User-Centric Adaptation Analysis of Multi-Tenant Services

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.

[1]  Vincenzo Grassi,et al.  MOSES: A Framework for QoS Driven Runtime Adaptation of Service-Oriented Systems , 2012, IEEE Transactions on Software Engineering.

[2]  Sebastian Erdweg,et al.  Abstract Features in Feature Modeling , 2011, 2011 15th International Software Product Line Conference.

[3]  Daniel S. Weld,et al.  Exploring the design space for adaptive graphical user interfaces , 2006, AVI '06.

[4]  Jasbir S. Arora,et al.  Survey of multi-objective optimization methods for engineering , 2004 .

[5]  Sergio Segura,et al.  BeTTy: benchmarking and testing on the automated analysis of feature models , 2012, VaMoS.

[6]  Jacob Stein,et al.  Preference-based feature model configuration with multiple stakeholders , 2014, SPLC.

[7]  Cor-Paul Bezemer,et al.  Enabling multi-tenancy: An industrial experience report , 2010, 2010 IEEE International Conference on Software Maintenance.

[8]  Roger B. Myerson,et al.  Game theory - Analysis of Conflict , 1991 .

[9]  Uwe Aßmann,et al.  Towards modeling a variable architecture for multi-tenant SaaS-applications , 2012, VaMoS.

[10]  Klaus Pohl,et al.  Towards exploiting the full adaptation potential of cloud applications , 2014, PESOS 2014.

[11]  David Ruiz,et al.  Priority-Based Human Resource Allocation in Business Processes , 2013, ICSOC.

[12]  Vicente Pelechano,et al.  A NFR-Based Framework for User-Centered Adaptation , 2012, ER.

[13]  Jeffrey O. Kephart,et al.  The Vision of Autonomic Computing , 2003, Computer.

[14]  Yinglin Wang,et al.  A genetic algorithm for optimized feature selection with resource constraints in software product lines , 2011, J. Syst. Softw..

[15]  Hamidreza Eskandari,et al.  FastPGA: A Dynamic Population Sizing Approach for Solving Expensive Multiobjective Optimization Problems , 2006, EMO.

[16]  Bashar Nuseibeh,et al.  Social Adaptation - When Software Gives Users a Voice , 2012, ENASE.

[17]  Kyo Chul Kang,et al.  Feature-Oriented Domain Analysis (FODA) Feasibility Study , 1990 .

[18]  W. Lam.  Governing the Commons , 2010 .

[19]  Omer F. Rana,et al.  CONCURRENCYANDCOMPUTATION : PRACTICE AND EXPERIENCE Towards autonomic management for Cloud services based upon volunteered resources , 2011 .

[20]  Gordon S. Blair,et al.  Dynamically Adaptive Systems are Product Lines too: Using Model-Driven Techniques to Capture Dynamic Variability of Adaptive Systems , 2008, SPLC.

[21]  Antonio J. Nebro,et al.  jMetal: A Java framework for multi-objective optimization , 2011, Adv. Eng. Softw..

[22]  Wendy Ju,et al.  The Design of Implicit Interactions: Making Interactive Systems Less Obnoxious , 2008, Design Issues.

[23]  Joseph S. Valacich,et al.  The Effects of Interruptions, Task Complexity, and Information Presentation on Computer-Supported Decision-Making Performance , 2003, Decis. Sci..

[24]  Antonio Ruiz Cortés,et al.  Migrating to the Cloud - A Software Product Line based Analysis , 2013, CLOSER.

[25]  Dídac Busquets,et al.  Self-Organising Common-Pool Resource Allocation and Canons of Distributive Justice , 2012, 2012 IEEE Sixth International Conference on Self-Adaptive and Self-Organizing Systems.

[26]  Liliana Pasquale,et al.  User-centric adaptation of multi-tenant services: preference-based analysis for service reconfiguration , 2014, SEAMS 2014.

[27]  Tim Menzies,et al.  Scalable product line configuration: A straw to break the camel's back , 2013, 2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE).

[28]  Antonio F. Gómez-Skarmeta,et al.  Semantic-aware multi-tenancy authorization system for cloud architectures , 2014, Future Gener. Comput. Syst..

[29]  Filip De Turck,et al.  User subscription-based resource management for Desktop-as-a-Service platforms , 2014, The Journal of Supercomputing.

[30]  Marija Mikic-Rakic,et al.  An Extensible Framework for Improving a Distributed Software System's Deployment Architecture , 2012, IEEE Transactions on Software Engineering.

[31]  Siobhán Clarke,et al.  Self-adaptation with End-User Preferences: Using Run-Time Models and Constraint Solving , 2013, MoDELS.

[32]  Rizos Sakellariou,et al.  Adaptive resource configuration for Cloud infrastructure management , 2013, Future Gener. Comput. Syst..

[33]  Frank Leymann,et al.  Variability modeling to support customization and deployment of multi-tenant-aware Software as a Service applications , 2009, 2009 ICSE Workshop on Principles of Engineering Service Oriented Systems.

[34]  Luciano Baresi,et al.  Service-Oriented Dynamic Software Product Lines , 2012, Computer.

[35]  Anupriya Ankolekar,et al.  Preference-based selection of highly configurable web services , 2007, WWW '07.

[36]  Antonio Ruiz Cortés,et al.  Multi-user variability configuration: A game theoretic approach , 2013, 2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE).

[37]  Sergio Segura,et al.  The Drupal framework: a case study to evaluate variability testing techniques , 2014, VaMoS '14.

[38]  David Ruiz,et al.  Integrating semantic Web services ranking mechanisms using a common preference model , 2013, Knowl. Based Syst..

[39]  Sergio Segura,et al.  Automated analysis of feature models 20 years later: A literature review , 2010, Inf. Syst..

[40]  Douglas C. Schmidt,et al.  Automated diagnosis of feature model configurations , 2010, J. Syst. Softw..

[41]  Malte Lochau,et al.  Dynamic configuration management of cloud-based applications , 2012, SPLC '12.

[42]  Naixue Xiong,et al.  A game-theoretic method of fair resource allocation for cloud computing services , 2010, The Journal of Supercomputing.

[43]  Rami Bahsoon,et al.  A decentralized self-adaptation mechanism for service-based applications in the cloud , 2013, IEEE Transactions on Software Engineering.

[44]  Jun Han,et al.  Sharing with a Difference: Realizing Service-Based SaaS Applications with Runtime Sharing and Variation in Dynamic Software Product Lines , 2013, 2013 IEEE International Conference on Services Computing.

[45]  Elizabeth Papadopoulou,et al.  Learning user preferences for adaptive pervasive environments: An incremental and temporal approach , 2013, TAAS.

[46]  Schahram Dustdar,et al.  Model-based Adaptation of Cloud Computing Applications , 2016, MODELSWARD.

[47]  Antonio Ruiz Cortés,et al.  FAMA Framework , 2008, 2008 12th International Software Product Line Conference.

[48]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[49]  Marek Hatala,et al.  Toward automated feature model configuration with optimizing non-functional requirements , 2014, Inf. Softw. Technol..