Continual Verification of Non-Functional Properties in Cloud-Based Systems

Cloud-based systems are used to deliver business-critical and safety-critical services in domains ranging from e-commerce and e-government to finance and healthcare. Many of these systems must comply with strict non-functional requirements while evolving in order to adapt to changing workloads and environments. To achieve this compliance, formal techniques traditionally employed to verify the non-functional properties of critical systems at design time must also be used during their operation. We describe how a formal technique called runtime quantitative verification can be used to verify cloud-based systems continually.

[1]  Carlo Ghezzi,et al.  Run-time efficient probabilistic model checking , 2011, 2011 33rd International Conference on Software Engineering (ICSE).

[2]  Marin Litoiu,et al.  Performance model driven QoS guarantees and optimization in clouds , 2009, 2009 ICSE Workshop on Software Engineering Challenges of Cloud Computing.

[3]  Radu Calinescu,et al.  Formal methods for the development and verification of autonomic IT systems , 2011 .

[4]  Carlo Ghezzi,et al.  Self-adaptive software needs quantitative verification at runtime , 2012, CACM.

[5]  Radu Calinescu,et al.  CADS*: Computer-Aided Development of Self-* Systems , 2009, FASE.

[6]  Radu Calinescu,et al.  Formal Methods @ Runtime , 2010, Monterey Workshop.

[7]  Marsha Chechik,et al.  Fundamental Approaches to Software Engineering , 2009, Lecture Notes in Computer Science.

[8]  Marta Z. Kwiatkowska,et al.  Automated Verification Techniques for Probabilistic Systems , 2011, SFM.

[9]  Hongyang Qu,et al.  Assume-Guarantee Verification for Probabilistic Systems , 2010, TACAS.

[10]  Valérie Issarny,et al.  Formal Methods for Eternal Networked Software Systems , 2011, Lecture Notes in Computer Science.

[11]  Carlo Ghezzi,et al.  Model evolution by run-time parameter adaptation , 2009, 2009 IEEE 31st International Conference on Software Engineering.

[12]  Radu Calinescu,et al.  log2cloud: log-based prediction of cost-performance trade-offs for cloud deployments , 2013, SAC '13.

[13]  Richard F. Paige,et al.  Taming EMF and GMF using model transformation , 2010, MODELS'10.

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

[15]  Lars Grunske,et al.  Specification patterns for probabilistic quality properties , 2008, 2008 ACM/IEEE 30th International Conference on Software Engineering.

[16]  Joost-Pieter Katoen,et al.  A Markov reward model checker , 2005, Second International Conference on the Quantitative Evaluation of Systems (QEST'05).

[17]  Toshiaki Aoki,et al.  Evaluation of Operational Vulnerability in Cloud Service Management Using Model Checking , 2013, 2013 IEEE Seventh International Symposium on Service-Oriented System Engineering.

[18]  Radu Calinescu,et al.  Emerging Techniques for the Engineering of Self-Adaptive High-Integrity Software , 2013, Assurances for Self-Adaptive Systems.

[19]  Radu Calinescu,et al.  Using Intelligent Proxies to Develop Self-Adaptive Service-Based Systems , 2013, 2013 International Symposium on Theoretical Aspects of Software Engineering.

[20]  Radu Calinescu When the requirements for adaptation and high integrity meet , 2011, ASAS '11.

[21]  Radu Calinescu,et al.  Foundations of Computer Software. Modeling, Development, and Verification of Adaptive Systems , 2010, Lecture Notes in Computer Science.

[22]  Marta Z. Kwiatkowska Quantitative verification: models techniques and tools , 2007, ESEC-FSE '07.

[23]  Radu Calinescu,et al.  Using quantitative analysis to implement autonomic IT systems , 2009, 2009 IEEE 31st International Conference on Software Engineering.

[24]  Marta Z. Kwiatkowska,et al.  Stochastic Model Checking , 2007, SFM.

[25]  Radu Calinescu,et al.  Compositional Reverification of Probabilistic Safety Properties for Large-Scale Complex IT Systems , 2012, Monterey Workshop.

[26]  Calton Pu,et al.  Mistral: Dynamically Managing Power, Performance, and Adaptation Cost in Cloud Infrastructures , 2010, 2010 IEEE 30th International Conference on Distributed Computing Systems.

[27]  Radu Calinescu,et al.  Dynamic QoS Management and Optimization in Service-Based Systems , 2011, IEEE Transactions on Software Engineering.

[28]  Marta Z. Kwiatkowska,et al.  PRISM 4.0: Verification of Probabilistic Real-Time Systems , 2011, CAV.

[29]  Radu Calinescu,et al.  Developing self-verifying service-based systems , 2013, 2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE).

[30]  Shinji Kikuchi,et al.  Configuration Procedure Synthesis for Complex Systems Using Model Finder , 2010, 2010 15th IEEE International Conference on Engineering of Complex Computer Systems.

[31]  Raffaela Mirandola,et al.  QoS and energy management with Petri nets: A self-adaptive framework , 2012, J. Syst. Softw..

[32]  Joost-Pieter Katoen Model Checking Meets Probability: A Gentle Introduction , 2013, Engineering Dependable Software Systems.

[33]  Radu Calinescu,et al.  Using observation ageing to improve markovian model learning in QoS engineering , 2011, ICPE '11.

[34]  Sakai Hiroshi,et al.  Use cases and functional requirements for inter-cloud computing , 2010 .

[35]  Radu Calinescu,et al.  Towards a Model-Driven Solution to the Vendor Lock-In Problem in Cloud Computing , 2013, 2013 IEEE 5th International Conference on Cloud Computing Technology and Science.

[36]  Kousha Etessami,et al.  Multi-objective Model Checking of Markov Decision Processes , 2007, TACAS.

[37]  Radu Calinescu,et al.  Using Continual Verication to Automate Service Selection in Service-Based Systems , 2013 .

[38]  Håkan L. S. Younes Ymer: A Statistical Model Checker , 2005, CAV.

[39]  Carlo Ghezzi,et al.  A formal approach to adaptive software: continuous assurance of non-functional requirements , 2011, Formal Aspects of Computing.

[40]  Radu Calinescu,et al.  An incremental verification framework for component-based software systems , 2013, CBSE '13.