skip to main content
research-article

Systematic Scalability Modeling of QoS-aware Dynamic Service Composition

Published:02 November 2022Publication History
Skip Abstract Section

Abstract

In Dynamic Service Composition (DSC), an application can be dynamically composed using web services to achieve its functional and Quality of Services (QoS) goals. DSC is a relatively mature area of research that crosscuts autonomous and services computing. Complex autonomous and self-adaptive computing paradigms (e.g., multi-tenant cloud services, mobile/smart services, services discovery and composition in intelligent environments such as smart cities) have been leveraging DSC to dynamically and adaptively maintain the desired QoS, cost and to stabilize long-lived software systems. While DSC is fundamentally known to be an NP-hard problem, systematic attempts to analyze its scalability have been limited, if not absent, though such analysis is of a paramount importance for their effective, efficient, and stable operations.

This article reports on a new application of goal-modeling, providing a systematic technique that can support DSC designers and architects in identifying DSC-relevant characteristics and metrics that can potentially affect the scalability goals of a system. The article then applies the technique to two different approaches for QoS-aware dynamic services composition, where the article describes two detailed exemplars that exemplify its application. The exemplars hope to provide researchers and practitioners with guidance and transferable knowledge in situations where the scalability analysis may not be straightforward. The contributions provide architects and designers for QoS-aware dynamic service composition with the fundamentals for assessing the scalability of their own solutions, along with goal models and a list of application domain characteristics and metrics that might be relevant to other solutions. Our experience has shown that the technique was able to identify in both exemplars application domain characteristics and metrics that had been overlooked in previous scalability analyses of these DSC, some of which indeed limited their scalability. It has also shown that the experiences and knowledge can be transferable: The first exemplar was used as an example to inform and ease the work of applying the technique in the second one, reducing the time to create the model, even for a non-expert.

