Matteo Ferroni
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Abstract
Autonomicity is a golden feature when dealing with a high level of complexity. This complexity can be tackled partitioning huge systems in small autonomous modules, i.e., agents. Each agent then needs to be capable of extracting knowledge from its environment and to learn from it, in order to fulfill its goals: this could not be achieved without proper modeling techniques that allow each agent to gaze beyond its sensors. Unfortunately, the simplicity of agents and the complexity of modeling do not fit together, thus demanding for a third party to bridge the gap.
Given the opportunities in the field, the main contributions of this work are twofold: (1) we propose a general methodology to model resource consumption trends and (2) we implemented it into MARC, a Cloud-service platform that produces Models-as-a-Service, thus relieving self-aware agents from the burden of building their custom modeling framework. In order to validate the proposed methodology, we set up a custom simulator to generate a wide spectrum of controlled traces: this allowed us to verify the correctness of our framework from a general and comprehensive point of view.
- Aijun An, Christine Chan, Ning Shan, Nick Cercone, and Wojciech Ziarko. 1997. Applying knowledge discovery to predict water-supply consumption. IEEE Expert 12, 4 (1997), 72--78. Google Scholar
Digital Library
- Apache. 2016. Akka Framework. Retrieved from http://akka.io.Google Scholar
- Gaurav Banga, Peter Druschel, and Jeffrey C. Mogul. 1999. Resource containers: A new facility for resource management in server systems. In Proceedings of OSDI, Vol. 99. 45--58.Google Scholar
- Andreas Bergen, Nina Taherimakhsousi, and Hausi A. Müller. 2015. Adaptive management of energy consumption using adaptive runtime models. In Proceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems. IEEE Press, 120--126. Google ScholarDigital Library
- W. Lloyd Bircher and Lizy K. John. 2007. Complete system power estimation: A trickle-down approach based on performance events. In Proceedings of the IEEE International Symposium on Performance Analysis of Systems 8 Software (ISPASS’07). IEEE, 158--168. Google ScholarCross Ref
- Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag New York, Inc.Google Scholar
- Sergio Bittanti. 2002. Teoria Della Predizione e Del Filtraggio. Pitagora.Google Scholar
- Andrea Cazzola. 2014. MModel: Automatic Generation of Mobile Devices Power Models Based on User Provided Data. Master’s thesis. Politecnico di Milano.Google Scholar
- EC-European Commission and others. 2011. Roadmap to a resource efficient Europe. In COM (2011). 571. http://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:52011DC05718from=EN.Google Scholar
- Andrea Corna, Andrea Damiani, Matteo Ferroni, Alessandro Antonio Nacci, Donatella Sciuto, and Marco Domenico Santambrogio. 2015. OpenMPower: An open and accessible database about real world mobile devices. In Proceedings of the 2015 IEEE 13th International Conference on Embedded and Ubiquitous Computing (EUC). IEEE, 183--187. Google ScholarDigital Library
- George F. Coulouris, Jean Dollimore, and Tim Kindberg. 2005. Distributed Systems: Concepts and Design. Pearson Education.Google ScholarDigital Library
- Manoranjan Dash and Huan Liu. 1997. Feature selection for classification. Intelligent Data Analysis 1, 3 (1997), 131--156. Google ScholarDigital Library
- Carla Schlatter Ellis. 1999. The case for higher-level power management. In Proceedings of the 7th Workshop on Hot Topics in Operating Systems. IEEE, 162--167. Google ScholarCross Ref
- Naeem Esfahani, Eric Yuan, Kyle R. Canavera, and Sam Malek. 2016. Inferring software component interaction dependencies for adaptation support. ACM Transactions on Autonomous and Adaptive Systems (TAAS) 10, 4 (2016), 26.Google Scholar
- Koli Fatai, Les Oxley, and F. G. Scrimgeour. 2004. Modelling the causal relationship between energy consumption and GDP in New Zealand, Australia, India, Indonesia, The Philippines and Thailand. Mathematics and Computers in Simulation 64, 3 (2004), 431--445. Google ScholarCross Ref
- Matteo Ferroni and Andrea Cazzola. 2013. Mpower: On How to Effectively Predict the Time to Live for Mobile Devices. Master’s thesis. Politecnico di Milano.Google ScholarDigital Library
- Matteo Ferroni, Andrea Cazzola, Domenico Matteo, Alessandro Antonio Nacci, Donatella Sciuto, and Marco Domenico Santambrogio. 2013. MPower: Gain back your android battery life!. In Proceedings of the 2013 ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication. ACM, 171--174. Google ScholarDigital Library
- Roy Thomas Fielding. 2000. Architectural Styles and the Design of Network-based Software Architectures. Ph.D. Dissertation. University of California, Irvine.Google Scholar
- Jason Flinn and Mahadev Satyanarayanan. 1999. Powerscope: A tool for profiling the energy usage of mobile applications. In Proceedings of the 2nd IEEE Workshop on Mobile Computing Systems and Applications (WMCSA’99). IEEE, 2--10. Google ScholarCross Ref
- Jason Flinn and Mahadev Satyanarayanan. 2004. Managing battery lifetime with energy-aware adaptation. ACM Transactions on Computer Systems (TOCS) 22, 2 (2004), 137--179. Google ScholarDigital Library
