permissions

                Check for updates

                Qualifiers

                • short-paper
                • Research
                • Refereed limited

                Acceptance Rates

                Overall Acceptance Rate147of427submissions,34%
              • Article Metrics

                • Downloads (Last 12 months)5
                • Downloads (Last 6 weeks)0

                Other Metrics

              PDF Format

              View or Download as a PDF file.

              PDF

              eReader

              View online with eReader.

              eReader