REFERENCES

  1. Alrebeish F. and Bahsoon R.. 2015. Stabilising performance in cloud services composition using portfolio theory. In Proceedings of the IEEE International Conference on Web Services (ICWS). IEEE, New York, NY, 18. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Arellanes Damian and Lau Kung-Kiu. 2020. Evaluating IoT service composition mechanisms for the scalability of IoT systems. Fut. Gen. Comput. Syst. 108 (2020), 827848.Google ScholarGoogle ScholarCross RefCross Ref
  3. Asghari Parvaneh, Rahmani Amir Masoud, and Javadi Hamid Haj Seyyed. 2018. Service composition approaches in IoT: A systematic review. J. Netw. Comput. Applic. 120 (2018), 6177.Google ScholarGoogle ScholarCross RefCross Ref
  4. Bahsoon R. and Emmerich W.. 2008. An economics-driven approach for valuing scalability in distributed architectures. In Proceedings of the 7th Working IEEE/IFIP Conference on Software Architecture (WICSA’08). IEEE Computer Society, 918.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Baresi Luciano, Pasquale Liliana, and Spoletini Paola. 2010. Fuzzy goals for requirements-driven adaptation. In Proceedings of the 18th IEEE International Requirements Engineering Conference. IEEE, 125134.Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Bennaceur Amel, Tun Thein Than, Yu Yijun, and Nuseibeh Bashar. 2019. Requirements engineering. In Handbook of Software Engineering. Springer, Cham, 5192.Google ScholarGoogle ScholarCross RefCross Ref
  7. Berry Daniel M., Cheng Betty H. C., and Zhang Jia. 2005. The four levels of requirements engineering for and in dynamic adaptive systems. In Proceedings of the 11th International Workshop on Requirements Engineering Foundation for Software Quality (REFSQ).Google ScholarGoogle Scholar
  8. Bichler Martin, Kaukal Marion, and Segev Arie. 1999. Multi-attribute auctions for electronic procurement. In Proceedings of the 1st IBM IAC Workshop on Internet-based Negotiation Technologies. Citeseer, 1819. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.17.8499&rep=rep1&type=pdf.Google ScholarGoogle Scholar
  9. Borissov N., Neumann D., and Weinhardt C.. 2010. Automated bidding in computational markets: An application in market-based allocation of computing services. Auton. Agents Multi-Agent Syst. 21, 2 (2010), 128. Retrieved from http://www.springerlink.com/index/41272l185u852475.pdf.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Calinescu Radu, Grunske Lars, Kwiatkowska Marta, Mirandola Raffaela, and Tamburrelli Giordano. 2010. Dynamic QoS management and optimization in service-based systems. IEEE Trans. Softw. Eng. 37, 3 (2010), 387409.Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Caporuscio Mauro, Grassi Vincenzo, Marzolla Moreno, and Mirandola Raffaela. 2015. Go prime: A fully decentralized middleware for utility-aware service assembly. IEEE Trans. Softw. Eng. 42, 2 (2015), 136152.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Cardoso Jorge, Sheth Amit, Miller John, Arnold Jonathan, and Kochut Krys. 2004. Quality of service for workflows and web service processes. Web Semant.: Sci., Serv. Agents World Wide Web 1, 3 (Apr. 2004), 281308. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  13. Castro Jaelson, Kolp Manuel, and Mylopoulos John. 2002. Towards requirements-driven information systems engineering: The Tropos project. Inf. Syst. 27, 6 (2002), 365389.Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Chen Yee-Ming and Yeh Hsin-Mei. 2010. Autonomous adaptive agents for market-based resource allocation of cloud computing. In Proceedings of the 9th International Conference on Machine Learning and Cybernetics. IEEE, 27602764.Google ScholarGoogle ScholarCross RefCross Ref
  15. Dahan Fadl, Binsaeedan Wojdan, Altaf Meteb, Al-Asaly Mahfoudh Saeed, and Hassan Mohammad Mehedi. 2021. An efficient hybrid metaheuristic algorithm for QoS-aware cloud service composition problem. IEEE Access 9 (2021), 9520895217.Google ScholarGoogle ScholarCross RefCross Ref
  16. Dardenne Anne, Lamsweerde Axel Van, and Fickas Stephen. 1993. Goal-directed requirements acquisition. Sci. Comput. Program. 20, 1–2 (1993), 350.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Wolf Tom De and Holvoet Tom. 2006. A Catalogue of Decentralised Coordination Mechanisms for Designing Self-organising Emergent Applications. Technical Report CW458. Katholieke Universiteit Leuven, Department of Computer Science.Google ScholarGoogle Scholar
  18. Dejun Jiang, Pierre Guillaume, and Chi Chi-Hung. 2009. EC2 performance analysis for resource provisioning of service-oriented applications. In Proceedings of the International Conference on Service-oriented Computing (ICSOC/ServiceWave’09). Springer-Verlag, Berlin, 197207. Retrieved from http://dl.acm.org/citation.cfm?id=1926618.1926641.Google ScholarGoogle Scholar
  19. Duboc Leticia, Alrebeish Faisal, Nallur Vivek, and Bahasoon Rami. 2018. Summary of a literature review in scalability of qos-aware service composition. CoRR abs/1810.033014 (Apr. 2018), 5.Google ScholarGoogle Scholar
  20. Duboc L., Letier E., and Rosenblum D. S.. 2013a. Systematic elaboration of scalability requirements through goal-obstacle analysis. IEEE Trans. Softw. Eng. 39, 1 (Jan. 2013), 119140. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  21. Duboc Leticia, Nallur Vivek, and Bahsoon Rami. 2013b. Escalabilidade em Composição Dinâmica de Serviços baseada em QoS: uma Revisão Sistemática. Cadernos do IME. Série Informática 35 (2013), 722. Retrieved from http://www.e-publicacoes.uerj.br/index.php/cadinf/article/view/7991.Google ScholarGoogle Scholar
