ABSTRACT
Adaptive computing systems automatically monitor their behavior and dynamically adjust their own configuration parameters—or knobs—to ensure that user goals are met despite unpredictable external disturbances to the system. A major limitation of prior adaptation frameworks is that their internal adaptation logic is implemented for a specific, narrow set of goals and knobs, which impedes the development of complex adaptive systems that must meet different goals using different sets of knobs for different deployments, or even change goals during one deployment.
To overcome this limitation we propose GOAL, an adaptation framework distinguished by its virtualized adaptation logic implemented independently of any specific goals or knobs. GOAL supports this logic with a programming interface allowing users to define and manipulate a wide range of goals and knobs within a running program. We demonstrate GOAL’s benefits by using it re-implement seven different adaptive systems from the literature, each of which has a different set of goals and knobs. We show GOAL’s general approach meets goals as well as prior approaches designed for specific goals and knobs. In dynamic scenarios where the goals and knobs are modified at runtime, GOAL achieves 93.7
- [n.d.]. Arlo: Wire-Free HD and HDR Smart Home Security Cameras. https://www.arlo.com/en-us/default.aspx Google Scholar
- [n.d.]. Tesseract OCR. https://opensource.google/projects/tesseract Google Scholar
- Frederico Alvares, Gwenaël Delaval, Eric Rutten, and Lionel Seinturier. 2017. Language Support for Modular Autonomic Managers in Reconfigurable Software Components. In 2017 IEEE International Conference on Autonomic Computing (ICAC). 271–278. https://doi.org/10.1109/ICAC.2017.48 Google ScholarCross Ref
- Jason Ansel, Maciej Pacula, Yee Lok Wong, Cy Chan, Marek Olszewski, Una-May O’Reilly, and Saman Amarasinghe. 2012. Siblingrivalry: Online Autotuning Through Local Competitions. In Proceedings of the 2012 International Conference on Compilers, Architectures and Synthesis for Embedded Systems (CASES ’12). ACM, New York, NY, USA. 91–100. isbn:978-1-4503-1424-4 https://doi.org/10.1145/2380403.2380425 Google ScholarDigital Library
- Apple. 2019. Swift. https://developer.apple.com/swift/ Google Scholar
- Woongki Baek and Trishul M. Chilimbi. 2010. Green: A Framework for Supporting Energy-conscious Programming Using Controlled Approximation. In Proceedings of the 31st ACM Conference on Programming Language Design and Implementation (PLDI ’10). ACM, New York, NY, USA. 198–209. isbn:978-1-4503-0019-3 https://doi.org/10.1145/1806596.1806620 Google ScholarDigital Library
- Baochun Li and K. Nahrstedt. 1999. A control-based middleware framework for quality-of-service adaptations. IEEE Journal on Selected Areas in Communications, 17, 9 (1999), 1632–1650. https://doi.org/10.1109/49.790486 Google ScholarDigital Library
- Saeid Barati, Ferenc A. Bartha, Swarnendu Biswas, Robert Cartwright, Adam Duracz, Donald Fussell, Henry Hoffmann, Connor Imes, Jason Miller, Nikita Mishra, Arvind, Dung Nguyen, Krishna V. Palem, Yan Pei, Keshav Pingali, Ryuichi Sai, Andrew Wright, Yao-Hsiang Yang, and Sizhuo Zhang. 2019. Proteus: Language and Runtime Support for Self-Adaptive Software Development. IEEE Software, 36, 2 (2019), 73–82. https://doi.org/10.1109/MS.2018.2884864 Google ScholarCross Ref
- Josep Ll. Berral, Íñigo Goiri, Ramón Nou, Ferran Julià, Jordi Guitart, Ricard Gavaldà, and Jordi Torres. 2010. Towards Energy-aware Scheduling in Data Centers Using Machine Learning. In Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking (e-Energy ’10). ACM, New York, NY, USA. 215–224. isbn:978-1-4503-0042-1 https://doi.org/10.1145/1791314.1791349 Google ScholarDigital Library