- Jesús García-galán, Liliana Pasquale, Pablo Trinidad, and Antonio Ruiz-Cortés. 2016. User-centric adaptation analysis of multi-tenant services. ACM Transactions on Autonomous and Adaptive Systems (TAAS) 10, 4 (2016), 24.Google Scholar
- Pamela S. Haines, Barry M. Popkin, and David K. Guilkey. 1988. Modeling food consumption decisions as a two-step process. American Journal of Agricultural Economics 70, 3 (1988), 543--552. Google ScholarCross Ref
- Nikolas Roman Herbst, Samuel Kounev, Andreas Weber, and Henning Groenda. 2015. BUNGEE: An elasticity benchmark for self-adaptive IaaS cloud environments. In Proceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems. IEEE Press, 46--56. Google ScholarDigital Library
- Aman Kansal, Feng Zhao, Jie Liu, Nupur Kothari, and Arka A. Bhattacharya. 2010. Virtual machine power metering and provisioning. In Proceedings of the 1st ACM Symposium on Cloud Computing. ACM, 39--50. Google ScholarDigital Library
- Richard M. Karp. 1972. Reducibility Among Combinatorial Problems. Springer. Google ScholarCross Ref
- Jeffrey O. Kephart and David M. Chess. 2003. The vision of autonomic computing. Computer 36, 1 (2003), 41--50. Google ScholarDigital Library
- Igor Kononenko. 1994. Estimating attributes: Analysis and extensions of RELIEF. In Machine Learning: ECML-94. Springer, 171--182. Google ScholarDigital Library
- Chao Li, Rui Wang, Depei Qian, and Tao Li. 2016. Managing server clusters on renewable energy mix. ACM Transactions on Autonomous and Adaptive Systems (TAAS) 11, 1 (2016), 1.Google ScholarDigital Library
- Pattie Maes. 1993. Modeling adaptive autonomous agents. Artificial Life 1, 1_2 (1993), 135--162.Google ScholarDigital Library
- P. C. Mahalanobis. 1936. On the generalised distance in statistics. In Proceedings National Institute of Science, India, Vol. 2. 49--55.Google Scholar
- Ali Yadavar Nikravesh, Samuel A. Ajila, and Chung-Horng Lung. 2015. Towards an autonomic auto-scaling prediction system for cloud resource provisioning. In Proceedings of the 2015 IEEE/ACM 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS). IEEE, 35--45. Google ScholarDigital Library
- Brian Noble, Morgan Price, and Mahadev Satyanarayanan. 1995. A programming interface for application-aware adaptation in mobile computing. Computing Systems 8, 4 (1995), 345--363. Google ScholarCross Ref
- Martin Odersky, Lex Spoon, and Bill Venners. 2008. Programming in Scala. Artima Inc.Google Scholar
- Jon Pretty. 2014. Rapture.Retrieved from http://rapture.io.Google Scholar
- Android Open Source Project. 2008. Android.Retrieved from https://www.android.com.Google Scholar
- Redislab. 2009. Redis.Retrieved from http://redis.io.Google Scholar
- Lucia A. Reisch and John Thgersen. 2015. Handbook of Research on Sustainable Consumption. Edward Elgar Publishing. Google ScholarCross Ref
- Murray Rosenblatt and others. 1956. Remarks on some nonparametric estimates of a density function. The Annals of Mathematical Statistics 27, 3 (1956), 832--837. Google ScholarCross Ref
- Stephen M. Rumble, Ryan Stutsman, Philip Levis, David Mazières, and Nickolai Zeldovich. 2010. Apprehending joule thieves with cinder. ACM SIGCOMM Computer Communication Review 40, 1 (2010), 106--111. Google ScholarDigital Library
- Ibrahim Takouna, Wesam Dawoud, and Christoph Meinel. 2011. Accurate mutlicore processor power models for power-aware resource management. In Proceedings of the 2011 IEEE 9th International Conference on Dependable, Autonomic and Secure Computing (DASC). IEEE, 419--426. Google ScholarDigital Library
- Andrew S. Tanenbaum and Maarten Van Steen. 2002. Distributed Systems: Principles and Paradigms. Vol. 2. Prentice Hall, Englewood Cliffs.Google Scholar
- Geoffrey K. F. Tso and Kelvin K. W. Yau. 2007. Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks. Energy 32, 9 (2007), 1761--1768. Google Scholar
- Narseo Vallina-Rodriguez and Jon Crowcroft. 2011. ErdOS: Achieving energy savings in mobile OS. In Proceedings of the 6th International Workshop on MobiArch. ACM, 37--42. Google ScholarDigital Library
- Narseo Vallina-Rodriguez and Jon Crowcroft. 2013. Energy management techniques in modern mobile handsets. IEEE Communications Surveys 8 Tutorials 15, 1 (2013), 179--198.Google Scholar
- Jóakim von Kistowski, Nikolas Herbst, Daniel Zoller, Samuel Kounev, and Andreas Hotho. 2015. Modeling and extracting load intensity profiles. In Proceedings of the 2015 IEEE/ACM 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS). IEEE, 109--119.Google ScholarDigital Library
- Micha Vor Dem Berge, Georges Da Costa, Mateusz Jarus, Ariel Oleksiak, Wojciech Piatek, and Eugen Volk. 2014. Modeling data center building blocks for energy-efficiency and thermal simulations. In Energy-Efficient Data Centers. Springer, 66--82. Google ScholarCross Ref
- Hailong Yang, Qi Zhao, Zhongzhi Luan, and Depei Qian. 2014. iMeter: An integrated VM power model based on performance profiling. Future Generation Computer Systems 36 (2014), 267--286. Google ScholarCross Ref
- F. Zappa. 2008. Elettronica. Semiconduttori, Diodi E Transistori, Amplificatori, Convertitori DAC e ADC. Esculapio.Google Scholar
- Parisa Zoghi, Mark Shtern, Marin Litoiu, and Hamoun Ghanbari. 2016. Designing adaptive applications deployed on cloud environments. ACM Transactions on Autonomous and Adaptive Systems (TAAS) 10, 4 (2016), 25.Google ScholarDigital Library
Index Terms
- MARC: A Resource Consumption Modeling Service for Self-Aware Autonomous Agents
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