  22. D’Angelo Mirko, Caporuscio Mauro, Grassi Vincenzo, and Mirandola Raffaela. 2020. Decentralized learning for self-adaptive QoS-aware service assembly. Fut. Gen. Comput. Syst. 108 (2020), 210227.Google ScholarGoogle ScholarCross RefCross Ref
  23. Fredericks Erik M., DeVries Byron, and Cheng Betty H. C.. 2014. AutoRELAX: Automatically RELAXing a goal model to address uncertainty. Empir. Softw. Eng. 19, 5 (2014), 14661501.Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Ghezzi Carlo, Manna Valerio Panzica La, Motta Alfredo, and Tamburrelli Giordano. 2015. Performance-driven dynamic service selection. Concurr. Computat.: Pract. Exper. 27, 3 (2015), 633650. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Goldsby Heather J., Sawyer Pete, Bencomo Nelly, Cheng Betty H. C., and Hughes Danny. 2008. Goal-based modeling of dynamically adaptive system requirements. In Proceedings of the 15th Annual IEEE International Conference and Workshop on the Engineering of Computer Based Systems (ECBS’08). IEEE, 3645.Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Guidara Ikbel, Guermouche Nawal, Chaari Tarak, and Jmaiel Mohamed. 2020. Time-aware selection approach for service composition based on pruning and improvement techniques. Softw, Qual, J, 28, 3 (2020), 12451277.Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Hassan Sara. 2019. Modelling and Evaluation of Microservice Granularity Adaptation Decisions. Ph.D. Dissertation. University of Birmingham.Google ScholarGoogle Scholar
  28. Hayyolalam Vahideh and Kazem Ali Asghar Pourhaji. 2018. A systematic literature review on QoS-aware service composition and selection in cloud environment. J. Netw. Comput. Applic. 110 (2018), 5274.Google ScholarGoogle ScholarCross RefCross Ref
  29. Bidi Arshia Hosseini, Movahedi Zahra, and Movahedi Zeinab. 2021. A fog-based fault-tolerant and QoE-aware service composition in smart cities. Trans. Emerg. Telecommun. Technol. 32, 11 (2021), e4326.Google ScholarGoogle Scholar
  30. Huang Jun, Duan Qiang, Guo Song, Yan Yuhong, and Yu Shui. 2015. Converged network-cloud service composition with end-to-end performance guarantee. IEEE Trans. Cloud Comput. 6, 2 (2015), 545557.Google ScholarGoogle ScholarCross RefCross Ref
  31. Jatoth Chandrashekar, Gangadharan G. R., and Buyya Rajkumar. 2015. Computational intelligence based QoS-aware web service composition: A systematic literature review. IEEE Trans. Serv. Comput. 10, 3 (2015), 475492.Google ScholarGoogle ScholarCross RefCross Ref
  32. Jula Amin, Sundararajan Elankovan, and Othman Zalinda. 2014. Cloud computing service composition: A systematic literature review. Expert Syst. Applic. 41, 8 (2014), 38093824.Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Krupitzer Christian, Roth Felix Maximilian, VanSyckel Sebastian, Schiele Gregor, and Becker Christian. 2015. A survey on engineering approaches for self-adaptive systems. Pervas. Mob. Comput. 17 (2015), 184206.Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Kumar Satish. 2021. Technical Debt-aware and Evolutionary Adaptation for Service Composition in SAAS Clouds. Ph.D. Dissertation. University of Birmingham.Google ScholarGoogle Scholar
  35. Letier Emmanuel and Lamsweerde Axel van. 2002. Agent-based tactics for goal-oriented requirements elaboration. In Proceedings of the 24th International Conference on Software Engineering. ACM, New York, NY, 8393. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Li Shuhua and Chen Minghua. 2010. An adaptive-GA based QoS driven service selection for web services composition. In Proceedings of the International Conference on Computer Application and System Modeling (ICCASM’10). IEEE, V13–416–V13–418. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  37. Liang Huagang, Wen Xiaoqian, Liu Yongkui, Zhang Haifeng, Zhang Lin, and Wang Lihui. 2021. Logistics-involved QoS-aware service composition in cloud manufacturing with deep reinforcement learning. Robot. Comput.-Integ. Manuf. 67 (2021), 101991.Google ScholarGoogle ScholarCross RefCross Ref
  38. Markowitz Harry. 1952. Portfolio selection. J. Fin. 7, 1 (1952), 7791. Retrieved from http://www.jstor.org/stable/2975974.Google ScholarGoogle Scholar
  39. Moghaddam Mahboobeh and Davis Joseph G.. 2014. Service selection in web service composition: A comparative review of existing approaches. In Web Services Foundations. 321346.Google ScholarGoogle ScholarCross RefCross Ref
  40. Moustafa Ahmed and Ito Takayuki. 2018. A deep reinforcement learning approach for large-scale service composition. In Proceedings of the International Conference on Principles and Practice of Multi-agent Systems. Springer, 296311.Google ScholarGoogle ScholarCross RefCross Ref
  41. Moustafa Ahmed and Zhang Minjie. 2013. Multi-objective service composition using reinforcement learning. In Proceedings of the International Conference on Service-oriented Computing. Springer, 298312.Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Nallur Vivek and Bahsoon Rami. 2013. A decentralized self-adaptation mechanism for service-based applications in the cloud. IEEE Trans. Softw. Eng. 39, 5 (2013), 591612. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Peng Shunshun, Wang Hongbing, and Yu Qi. 2020. Multi-clusters adaptive brain storm optimization algorithm for QoS-aware service composition. IEEE Access 8 (2020), 4882248835.Google ScholarGoogle ScholarCross RefCross Ref