- James Bornholt, Todd Mytkowicz, and Kathryn S. McKinley. 2014. Uncertain<T>: A First-Order Type for Uncertain Data. In Proceedings of the 19th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS ’14). Association for Computing Machinery, New York, NY, USA. 51–66. isbn:9781450323055 https://doi.org/10.1145/2541940.2541958 Google ScholarDigital Library
- John Boyd. 1995. The Essence of Winning and Losing. Online document. https://www.danford.net/boyd/essence.htm Google Scholar
- John Raymond Boyd. 1976. Destruction and Creation. Google Scholar
- John Raymond Boyd. 1996. The Essence of Winning and Losing. Google Scholar
- Stephen P. Bradley, Arnoldo C. Hax, and Thomas L. Magnanti. 1977. Applied mathematical programming. Addison-Wesley. Google Scholar
- Anthony Canino and Yu David Liu. 2017. Proactive and Adaptive Energy-aware Programming with Mixed Typechecking. In Proceedings of the 38th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI 2017). ACM, New York, NY, USA. 217–232. isbn:978-1-4503-4988-8 https://doi.org/10.1145/3062341.3062356 Google ScholarDigital Library
- Anthony Canino, Yu David Liu, and Hidehiko Masuhara. 2018. Stochastic Energy Optimization for Mobile GPS Applications. In Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2018). ACM, New York, NY, USA. 703–713. isbn:978-1-4503-5573-5 https://doi.org/10.1145/3236024.3236076 Google ScholarDigital Library
- Liyu Cao and Howard M. Schwartz. 2004. Analysis of the Kalman filter based estimation algorithm: an orthogonal decomposition approach. Automatica, 40, 1 (2004), 5–19. issn:0005-1098 https://doi.org/10.1016/j.automatica.2003.07.011 Google ScholarDigital Library
- Jacques Carette, Oleg Kiselyov, and Chung-chieh Shan. 2009. Finally tagless, partially evaluated: Tagless staged interpreters for simpler typed languages. Journal of Functional Programming, 19, 5 (2009), 509–543. Google ScholarDigital Library
- H. Chen, M. Song, J. Song, A. Gavrilovska, and K. Schwan. 2011. HEaRS: A Hierarchical Energy-Aware Resource Scheduler for Virtualized Data Centers. In 2011 IEEE International Conference on Cluster Computing. 508–512. https://doi.org/10.1109/CLUSTER.2011.60 Google ScholarDigital Library
- Christina Delimitrou and Christos Kozyrakis. 2013. Paragon: QoS-Aware Scheduling for Heterogeneous Datacenters. SIGPLAN Not., 48, 4 (2013), March, 77–88. issn:0362-1340 https://doi.org/10.1145/2499368.2451125 Google ScholarDigital Library
- Christina Delimitrou and Christos Kozyrakis. 2014. Quasar: Resource-Efficient and QoS-Aware Cluster Management. SIGPLAN Not., 49, 4 (2014), Feb., 127–144. issn:0362-1340 https://doi.org/10.1145/2644865.2541941 Google ScholarDigital Library
- Yi Ding, Nikita Mishra, and Henry Hoffmann. 2019. Generative and Multi-Phase Learning for Computer Systems Optimization. In Proceedings of the 46th International Symposium on Computer Architecture (ISCA ’19). Association for Computing Machinery, New York, NY, USA. 39–52. isbn:9781450366694 https://doi.org/10.1145/3307650.3326633 Google ScholarDigital Library
- Yi Ding, Nikita Mishra, and Henry Hoffmann. 2019. Generative and Multi-Phase Learning for Computer Systems Optimization. In Proceedings of the 46th International Symposium on Computer Architecture (ISCA ’19). Association for Computing Machinery, New York, NY, USA. 39–52. isbn:9781450366694 https://doi.org/10.1145/3307650.3326633 Google ScholarDigital Library
- Mirko D’Angelo. 2018. Decentralized Self-Adaptive Computing at the Edge. In Proceedings of the 13th International Conference on Software Engineering for Adaptive and Self-Managing Systems (SEAMS ’18). Association for Computing Machinery, New York, NY, USA. 144–148. isbn:9781450357159 https://doi.org/10.1145/3194133.3194160 Google ScholarDigital Library
- Hadi Esmaeilzadeh, Adrian Sampson, Luis Ceze, and Doug Burger. 2012. Architecture Support for Disciplined Approximate Programming. SIGARCH Comput. Archit. News, 40, 1 (2012), March, 301–312. issn:0163-5964 https://doi.org/10.1145/2189750.2151008 Google ScholarDigital Library
