skip to main content
research-article

User-Centric Adaptation Analysis of Multi-Tenant Services

Published:13 January 2016Publication History
Skip Abstract Section

Abstract

Multi-tenancy is a key pillar of cloud services. It allows different users to share computing and virtual resources transparently, meanwhile guaranteeing substantial cost savings. Due to the tradeoff between scalability and customization, one of the major drawbacks of multi-tenancy is limited configurability. Since users may often have conflicting configuration preferences, offering the best user experience is an open challenge for service providers. In addition, the users, their preferences, and the operational environment may change during the service operation, thus jeopardizing the satisfaction of user preferences. In this article, we present an approach to support user-centric adaptation of multi-tenant services. We describe how to engineer the activities of the Monitoring, Analysis, Planning, Execution (MAPE) loop to support user-centric adaptation, and we focus on adaptation analysis. Our analysis computes a service configuration that optimizes user satisfaction, complies with infrastructural constraints, and minimizes reconfiguration obtrusiveness when user- or service-related changes take place. To support our analysis, we model multi-tenant services and user preferences by using feature and preference models, respectively. We illustrate our approach by utilizing different cases of virtual desktops. Our results demonstrate the effectiveness of the analysis in improving user preferences satisfaction in negligible time.

References

  1. Raian Ali, Carlos Solis, Inah Omoronyia, Mazeiar Salehie, and Bashar Nuseibeh. 2012. Social adaptation: When software gives users a voice. In Proceedings of the 7th International Conference on the Evaluation of Novel Approaches to Software Engineering.Google ScholarGoogle Scholar
  2. Mohsen Asadi, Samaneh Soltani, Dragan Gasevic, Marek Hatala, and Ebrahim Bagheri. 2014. Toward automated feature model configuration with optimizing non-functional requirements. Information and Software Technology 56, 9 (2014), 1144--1165.Google ScholarGoogle ScholarCross RefCross Ref
  3. Luciano Baresi, Sam Guinea, and Liliana Pasquale. 2012. Service-oriented dynamic software product lines. IEEE Computer 45, 10 (2012), 42--48. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. David Benavides, Sergio Segura, and Antonio Ruiz-Cortes. 2010. Automated analysis of feature models 20 years later: A literature review. Information Systems 35, 6 (2010), 615--636. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Nelly Bencomo, Peter Sawyer, Gordon S. Blair, and Paul Grace. 2008. Dynamically adaptive systems are product lines too: Using model-driven techniques to capture dynamic variability of adaptive systems. In Proceedings of the 12th International Conference on Software Product Lines (Workshops). 23--32.Google ScholarGoogle Scholar
  6. Jorge Bernal Bernabe, Juan M. Marin Perez, Jose M. Alcaraz Calero, Felix J. Garcia Clemente, Gregorio Martinez Perez, and Antonio F. Gomez Skarmeta. 2014. Semantic-aware multi-tenancy authorization system for cloud architectures. Future Generation Computer Systems 32 (2014), 154--167. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. C.-P. Bezemer, Andy Zaidman, Bart Platzbeecker, Toine Hurkmans, and A. T. Hart. 2010. Enabling multi-tenancy: An industrial experience report. In Proceedings of the 26th International Conference on Software Maintenance. 1--8. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Cristina Cabanillas, José M. García, Manuel Resinas, David Ruiz, Jan Mendling, and A. Ruiz-Cortés. 2013. Priority-based human resource allocation in business processes. In Proceedings of the 11th International Conference on Service-Oriented Computing. 374--388.Google ScholarGoogle Scholar
  9. Valeria Cardellini, Emiliano Casalicchio, Vincenzo Grassi, Stefano Iannucci, Francesco Lo Presti, and Raffaela Mirandola. 2012. Moses: A framework for QoS driven runtime adaptation of service-oriented systems. IEEE Transactions on Software Engineering 38, 5 (2012), 1138--1159. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Simon Caton and Omer Rana. 2012. Towards autonomic management for cloud services based upon volunteered resources. Concurrency and Computation: Practice and Experience 24, 9 (2012), 992--1014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Citrix. 2013. Citrix Virtual Desktop Handbook 7.x. Retrieved from http://support.citrix.com/article/CTX139331.Google ScholarGoogle Scholar
  12. Fabiano Dalpiaz, Estefan’ia Serral, Pedro Valderas, Paolo Giorgini, and Vicente Pelechano. 2012. A NFR-based framework for user-centered adaptation. In Proceedings of the 31st International Conference on Conceptual Modeling, 439--448. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and TAMT Meyarivan. 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 2 (2002), 182--197. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Juan J. Durillo and Antonio J. Nebro. 2011. jMetal: A Java framework for multi-objective optimization. Advances in Engineering Software 42, 10 (2011), 760--771. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. E. Ostrom. 1990. Governing the Commons. CUP.Google ScholarGoogle Scholar
