skip to main content
10.1145/2513534.2513543acmotherconferencesArticle/Chapter ViewAbstractPublication PagesnordicloudConference Proceedingsconference-collections
research-article

A vision for a stochastic reasoner for autonomic cloud deployment

Published: 02 September 2013 Publication History

Abstract

Applications deployed in multi-clouds will face issues like where to deploy the different artefacts, how to scale the application in case of performance problems, and how to adapt the application deployment. For complex applications it may be difficult to find manually the best allocation of the artefacts on the available infrastructures. This paper presents a vision for an autonomic deployment system. In particular, it details the architecture of a learning automata based reasoning component envisioned to be able to provide feasible allocations and discusses the research challenges originating from this approach.

References

[1]
A. S. Poznyak and K. Najim. Learning Automata and Stochastic Optimization, volume 225 of Lecture Notes in Control and Information Sciences. Springer Berlin Heidelberg, 1997.
[2]
Aharon Ben-Tal, Laurent El Ghaoui, and Arkadii Semenovich Nemirovskii. Robust optimization. Princeton Series in Applied Mathematics. Princeton University Press, Princeton, 2009.
[3]
Antonis Bikakis, Theodore Patkos, Grigoris Antoniou, and Dimitris Plexousakis. A survey of semantics-based approaches for context reasoning in ambient intelligence. In Max Mühlhäuser, Alois Ferscha, and Erwin Aitenbichler, editors, Constructing Ambient Intelligence: Proceedings of the AmI 2007 Workshops, volume 11 of Communications in Computer and Information Science, pages 14--23, Conference location: Darmstadt, Germany, Nov. 2007. Springer Berlin Heidelberg.
[4]
Ben Goertzel, Matthew Iklé, Izabela Freire Goertzel, and Ari Heljakka. Probabilistic Logic Networks: A Comprehensive Framework for Uncertain Inference. Springer, 2008.
[5]
Bernhard Korte and Jens Vygen. Combinatorial Optimization: Theory and Algorithms, volume 21 of Algorithms and Combinatorics. Springer, Berlin Heidelberg, 4th edition, 2008.
[6]
Brice Morin, Olivier Barais, Jean-Marc Jézéquel, Franck Fleurey, and Arnor Solberg. [email protected] to support dynamic adaptation. Computer, 42(10):44--51, 2009.
[7]
Claudio Bettini, Oliver Brdiczka, Karen Henricksen, Jadwiga Indulska, Daniela Nicklas, Anand Ranganathan, and Daniele Riboni. A survey of context modelling and reasoning techniques. Pervasive and Mobile Computing, 6(2):161--180, Apr. 2010.
[8]
Costas P. Pappis and Constantinos I. Siettos. Fuzzy reasoning. In Edmund K. Burke and Graham Kendall, editors, Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, pages 437--474. Springer, 2005. Chapter 15.
[9]
David G. Luenberger and Yinyu Ye. Linear and Nonlinear Programming. Springer, 3rd edition, 2008.
[10]
Dimitris Bertsimas and Melvyn Sim. Robust discrete optimization and network flows. Mathematical Programming, 98(1-3):49--71, Sept. 2003.
[11]
Franck Fleurey and Arnor Solberg. A domain specific modeling language supporting specification, simulation and execution of dynamic adaptive systems. In Andy Schürr and Bran Selic, editors, Model Driven Engineering Languages and Systems: Proceedings of the 12 International conference (MODELS 2009), volume 5795 of Lecture Notes in Computer Science, pages 606--621, Conference location: Denver, Colorado, USA, Oct. 2009. Springer.
[12]
Geir Horn and B. John Oommen. Solving multiconstraint assignment problems using learning automata. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 40(1):6--18, Feb. 2010.
[13]
Gordon Blair, Nelly Bencomo, and Robert B. France. [email protected]. Computer, 42(10):22--27, 2009.
[14]
Gregory L. McArthur. Reasoning about knowledge and belief: a survey. Computational Intelligence, 4(3):223--243, 1988.
[15]
Jacqueline Floch, Svein Hallsteinsen, Erlend Stav, Frank Eliassen, Ketil Lund, and Eli Gjørven. Using architecture models for runtime adaptability. IEEE Software, 23(2):62--70, 2006.
[16]
James C. Spall. Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control. Wiley, Apr. 2003.
[17]
Jinghai Rao and Xiaomeng Su. A survey of automated web service composition methods. In Jorge Cardoso and Amit Sheth, editors, Semantic Web Services and Web Process Composition: Proceedings of the first International Workshop (SWSWPC 2004), volume 3387 of Lecture Notes in Computer Science, pages 43--54, Conference location: San Diego, CA, USA, July 2004. Springer Berlin Heidelberg.
[18]
Keith Jeffery, Geir Horn, and Lutz Schubert. A vision for better cloud applications. In Danilo Ardagna and Lutz Schubert, editors, Proceedings of the 2013 international workshop on Multi-cloud applications and federated clouds (MultiCloud'13), MultiCloud '13, page 7--12, Conference location: Prague, Czech Republic, Apr. 2013. ACM.
[19]
Kumpati S. Narendra and Mandayam A. L. Thathachar. Learning Automata: An Introduction. Prentice Hall, May 1989.
[20]
Kurt Geihs, Paolo Barone, Frank Eliassen, Jacqueline Floch, Rolf Fricke, Eli Gjørven, Svein Hallsteinsen, Geir Horn, Mohammad U. Khan, Alessandro Mamelli, George A. Papadopoulos, Nearchos Paspallis, Roland Reichle, and Erlend Stav. A comprehensive solution for application-level adaptation. Software: Practice and Experience, 39(4):385--422, 2009.
[21]
Laurence A. Wolsey. Mixed integer programming. In Wiley Encyclopedia of Computer Science and Engineering, pages 1--10. John Wiley & Sons, Inc., 2008.
[22]
Livia Predoiu and Heiner Stuckenschmidt. Probabilistic models for the semantic web: A survey. In Arthur Tatnall, editor, Web Technologies: Concepts, Methodologies, Tools, and Applications, pages 1896--1928. IGI Global, July 2013. Chapter 102.
[23]
Mandayam A. L. Thathachar and P. S. Sastry. Networks of Learning Automata: Techniques for Online Stochastic Optimization. Kluwer Academic, Boston, MA, USA, 1 edition, 2004.
[24]
Norio Baba. On the learning behaviors of variable-structure stochastic automaton in the general n-teacher environment. IEEE Transactions on Systems, Man, and Cybernetics, SMC-13(3):224--231, Mar. 1983.
[25]
Pascal Van Hentenryck and Russell Bent. Online Stochastic Combinatorial Optimization. The MIT Press, 2009.
[26]
Pei Wang. Non-Axiomatic Logic: A Model of Intelligent Reasoning. World Scientific, July 2013.
[27]
Peter W. Glynn and Donald L. Iglehart. Importance sampling for stochastic simulations. Management Science, 35(11):1367--1392, Nov. 1989.
[28]
Pierre-Charles David and Thomas Ledoux. An aspect-oriented approach for developing self-adaptive fractal components. In Welf Löwe and Mario Südholt, editors, Software Composition: Proceedings of the 5th International Symposium, (SC 2006), volume 4089 of Lecture Notes in Computer Science, pages 82--97, Conference location: Vienna, Austria, Mar. 2006. Springer.
[29]
Richard S. Sutton and Andrew G. Barto. Reinforcement learning, volume 9. MIT Press, Boston, MA, USA, 1998.
[30]
Svein Hallsteinsen, Kurt Geihs, Nearchos Paspallis, Frank Eliassen, Geir Horn, Jorge Lorenzo, Alessandro Mamelli, and George A. Papadopoulos. A development framework and methodology for self-adapting applications in ubiquitous computing environments. Journal of Systems and Software, 85(12):2840--2859, Dec. 2012.
[31]
T. P. I. Ahamed, V. S. Borkar, and S. Juneja. Adaptive importance sampling technique for markov chains using stochastic approximation. Operations Research, 54(3):489--504, May 2006.