- Antonio Filieri, Henry Hoffmann, and Martina Maggio. 2014. Automated Design of Self-adaptive Software with Control-theoretical Formal Guarantees. In Proceedings of the 36th International Conference on Software Engineering (ICSE 2014). ACM, New York, NY, USA. 299–310. isbn:978-1-4503-2756-5 https://doi.org/10.1145/2568225.2568272 Google ScholarDigital Library
- Antonio Filieri, Henry Hoffmann, and Martina Maggio. 2015. Automated Multi-objective Control for Self-adaptive Software Design. In Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering (ESEC/FSE 2015). ACM, New York, NY, USA. 13–24. isbn:978-1-4503-3675-8 https://doi.org/10.1145/2786805.2786833 Google ScholarDigital Library
- Antonio Filieri, Martina Maggio, Konstantinos Angelopoulos, Nicolás D’Ippolito, Ilias Gerostathopoulos, Andreas Berndt Hempel, Henry Hoffmann, Pooyan Jamshidi, Evangelia Kalyvianaki, Cristian Klein, Filip Krikava, Sasa Misailovic, Alessandro Vittorio Papadopoulos, Suprio Ray, Amir Molzam Sharifloo, Stepan Shevtsov, Mateusz Ujma, and Thomas Vogel. 2017. Control Strategies for Self-Adaptive Software Systems. TAAS, 11, 4 (2017), https://doi.org/10.1145/3024188 Google ScholarDigital Library
- Jason Flinn and M. Satyanarayanan. 1999. Energy-Aware Adaptation for Mobile Applications. In Proceedings of the Seventeenth ACM Symposium on Operating Systems Principles (SOSP ’99). Association for Computing Machinery, New York, NY, USA. 48–63. isbn:1581131402 https://doi.org/10.1145/319151.319155 Google ScholarDigital Library
- Andrei Frumusanu. 2018. Improving The Exynos 9810 Galaxy S9: Part 1. https://www.anandtech.com/show/12615/improving-exynos-9810-galaxy-s9-part-1. Google Scholar
- Andrei Frumusanu. 2018. The Samsung Galaxy S9 and S9+ Review: Exynos and Snapdragon at 960fps. https://www.anandtech.com/show/12520/the-galaxy-s9-review/5. Google Scholar
- Jim Gao. 2014. Machine Learning Applications for Data Center Optimization. Google Scholar
- Ashvin Goel, David Steere, Calton Pu, and Jonathan Walpole. 1998. SWiFT: A Feedback Control and Dynamic Reconfiguration Toolkit. Google Scholar
- J. Goodman, A. P. Dancy, and A. P. Chandrakasan. 1998. An energy/security scalable encryption processor using an embedded variable voltage DC/DC converter. IEEE Journal of Solid-State Circuits, 33, 11 (1998), Nov, 1799–1809. issn:1558-173X https://doi.org/10.1109/4.726580 Google ScholarCross Ref
- Google. 2020. Android Power Management: Battery Saver. https://developer.android.com/about/versions/pie/power##battery-saver Google Scholar
- Guang-Liang Li. 1995. An analysis of impact of workload fluctuations on performance of computer systems. In Proceedings of 1995 IEEE International Computer Performance and Dependability Symposium. 256–264. https://doi.org/10.1109/IPDS.1995.395826 Google ScholarCross Ref
- Arpan Gujarati, Reza Karimi, Safya Alzayat, Wei Hao, Antoine Kaufmann, Ymir Vigfusson, and Jonathan Mace. 2020. Serving DNNs like Clockwork: Performance Predictability from the Bottom Up. In 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20). USENIX Association, 443–462. isbn:978-1-939133-19-9 https://www.usenix.org/conference/osdi20/presentation/gujarati Google Scholar
- Mingzhe Hao, Levent Toksoz, Nanqinqin Li, Edward Edberg Halim, Henry Hoffmann, and Haryadi S. Gunawi. 2020. LinnOS: Predictability on Unpredictable Flash Storage with a Light Neural Network. In 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20). USENIX Association, 173–190. isbn:978-1-939133-19-9 https://www.usenix.org/conference/osdi20/presentation/hao Google Scholar
- Joseph L. Hellerstein. 2009. Engineering Autonomic Systems. In Proceedings of the 6th International Conference on Autonomic Computing (ICAC ’09). Association for Computing Machinery, New York, NY, USA. 75–76. isbn:9781605585642 https://doi.org/10.1145/1555228.1555254 Google ScholarDigital Library