  16. Hamidreza Eskandari, Christopher D. Geiger, and Gary B. Lamont. 2007. FastPGA: A dynamic population sizing approach for solving expensive multiobjective optimization problems. In Proceedings of the 4th International Conference on Evolutionary Multi-Criterion Optimization. Springer, 141--155. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Krzysztof Z. Gajos, Mary Czerwinski, Desney S. Tan, and Daniel S. Weld. 2006. Exploring the design space for adaptive graphical user interfaces. In Proceedings of the International Working Conference on Advanced Visual Interfaces., 201--208. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Sarah Gallacher, Eliza Papadopoulou, Nick K. Taylor, and M. Howard Williams. 2013. Learning user preferences for adaptive pervasive environments: An incremental and temporal approach. ACM Transactions on Autonomous Adaptive Systems 8, 1 (2013). Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. José M. García, Martin Junghans, David Ruiz, Sudhir Agarwal, and Antonio Ruiz-Cortés. 2013. Integrating semantic web services ranking mechanisms using a common preference model. Knowledge-Based Systems 49 (2013), 22--36. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Jesús García-Galán, Liliana Pasquale, Pablo Trinidad, and Antonio Ruiz Cortés. 2014. User-centric adaptation of multi-tenant services: Preference-based analysis for service reconfiguration. In Proceedings of the 9th International Symposium on Software Engineering for Adaptive and Self-Managing Systems. 65--74. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. J. García-Galán, O. F. Rana, P. Trinidad, and A. Ruiz-Cortés. 2013. Migrating to the cloud: A software product line based analysis. In Proceedings of the 3rd International Conference on Cloud Computing and Services Science. 416--426.Google ScholarGoogle Scholar
  22. Jesús García-Galán, Pablo Trinidad, and Antonio Ruiz-Cortés. 2013. Multi-user variability configuration: A game theoretic approach. In Proceedings of the 28th International Conference on Automated Software Engineering. 574--579.Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Jianmei Guo, Jules White, Guangxin Wang, Jian Li, and Yinglin Wang. 2011. A genetic algorithm for optimized feature selection with resource constraints in software product lines. Journal of Systems and Software 84, 12 (2011), 2208--2221. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Christian Inzinger, Benjamin Satzger, Philipp Leitner, Waldemar Hummer, and Schahram Dustdar. 2013. Model-based adaptation of cloud computing applications. In Procedings of the 1st International Conference on Model-Driven Engineering and Software Development. 351--355.Google ScholarGoogle Scholar
  25. Wendy Ju and Larry Leifer. 2008. The design of implicit interactions: Making interactive systems less obnoxious. Design Issues 24, 3 (2008), 72--84.Google ScholarGoogle Scholar
  26. Kyo Kang, Sholom Cohen, James Hess, William Novak, and A. Peterson. 1990. Feature-Oriented Domain Analysis (FODA) Feasibility Study. Technical Report CMU/SEI-90-TR-021. Software Engineering Institute, Carnegie Mellon University.Google ScholarGoogle Scholar
  27. J. O. Kephart and D. M. Chess. 2003. The vision of autonomic computing. Computer 36, 1 (2003), 41--50. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. I. Kumara, J. Han, A. Colman, Tuan Nguyen, and M. Kapuruge. 2013. Sharing with a difference: Realizing service-based saas applications with runtime sharing and variation in dynamic software product lines. In Proceedings of the 10th International Conference on Services Computing. 567--574. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Steffen Lamparter, Anupriya Ankolekar, Rudi Studer, and Stephan Grimm. 2007. Preference-based selection of highly configurable web services. In Proceedings of the 16th International Conference on World Wide Web. 1013--1022. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Sam Malek, Nenad Medvidovic, and Marija Mikic-Rakic. 2012. An extensible framework for improving a distributed software system’s deployment architecture. IEEE Transactions on Software Engineering 38, 1 (2012), 73--100. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. R. Timothy Marler and Jasbir S. Arora. 2004. Survey of multi-objective optimization methods for engineering. Structural and Multidisciplinary Optimization 26, 6 (2004), 369--395.Google ScholarGoogle ScholarCross RefCross Ref