Cited By

View all
  • (2023)Online Deployment Algorithms for Microservice Systems With Complex DependenciesIEEE Transactions on Cloud Computing10.1109/TCC.2022.316168411:2(1746-1763)Online publication date: 1-Apr-2023
  • (2020)Evolving Adaptation Rules at Runtime for Multi-cloud ApplicationsCloud Computing and Services Science10.1007/978-3-030-49432-2_11(223-246)Online publication date: 4-Jun-2020
  • (2019)MELODIC: Selection and Integration of Open Source to Build an Autonomic Cross-Cloud Deployment PlatformSoftware Technology: Methods and Tools10.1007/978-3-030-29852-4_31(364-377)Online publication date: 8-Oct-2019
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
NordiCloud '13: Proceedings of the Second Nordic Symposium on Cloud Computing & Internet Technologies
September 2013
88 pages
ISBN:9781450323079
DOI:10.1145/2513534
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 ACM 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: 02 September 2013

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. combinatorial optimization
  2. learning automata
  3. reinforcement learning
  4. stochastic search

Qualifiers

  • Research-article

Funding Sources

Conference

NordiCloud '13

Acceptance Rates

NordiCloud '13 Paper Acceptance Rate 9 of 15 submissions, 60%;
Overall Acceptance Rate 9 of 15 submissions, 60%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)2
  • Downloads (Last 6 weeks)0
Reflects downloads up to 20 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2023)Online Deployment Algorithms for Microservice Systems With Complex DependenciesIEEE Transactions on Cloud Computing10.1109/TCC.2022.316168411:2(1746-1763)Online publication date: 1-Apr-2023
  • (2020)Evolving Adaptation Rules at Runtime for Multi-cloud ApplicationsCloud Computing and Services Science10.1007/978-3-030-49432-2_11(223-246)Online publication date: 4-Jun-2020
  • (2019)MELODIC: Selection and Integration of Open Source to Build an Autonomic Cross-Cloud Deployment PlatformSoftware Technology: Methods and Tools10.1007/978-3-030-29852-4_31(364-377)Online publication date: 8-Oct-2019
  • (2018)MELODIC: Utility Based Cross Cloud Deployment Optimisation2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA)10.1109/WAINA.2018.00112(360-367)Online publication date: May-2018
  • (2018)IaaS Service Selection RevisitedService-Oriented and Cloud Computing10.1007/978-3-319-99819-0_13(170-184)Online publication date: 31-Aug-2018
  • (2016)Towards Knowledge-Based Assisted IaaS Selection2016 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)10.1109/CloudCom.2016.0073(431-439)Online publication date: Dec-2016
  • (2015)Towards a Model-Based Execution-Ware for Deploying Multi-cloud ApplicationsAdvances in Service-Oriented and Cloud Computing10.1007/978-3-319-14886-1_13(124-138)Online publication date: 28-Feb-2015
  • (2014)Cloud-Based Tasking, Collection, Processing, Exploitation, and Dissemination in a Case-Based Reasoning SystemIntegration of Reusable Systems10.1007/978-3-319-04717-1_1(1-26)Online publication date: 18-Feb-2014

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media