  44. Shepperd M., Schofield C., and Kitchenham B.. 1996. Effort estimation using analogy. In Proceedings of the 18th International Conference on Software Engineering. IEEE Computer Society, 170178.Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Souri Alireza, Rahmani Amir Masoud, Navimipour Nima Jafari, and Rezaei Reza. 2020. A hybrid formal verification approach for QoS-aware multi-cloud service composition. Clust. Comput. 23, 4 (2020), 24532470.Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Su Sen, Li Fei, and Yang FangChun. 2008. Iterative selection algorithm for service composition in distributed environments. Sci. China Series F: Inf. Sci. 51, 11 (2008), 18411856.Google ScholarGoogle ScholarCross RefCross Ref
  47. Sykes Daniel, Magee Jeff, and Kramer Jeff. 2011. FlashMob: Distributed adaptive self-assembly. In Proceedings of the 6th International Symposium on Software Engineering for Adaptive and Self-managing Systems. Association for Computing Machinery, New York, NY, 100109.Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. Vakili Asrin and Navimipour Nima Jafari. 2017. Comprehensive and systematic review of the service composition mechanisms in the cloud environments. J. Netw. Comput. Applic. 81 (2017), 2436.Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. Lamsweerde Axel van. 2008. Systematic Requirements Engineering: From System Goals to UML Models to Software Specifications. John Wiley & Sons. Google ScholarGoogle Scholar
  50. Wang Hongbign, Chen Xin, Wu Qin, Yu Qi, Hu Xingguo, Zheng Zibin, and Bouguettaya Athman. 2017. Integrating reinforcement learning with multi-agent techniques for adaptive service composition. ACM Trans. Auton. Adapt. Syst. 12, 2 (2017), 142.Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. Wang Hongbing, Gu Mingzhu, Yu Qi, Tao Yong, Li Jiajie, Fei Huanhuan, Yan Jia, Zhao Wei, and Hong Tianjing. 2019. Adaptive and large-scale service composition based on deep reinforcement learning. Knowl.-based Syst. 180 (2019), 7590.Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. Wang Hongbing, Huang Guicheng, and Yu Qi. 2016. Automatic hierarchical reinforcement learning for efficient large-scale service composition. In Proceedings of the IEEE International Conference on Web Services (ICWS). IEEE, 5764.Google ScholarGoogle ScholarCross RefCross Ref
  53. Wang Hongbing, Li Jiajie, Yu Qi, Hong Tianjing, Yan Jia, and Zhao Wei. 2020. Integrating recurrent neural networks and reinforcement learning for dynamic service composition. Fut. Gen. Comput. Syst. 107 (2020), 551563.Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. Weyns Danny. 2019. Software engineering of self-adaptive systems. In Handbook of Software Engineering. Springer, 399443.Google ScholarGoogle ScholarCross RefCross Ref
  55. Whittle Jon, Sawyer Pete, Bencomo Nelly, Cheng Betty H. C., and Bruel Jean-Michel. 2010. RELAX: A language to address uncertainty in self-adaptive systems requirement. Requir. Eng. 15, 2 (2010), 177196.Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. Wang X.Yang Z. Jing, and H.. 2011. Service selection constraint model and optimization algorithm for web service composition. Inf. Technol. J. 10 (2011), 10241030. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  57. Yang Zhuoqun, Li Zhi, Jin Zhi, and Chen Yunchuan. 2014. A systematic literature review of requirements modeling and analysis for self-adaptive systems. In Proceedings of the International Working Conference on Requirements Engineering: Foundation for Software Quality. Springer, 5571.Google ScholarGoogle ScholarDigital LibraryDigital Library
  58. Yu Eric S. K.. 1997. Towards modelling and reasoning support for early-phase requirements engineering. In Proceedings of the 3rd IEEE International Symposium on Requirements Engineering. IEEE, 226235.Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. Yu Tao, Zhang Yue, and Lin Kwei-Jay. 2007. Efficient algorithms for web services selection with end-to-end QoS constraints. ACM Trans. Web 1, 1 (May 2007), 6. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  60. Yuan Yuan, Zhang Weishi, and Zhang Xiuguo. 2018. A context-aware self-adaptation approach for web service composition. In Proceedings of the 3rd International Conference on Information Systems Engineering (ICISE). IEEE, 3338.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. Systematic Scalability Modeling of QoS-aware Dynamic Service Composition

        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

        Full Access

        • Published in

          cover image ACM Transactions on Autonomous and Adaptive Systems
          ACM Transactions on Autonomous and Adaptive Systems  Volume 16, Issue 3-4
          December 2021
          150 pages
          ISSN:1556-4665
          EISSN:1556-4703
          DOI:10.1145/3543993
          Issue’s Table of Contents

          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: 2 November 2022
          • Online AM: 12 July 2022
          • Accepted: 25 March 2022
          • Revised: 31 January 2022
          • Received: 23 August 2020
          Published in taas Volume 16, Issue 3-4

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article
          • Refereed
        • Article Metrics

          • Downloads (Last 12 months)188
          • Downloads (Last 6 weeks)13

          Other Metrics

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        Full Text

        View this article in Full Text.

        View Full Text

        HTML Format

        View this article in HTML Format .

        View HTML Format
        About Cookies On This Site

        We use cookies to ensure that we give you the best experience on our website.

        Learn more

        Got it!