  32. Clarissa Cassales Marquezan, Florian Wessling, Andreas Metzger, Klaus Pohl, Chris Woods, and Karl Wallbom. 2014. Towards exploiting the full adaptation potential of cloud applications. In Proceedings of the 6th International Workshop on Principles of Engineering Service-Oriented and Cloud Systems, 48--57. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Michael Maurer, Ivona Brandic, and Rizos Sakellariou. 2013. Adaptive resource configuration for cloud infrastructure management. Future Generation Computer Systems 29, 2 (2013), 472--487. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Ralph Mietzner, Andreas Metzger, Frank Leymann, and Klaus Pohl. 2009. Variability modeling to support customization and deployment of multi-tenant-aware software as a service applications. In Proceedings of the International Workshop on Principles of Engineering Service-Oriented Systems, 18--25. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Roger B. Myerson. 1991. Game Theory: Analysis of Conflict. Harvard University Press.Google ScholarGoogle Scholar
  36. V. Nallur and R. Bahsoon. 2013. A decentralized self-adaptation mechanism for service-based applications in the cloud. IEEE Transactions on Software Engineering 39, 5 (2013), 591--612. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Yefim V. Natis. 2012. Gartner Reference Model for Elasticity and Multitenancy. Technical Report. Gartner, Inc.Google ScholarGoogle Scholar
  38. J. Pitt, J. Schaumeier, D. Busquets, and S. Macbeth. 2012. Self-organising common-pool resource allocation and canons of distributive justice. In Proceeddings of the 6th International Conference on Self-Adaptive and Self-Organizing Systems. 119--128. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Ana B. Sánchez, Sergio Segura, and Antonio Ruiz-Cortés. 2014. The drupal framework: A case study to evaluate variability testing techniques. In Proceedings of the 8th International Workshop on Variability Modelling of Software-intensive Systems. 11.Google ScholarGoogle Scholar
  40. Abdel Salam Sayyad, Joseph Ingram, Tim Menzies, and Hany Ammar. 2013. Scalable product line configuration: A straw to break the camel’s back. In Proceedings of the 28th International Conference on Automated Software Engineering. 465--474.Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Julia Schroeter, Sebastian Cech, Sebastian Götz, Claas Wilke, and Uwe Assmann. 2012a. Towards modeling a variable architecture for multi-tenant SaaS-applications. In Proceedings of the 6th Workshop on Variability Modeling of Software-Intensive Systems. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Julia Schroeter, Peter Mucha, Marcel Muth, Kay Jugel, and Malte Lochau. 2012b. Dynamic configuration management of cloud-based applications. In Proceedings of the 16th International Software Product Line Conference - Volume 2, 171--178. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. S. Segura, José Á. Galindo, David Benavides, José A. Parejo, and A. Ruiz-Cortés. 2012. BeTTy: Benchmarking and testing on the automated analysis of feature models. In Proceedings of the 6th Workshop on Variability Modeling of Software-Intensive Systems. Leipzig, Germany. Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Hui Song, Stephen Barrett, Aidan Clarke, and Siobhán Clarke. 2013. Self-adaptation with end-user preferences: Using run-time models and constraint solving. In Proceedings of the 16th International Conference on Model-Driven Engineering Languages and Systems. 555--571.Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Cheri Speier, Iris Vessey, and Joseph S. Valacich. 2003. The effects of interruptions, task complexity, and information presentation on computer-supported decision-making performance. Decision Sciences 34, 4 (2003), 771--797.Google ScholarGoogle ScholarCross RefCross Ref
  46. Jacob Stein, Ingrid Nunes, and Elder Cirilo. 2014. Preference-based feature model configuration with multiple stakeholders. In Proceedings of the 18th International Software Product Lines Conference. 132--141. Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. Thomas Thum, Christian Kastner, Sebastian Erdweg, and Norbert Siegmund. 2011. Abstract features in feature modeling. In Proceedings of the 15th International Software Product Line Conference. 10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. P. Trinidad, D. Benavides, A. Ruiz-Cortés, S. Segura, and A. Jimenez. 2008. FAMA framework. In 12th Software Product Lines Conference. 359--359. Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. Bert Vankeirsbilck, Lien Deboosere, Pieter Simoens, Piet Demeester, Filip De Turck, and Bart Dhoedt. 2014. User subscription-based resource management for desktop-as-a-service platforms. Journal of Supercomputing 69, 1 (2014), 412--428. Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. Guiyi Wei, Athanasios V. Vasilakos, Yao Zheng, and Naixue Xiong. 2010. A game-theoretic method of fair resource allocation for cloud computing services. Journal of Supercomputing 54, 2 (2010), 252--269. Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. J. White, D. Benavides, D. C. Schmidt, P. Trinidad, B. Dougherty, and A. Ruiz-Cortes. 2010. Automated diagnosis of feature model configurations. Journal of Systems and Software 83, 7 (2010), 1094--1107. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. User-Centric Adaptation Analysis of Multi-Tenant Services