- Joseph L. Hellerstein, Yixin Diao, Sujay Parekh, and Dawn M. Tilbury. 2004. Feedback Control of Computing Systems. John Wiley & Sons. isbn:047126637X Google ScholarDigital Library
- Henry Hoffmann. 2015. JouleGuard: Energy Guarantees for Approximate Applications. In Proceedings of the 25th Symposium on Operating Systems Principles (SOSP ’15). ACM, New York, NY, USA. 198–214. isbn:978-1-4503-3834-9 https://doi.org/10.1145/2815400.2815403 Google ScholarDigital Library
- Henry Hoffmann, Jim Holt, George Kurian, Eric Lau, Martina Maggio, Jason E. Miller, Sabrina M. Neuman, Mahmut Sinangil, Yildiz Sinangil, Anant Agarwal, Anantha P. Chandrakasan, and Srinivas Devadas. 2012. Self-Aware Computing in the Angstrom Processor. In Proceedings of the 49th Annual Design Automation Conference (DAC ’12). Association for Computing Machinery, New York, NY, USA. 259–264. isbn:9781450311991 https://doi.org/10.1145/2228360.2228409 Google ScholarDigital Library
- Henry Hoffmann, Jim Holt, George Kurian, Eric Lau, Martina Maggio, Jason E. Miller, Sabrina M. Neuman, Mahmut Sinangil, Yildiz Sinangil, Anant Agarwal, Anantha P. Chandrakasan, and Srinivas Devadas. 2012. Self-Aware Computing in the Angstrom Processor. In Proceedings of the 49th Annual Design Automation Conference (DAC ’12). Association for Computing Machinery, New York, NY, USA. 259–264. isbn:9781450311991 https://doi.org/10.1145/2228360.2228409 Google ScholarDigital Library
- Henry Hoffmann, Stelios Sidiroglou, Michael Carbin, Sasa Misailovic, Anant Agarwal, and Martin Rinard. 2011. Dynamic Knobs for Responsive Power-Aware Computing. In Proceedings of the Sixteenth International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS XVI). Association for Computing Machinery, New York, NY, USA. 199–212. isbn:9781450302661 https://doi.org/10.1145/1950365.1950390 Google ScholarDigital Library
- Petr Jan Horn. 2001. Autonomic Computing: IBM’s Perspective on the State of Information Technology. Google Scholar
- Y. Hsu, K. Matsuda, and M. Matsuoka. 2018. Self-Aware Workload Forecasting in Data Center Power Prediction. In 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID). 321–330. Google Scholar
- Jian Huang, Xuechen Zhang, and Karsten Schwan. 2015. Understanding issue correlations: a case study of the Hadoop system. In SoCC. Google Scholar
- Connor Imes, Lars Bergstrom, and Henry Hoffmann. 2016. A Portable Interface for Runtime Energy Monitoring. In Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE 2016). Association for Computing Machinery, New York, NY, USA. 968–974. isbn:9781450342186 https://doi.org/10.1145/2950290.2983956 Google ScholarDigital Library
- Connor Imes and Henry Hoffmann. 2016. Bard: A unified framework for managing soft timing and power constraints. In Embedded Computer Systems: Architectures, Modeling and Simulation (SAMOS), 2016 International Conference on. 31–38. Google Scholar
- Connor Imes, Steven Hofmeyr, and Henry Hoffmann. 2018. Energy-Efficient Application Resource Scheduling Using Machine Learning Classifiers. In Proceedings of the 47th International Conference on Parallel Processing (ICPP 2018). Association for Computing Machinery, New York, NY, USA. Article 45, 11 pages. isbn:9781450365109 https://doi.org/10.1145/3225058.3225088 Google ScholarDigital Library
- C. Imes, D. H. K. Kim, M. Maggio, and H. Hoffmann. 2015. POET: a portable approach to minimizing energy under soft real-time constraints. In 21st IEEE Real-Time and Embedded Technology and Applications Symposium. 75–86. https://doi.org/10.1109/RTAS.2015.7108419 Google ScholarCross Ref
- Evangelia Kalyvianaki, Themistoklis Charalambous, and Steven Hand. 2009. Self-Adaptive and Self-Configured CPU Resource Provisioning for Virtualized Servers Using Kalman Filters. In Proceedings of the 6th International Conference on Autonomic Computing (ICAC ’09). Association for Computing Machinery, New York, NY, USA. 117–126. isbn:9781605585642 https://doi.org/10.1145/1555228.1555261 Google ScholarDigital Library
- Evangelia Kalyvianaki, Themistoklis Charalambous, and Steven Hand. 2014. Adaptive Resource Provisioning for Virtualized Servers Using Kalman Filters. ACM Trans. Auton. Adapt. Syst., 9, 2 (2014), Article 10, July, 35 pages. issn:1556-4665 https://doi.org/10.1145/2626290 Google ScholarDigital Library
- Aman Kansal, Scott Saponas, A.J. Bernheim Brush, Kathryn S. McKinley, Todd Mytkowicz, and Ryder Ziola. 2013. The Latency, Accuracy, and Battery (LAB) Abstraction: Programmer Productivity and Energy Efficiency for Continuous Mobile Context Sensing. In Proceedings of the 2013 ACM SIGPLAN International Conference on Object Oriented Programming Systems Languages & Applications (OOPSLA ’13). ACM, New York, NY, USA. 661–676. isbn:978-1-4503-2374-1 https://doi.org/10.1145/2509136.2509541 Google ScholarDigital Library
- J. O. Kephart and D. M. Chess. 2003. The vision of autonomic computing. Computer, 36, 1 (2003), Jan, 41–50. https://doi.org/10.1109/MC.2003.1160055 Google ScholarDigital Library
- Minyoung Kim, Mark-Oliver Stehr, Carolyn Talcott, Nikil Dutt, and Nalini Venkatasubramanian. 2013. XTune: A Formal Methodology for Cross-Layer Tuning of Mobile Embedded Systems. ACM Trans. Embed. Comput. Syst., 11, 4 (2013), Article 73, Jan., 23 pages. issn:1539-9087 https://doi.org/10.1145/2362336.2362340 Google ScholarDigital Library
- Karl Krauth, Stephen Tu, and Benjamin Recht. 2019. Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator. In Advances in Neural Information Processing Systems 32, H. Wallach, H. Larochelle, A. Beygelzimer, F. d' Alché-Buc, E. Fox, and R. Garnett (Eds.). Curran Associates, Inc., 8514–8524. http://papers.nips.cc/paper/9058-finite-time-analysis-of-approximate-policy-iteration-for-the-linear-quadratic-regulator.pdf Google Scholar
- Robert Laddaga. 1999. Guest Editor’s Introduction: Creating Robust Software Through Self-Adaptation. IEEE Intelligent Systems, 14, 3 (1999), May, 26–29. issn:1541-1672 https://doi.org/10.1109/MIS.1999.769879 Google ScholarDigital Library
- Guang-Liang Li and P. Dowd. 1995. An analysis of network performance degradation induced by workload fluctuations. IEEE/ACM Transactions on Networking, 3, 04 (1995), jul, 433–440. issn:1558-2566 https://doi.org/10.1109/90.413217 Google ScholarDigital Library
- Kiwan Maeng and Brandon Lucia. 2018. Adaptive Dynamic Checkpointing for Safe Efficient Intermittent Computing. In 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18). USENIX Association, Carlsbad, CA. 129–144. isbn:978-1-939133-08-3 https://www.usenix.org/conference/osdi18/presentation/maeng Google ScholarDigital Library
- Martina Maggio, Henry Hoffmann, Alessandro V. Papadopoulos, Jacopo Panerati, Marco D. Santambrogio, Anant Agarwal, and Alberto Leva. 2012. Comparison of Decision-Making Strategies for Self-Optimization in Autonomic Computing Systems. ACM Trans. Auton. Adapt. Syst., 7, 4 (2012), Article 36, Dec., 32 pages. issn:1556-4665 https://doi.org/10.1145/2382570.2382572 Google ScholarDigital Library
- Martina Maggio, Alessandro Vittorio Papadopoulos, Antonio Filieri, and Henry Hoffmann. 2017. Automated Control of Multiple Software Goals Using Multiple Actuators. In Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering (ESEC/FSE 2017). Association for Computing Machinery, New York, NY, USA. 373–384. isbn:9781450351058 https://doi.org/10.1145/3106237.3106247 Google ScholarDigital Library
- M. Maggio, A. V. Papadopoulos, A. Filieri, and H. Hoffmann. 2017. Self-Adaptive Video Encoder: Comparison of Multiple Adaptation Strategies Made Simple. In 2017 IEEE/ACM 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS). 123–128. issn:null https://doi.org/10.1109/SEAMS.2017.16 Google ScholarDigital Library