      Recommendations

      Reviews

      John S. Edwards

      A method to arrive at configuration consensus in a multitenant shared-service cloud environment is presented in this paper. Section 1 discusses the multitenant cloud. The authors are interested in such systems where each member of the user community has the capability to dynamically modify or preselect various preferences. Since an activity on the part of one user might conflict with the preferences of another member of the community, the authors propose user-centric adaptations based on preference-based analyses. They introduce a four-fold process: monitoring, analysis, planning, and executing (MAPE) in terms of a loop. They use a game-theoretic mechanism to demonstrate their approach. Section 2 discusses the motivating scenario. Section 3 covers the problem. Section 4 describes a solution approach. Section 5 presents the implementation of their prototype. While this paper could be of value to workers in the field, several comments are in order. The scenario chosen as an example comprises four tenants and their preferences. The preferences are a combination of the mundane and the extraordinary. The choice between "Aero" and "Classic" is mundane. The choices among "Very frequent antivirus checks," "Frequent antivirus checks," and "Unfrequent virus checks" are strange and seemingly forced, as is the choice between "Highest firewall level" and "Medium firewall level." These choices attempt to reflect real-life conditions, but they seem artificial. Indeed, a reader could code the preferences (and the authors do in some cases) differently; for example, "Aero" and "Classic" could be coded as " x " and "not x " while the preferences "Very frequent antivirus checks," "Frequent antivirus checks," and "Unfrequent virus checks" could be coded as " y +," " y ," and "- y ." The analysis could proceed without the semantics. Three very complex figures are included purporting to indicate how the process works. Section 4.2, "Analysis," uses game theory to arrive at a solution. Unfortunately, the discussion is chock-a-block with formulae and must be either taken at face value for those who are not game theory adepts or taken for granted by adepts. The results of the game are discussed as it progresses. The authors discuss the need to extend their work and give examples of where they may choose to go. In the final section, as they point out, their approach could equally apply to "any kind of adaptive system" and they chose one based upon multitenant services. This work could be of general utility; perhaps they chose the multitenant environment to attract attention to the effort. A more abstract presentation would have been valid, but perhaps less noteworthy. Online Computing Reviews Service

      Access critical reviews of Computing literature here

      Become a reviewer for Computing Reviews.

      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 10, Issue 4
        Special Section on Best Papers from SEAMS 2014 and Regular Articles
        February 2016
        211 pages
        ISSN:1556-4665
        EISSN:1556-4703
        DOI:10.1145/2872308
        Issue’s Table of Contents

        Copyright © 2016 ACM

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 13 January 2016
        • Accepted: 1 June 2015
        • Revised: 1 March 2015
        • Received: 1 October 2010
        Published in taas Volume 10, Issue 4

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article
        • Research
        • Refereed

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader
      About Cookies On This Site

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

      Learn more

      Got it!