- Hongzi Mao, Ravi Netravali, and Mohammad Alizadeh. 2017. Neural Adaptive Video Streaming with Pensieve. In Proceedings of the Conference of the ACM Special Interest Group on Data Communication (SIGCOMM ’17). Association for Computing Machinery, New York, NY, USA. 197–210. isbn:9781450346535 https://doi.org/10.1145/3098822.3098843 Google ScholarDigital Library
- Nikita Mishra, Connor Imes, John D. Lafferty, and Henry Hoffmann. 2018. CALOREE: Learning Control for Predictable Latency and Low Energy. In Proceedings of the Twenty-Third International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS ’18). Association for Computing Machinery, New York, NY, USA. 184–198. isbn:9781450349116 https://doi.org/10.1145/3173162.3173184 Google ScholarDigital Library
- A. Moustafa and M. Zhang. 2014. Learning Efficient Compositions for QoS-Aware Service Provisioning. In 2014 IEEE International Conference on Web Services. 185–192. https://doi.org/10.1109/ICWS.2014.37 Google ScholarCross Ref
- Frans P. B. Osinga. 2007. Science, Strategy and War: The strategic theory of John Boyd. Routledge. Google Scholar
- A. Padovitz, S. Loke, and A. Zaslavsky. 2003. Awareness and Agility for Autonomic Distributed Systems: Platform-Independent Publish-Subscribe Event-Based Communication for Mobile Agents. In 2012 23rd International Workshop on Database and Expert Systems Applications. IEEE Computer Society, Los Alamitos, CA, USA. 669. issn:1529-4188 https://doi.org/10.1109/DEXA.2003.1232098 Google ScholarCross Ref
- Ariel Rabkin and Randy Howard Katz. 2013. How hadoop clusters break. IEEE software, 30, 4 (2013). Google ScholarDigital Library
- Amir M. Rahmani, Bryan Donyanavard, Tiago Mück, Kasra Moazzemi, Axel Jantsch, Onur Mutlu, and Nikil Dutt. 2018. SPECTR: Formal Supervisory Control and Coordination for Many-Core Systems Resource Management. SIGPLAN Not., 53, 2 (2018), March, 169–183. issn:0362-1340 https://doi.org/10.1145/3296957.3173199 Google ScholarDigital Library
- Redhat. 2020. Tuned: Tuning Profile Delivery Mechanism for Linux. https://tuned-project.org/ Google Scholar
- Eric Rutten, Nicolas Marchand, and Daniel Simon. 2017. Feedback Control as MAPE-K Loop in Autonomic Computing. In Software Engineering for Self-Adaptive Systems III. Assurances, Rogério de Lemos, David Garlan, Carlo Ghezzi, and Holger Giese (Eds.). Springer International Publishing, Cham. 349–373. isbn:978-3-319-74183-3 Google Scholar
- M. Révay and M. Líška. 2017. OODA loop in command control systems. In 2017 Communication and Information Technologies (KIT). 1–4. https://doi.org/10.23919/KIT.2017.8109463 Google ScholarCross Ref
- Adrian Sampson, Werner Dietl, Emily Fortuna, Danushen Gnanapragasam, Luis Ceze, and Dan Grossman. 2011. EnerJ: Approximate Data Types for Safe and General Low-power Computation. In Proceedings of the 32Nd ACM Conference on Programming Language Design and Implementation (PLDI ’11). ACM, New York, NY, USA. 164–174. isbn:978-1-4503-0663-8 https://doi.org/10.1145/1993498.1993518 Google ScholarDigital Library
- Muhammad Husni Santriaji and Henry Hoffmann. 2016. GRAPE: Minimizing energy for GPU applications with performance requirements. In 2016 49th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO). 1–13. https://doi.org/10.1109/MICRO.2016.7783719 Google ScholarCross Ref
- Homeland Security. 2013. CCTV Technology Handbook. Online Documen. Google Scholar
- Akbar Sharifi, Shekhar Srikantaiah, Asit K. Mishra, Mahmut Kandemir, and Chita R. Das. 2011. METE: Meeting End-to-End QoS in Multicores through System-Wide Resource Management. In Proceedings of the ACM SIGMETRICS Joint International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS ’11). Association for Computing Machinery, New York, NY, USA. 13–24. isbn:9781450308144 https://doi.org/10.1145/1993744.1993747 Google ScholarDigital Library
- David C. Snowdon, Etienne Le Sueur, Stefan M. Petters, and Gernot Heiser. 2009. Koala: A Platform for OS-Level Power Management. In Proceedings of the 4th ACM European Conference on Computer Systems (EuroSys ’09). Association for Computing Machinery, New York, NY, USA. 289–302. isbn:9781605584829 https://doi.org/10.1145/1519065.1519097 Google ScholarDigital Library
- Jacob Sorber, Alexander Kostadinov, Matthew Garber, Matthew Brennan, Mark D. Corner, and Emery D. Berger. 2007. Eon: A Language and Runtime System for Perpetual Systems. In In Proceedings of The Fifth International ACM Conference on Embedded Networked Sensor Systems (SenSys ’07), Syndey. Google Scholar
- Akshitha Sriraman and Thomas F. Wenisch. 2018. mu Tune: Auto-Tuned Threading for OLDI Microservices. In 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18). USENIX Association, Carlsbad, CA. 177–194. isbn:978-1-939133-08-3 https://www.usenix.org/conference/osdi18/presentation/sriraman Google Scholar
- David C. Steere, Ashvin Goel, Joshua Gruenberg, Dylan McNamee, Calton Pu, and Jonathan Walpole. 1999. A Feedback-Driven Proportion Allocator for Real-Rate Scheduling. In Proceedings of the Third Symposium on Operating Systems Design and Implementation (OSDI ’99). USENIX Association, USA. 145–158. isbn:1880446391 Google ScholarDigital Library
- Xin Sui, Andrew Lenharth, Donald S. Fussell, and Keshav Pingali. 2016. Proactive Control of Approximate Programs. In Proceedings of the Twenty-First International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS ’16, Atlanta, GA, USA, April 2-6, 2016. 607–621. https://doi.org/10.1145/2872362.2872402 Google ScholarDigital Library
- Chunqiang Tang, Kenny Yu, Kaushik Veeraraghavan, Jonathan Kaldor, Scott Michelson, Thawan Kooburat, Aravind Anbudurai, Matthew Clark, Kabir Gogia, Long Cheng, Ben Christensen, Alex Gartrell, Maxim Khutornenko, Sachin Kulkarni, Marcin Pawlowski, Tuomas Pelkonen, Andre Rodrigues, Rounak Tibrewal, Vaishnavi Venkatesan, and Peter Zhang. 2020. Twine: A Unified Cluster Management System for Shared Infrastructure. In 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20). USENIX Association, 787–803. isbn:978-1-939133-19-9 https://www.usenix.org/conference/osdi20/presentation/tang Google Scholar
- Konstantinos Tovletoglou, Lev Mukhanov, Dimitrios S. Nikolopoulos, and Georgios Karakonstantis. 2020. HaRMony: Heterogeneous-Reliability Memory and QoS-Aware Energy Management on Virtualized Servers. In Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS ’20). Association for Computing Machinery, New York, NY, USA. 575–590. isbn:9781450371025 https://doi.org/10.1145/3373376.3378489 Google ScholarDigital Library
- Stephen Tu and Benjamin Recht. 2018. Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator. In Proceedings of the 35th International Conference on Machine Learning, Jennifer Dy and Andreas Krause (Eds.) (Proceedings of Machine Learning Research, Vol. 80). PMLR, Stockholmsmässan, Stockholm Sweden. 5005–5014. http://proceedings.mlr.press/v80/tu18a.html Google Scholar
- David Vengerov. 2009. A reinforcement learning framework for utility-based scheduling in resource-constrained systems. Future Generation Computer Systems, 25, 7 (2009), 728 – 736. issn:0167-739X https://doi.org/10.1016/j.future.2008.02.006 Google ScholarDigital Library
- Jóakim von Kistowski, Hansfried Block, John Beckett, Klaus-Dieter Lange, Jeremy Arnold, and Samuel Kounev. 2015. Analysis of the Influences on Server Power Consumption and Energy Efficiency for CPU-Intensive Workloads. ICPE 2015 - Proceedings of the 6th ACM/SPEC International Conference on Performance Engineering, https://doi.org/10.1145/2668930.2688057 Google ScholarDigital Library
- Chengcheng Wan, Muhammad Santriaji, Eri Rogers, Henry Hoffmann, Michael Maire, and Shan Lu. 2020. ALERT: Accurate Learning for Energy and Timeliness. In 2020 USENIX Annual Technical Conference (USENIX ATC 20). USENIX Association, 353–369. isbn:978-1-939133-14-4 https://www.usenix.org/conference/atc20/presentation/wan Google Scholar
- Hongbign Wang, Xin Chen, Qin Wu, Qi Yu, Xingguo Hu, Zibin Zheng, and Athman Bouguettaya. 2017. Integrating Reinforcement Learning with Multi-Agent Techniques for Adaptive Service Composition. ACM Trans. Auton. Adapt. Syst., 12, 2 (2017), Article 8, May, 42 pages. issn:1556-4665 https://doi.org/10.1145/3058592 Google ScholarDigital Library
- Shu Wang, Chi Li, Henry Hoffmann, Shan Lu, William Sentosa, and Achmad Imam Kistijantoro. 2018. Understanding and Auto-Adjusting Performance-Sensitive Configurations. In Proceedings of the Twenty-Third International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS ’18). ACM, New York, NY, USA. 154–168. isbn:978-1-4503-4911-6 https://doi.org/10.1145/3173162.3173206 Google ScholarDigital Library
- Yanzhi Wang and Massoud Pedram. 2016. Model-Free Reinforcement Learning and Bayesian Classification in System-Level Power Management. IEEE Trans. Comput., 65, 12 (2016), Dec, 3713–3726. issn:2326-3814 https://doi.org/10.1109/TC.2016.2543219 Google ScholarDigital Library
- Greg Welch and Gary Bishop. 1995. An Introduction to the Kalman Filter. USA. Google Scholar
- Hyunho Yeo, Youngmok Jung, Jaehong Kim, Jinwoo Shin, and Dongsu Han. 2018. Neural Adaptive Content-aware Internet Video Delivery. In 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18). USENIX Association, Carlsbad, CA. 645–661. isbn:978-1-939133-08-3 https://www.usenix.org/conference/osdi18/presentation/yeo Google Scholar
- Wanghong Yuan and Klara Nahrstedt. 2003. Energy-Efficient Soft Real-Time CPU Scheduling for Mobile Multimedia Systems. In Proceedings of the Nineteenth ACM Symposium on Operating Systems Principles (SOSP ’03). Association for Computing Machinery, New York, NY, USA. 149–163. isbn:1581137575 https://doi.org/10.1145/945445.945460 Google ScholarDigital Library
- Ronghua Zhang, Chenyang Lu, Tarek F. Abdelzaher, and John A. Stankovic. 2002. ControlWare: a middleware architecture for feedback control of software performance. In Proceedings 22nd International Conference on Distributed Computing Systems. 301–310. issn:1063-6927 https://doi.org/10.1109/ICDCS.2002.1022267 Google ScholarCross Ref
- Yuhao Zhu and Vijay Janapa Reddi. 2016. GreenWeb: Language Extensions for Energy-efficient Mobile Web Computing. In Proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI ’16). ACM, New York, NY, USA. 145–160. isbn:978-1-4503-4261-2 https://doi.org/10.1145/2908080.2908082 Google ScholarDigital Library
Index Terms
- GOAL: Supporting General and Dynamic Adaptation in Computing Systems
Recommendations
Implementation of a GOAL PROGRAM
SIGUCCS '75: Proceedings of the 3rd annual ACM SIGUCCS conference on User servicesWe all need specific goals to guide us. Goals give you direction. Recognizing that we need goals is the first step toward choosing and achieving those goals.
By making each goal specific, you have definite deadlines to aim for. Get a statement of your ...
Enabling Goal Oriented Action Planning with Goal Net
WI-IAT '09: Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02In this paper, we propose an agent planning system based on the Goal Net model. The agent’s goals are identified and organized in a composite goal hierarchy. Three kinds of relations between goals are defined: choice, concurrency and synchronization. ...
A Runtime Goal Conflict Resolution Model for Agent Systems
WI-IAT '12: Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02The goal-oriented agent programming based on BDI model is obtaining increasing attentions, because it allows us to design proactive behaviors for an agent. Generally, an agent does pursue multiple goals not only in a sequential way, but in a ...
Comments