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A multi-level AI-based scheduler to increase adaptiveness in time-constrained mobile communication environments

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Abstract

Scheduling is one of the classic problems in real-time adaptive systems. Due to the complex nature of these applications, the implementation of some sort of run-time intelligence is required, in order to build intelligent systems capable of operating adequately in dynamic environments. The incorporation of artificial intelligence planning techniques in a real-time scenario allows a timely reaction to external and internal events. In this work, a layered architecture integrating real-time scheduling at the bottom level and artificial intelligence planning techniques at the top level has been designed, to implement a multi-level scheduler with the capability to perform effectively in this kind of situation. This multi-level scheduler has been implemented and evaluated in a simulated information access system destined to broadcast information to mobile users in a time-constrained communication environment, modeling mobile users’ realistic information access patterns. Results show that the incorporation of artificial intelligence planning improves the overall performance, adaptiveness, and responsiveness with respect to the non-AI-based scheduler version of the system.

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References

  • Abroyan N, Hakobyan R (2016) A review of the usage of machine learning in real-time systems. In: Proceedings of NPUA information technologies, electronics, radio, engineering

  • Acharya S, Alonso R, Franklin M, Zdonik S (1995) Broadcast disk data management for asymmetric communication environments. In: Proceedings of ACM SIGMOD conference. San Jose, California, USA.Ali GG, Chong PH, Samantha SK, Chan E (2016) Efficient data dissemination in cooperative multi-RSU Vehicular Ad Hoc Networks (VANETs). J Syst Softw 117: 508-527

  • Ali GG, Chong PH, Samantha SK, Chan E (2016) Efficient data dissemination in cooperative multi-RSU Vehicular Ad Hoc Networks (VANETs). J Syst Softw 117:508–527

    Article  Google Scholar 

  • Barták R, Salido MA, Rossi F (2010) New trends in constraint satisfaction, planning, and scheduling: a survey. Knowl Eng. Rev 25:249–279

    Article  Google Scholar 

  • Baruah S, Lin S (1997) Improved scheduling of generalized pinwheel task systems. In: Proceedings of 4th international workshop on real-time computer systems applications, Taipei, Taiwan

  • Breslau L et al. (1999) Web caching and Zipf-like distributions: evidence and implications. In: Proc. IEEE Infocom 99

  • Chatila R (1995) Deliberation and reactivity in autonomous mobile robots. Robot Auton Syst 16:197–211

    Article  Google Scholar 

  • Decker KS, Garvey AJ, Humphrey MA, Lesser VR (1993) A real-time control architecture for an approximate processing blackboard system. Int J Pattern Recognit Artif Intell 7(2):265–284

    Article  Google Scholar 

  • Fernandez J, Ramamritham K (2004) adaptive dissemination of data in time-critical asymmetric communication environments. Mobile Netw Appl 9(5):491–505

    Article  Google Scholar 

  • Fernandez-Conde J, Mozos D (2006) Adaptive hybrid broadcast for data dissemination in time-constrained asymmetric communication environments. In: 32nd IEEE Euromicro conference on software engineering and advanced applications (SEAA), Cavtat/Dubrovnik (Croatia), pp. 438–447

  • Fernandez-Conde J, Mozos D (2007) Efficient scheduling for mobile time-constrained environments. IET Electron Lett J 43(22):1214–1215

    Article  Google Scholar 

  • Fernandez-Conde J, Mozos D (2008) Pull vs. Hybrid: comparing scheduling algorithms for asymmetric time-constrained environments. In: Proceedings of 2008 international conference on wireless networks, pp 222-228. Las Vegas, USA

  • Fernandez-Conde J, Cuenca-Jimenez P, Toledo-Moreo R (2019) Improving scheduling performance of a real-time system by incorporation of an artificial intelligence planner. In: Proceedings of IWINAC09 conference, pp 127–136. Almería, Spain. https://doi.org/10.1007/978-3-030-19651-6_13

  • Firby RJ (1987) An investigation into reactive planning in complex domains. In: Proceedings of the sixth national conference on artificial intelligence, pp 202–206, Seattle, WA

  • Garcia-Martinez A, Fernández-Conde J, Viña A (1996) A comprehensive approach in performance evaluation for modern real-time operating systems, pp 61–68. In: Proceedings of EUROMICRO96, Prague, Czech Republic

  • Garvey A, Lesser V (1993) Design-to-time real-time scheduling. IEEE Trans Syst, Man Cybern 23(6):1491–1502

    Article  Google Scholar 

  • Garvey A, Lesser V (1994) A survey of research in deliberative real-time artificial intelligence. Real-Time Syst 6(3):317–347

    Article  Google Scholar 

  • Garvey A, Lesser V (1995) Representing and scheduling satisficing tasks. Imprecise and approximate computation. The Springer international series in engineering and computer science (Real-Time Systems), Springer, Boston

    Google Scholar 

  • Garvey A, Humphrey M, Lesser V (1993) Task interdependencies in design-to-time real-time scheduling. In: Proceedings of the eleventh national conference on artificial intelligence, pp 580–585, Washington, D.C

  • Graham R, Lawler EL, Lenstra JK, Kan AHGR (1979) Optimization and approximation in deterministic sequencing and scheduling: a survey. Discrete optimization II. North-Holland Publishing Company, Amsterdam

    Book  MATH  Google Scholar 

  • Hernández L, Botti VJ, García-Fornes A (2006) A deliberative scheduling technique for a real-time agent architecture. Eng Appl Artif Intell 19:521–534

    Article  Google Scholar 

  • Imielinski T, Viswanathan S, Badrinath B (1994) Energy efficient indexing on air. In: Proceedings of ACM SIGMOD conference

  • Ingrand F, Georgeff M (1993) An architecture for real-time reasoning and system control. IEEE Expert 7(6):34–44

    Article  Google Scholar 

  • Ingrand F, Ghallab M (2017) Deliberation for autonomous robots: a survey. Artif Intell 247:10–44

    Article  MathSciNet  Google Scholar 

  • Kaldeli E, Lazovik A, Aiello M (2016) Domain-independent planning for services in uncertain and dynamic environments. Artif Intell 236:30–64

    Article  MathSciNet  MATH  Google Scholar 

  • Katrakazasa C, Quddus M, Chen WH, Deka L (2015) Real-time motion planning methods for autonomous on-road driving: state-of-the-art and future research directions. Transp Res Part C: Emerg Technol 60:416–442

    Article  Google Scholar 

  • Megherbi DB, Kim, MS (2015) A collaborative distributed multi-agent reinforcement learning technique for dynamic agent shortest path planning via selected sub-goals in complex cluttered environments. In: 2015 IEEE international multi-disciplinary conference on cognitive methods in situation awareness and decision, pp 118–124

  • Liu CL, Layland JW (1973) Scheduling algorithms for multiprogramming in a hard real-time environment. J ACM 20(1):46–61

    Article  MathSciNet  MATH  Google Scholar 

  • Ma X, Yang L (2013) A real-time scheduling strategy in on-demand broadcasting. In: International conference on graphic and image processing

  • Mouaddib A (2004) Incremental coordination for time-bounded agents. Int J Artif Intell Tools 13:511–532

    Article  Google Scholar 

  • Musliner D, Durfee E, Shin K (1993) CIRCA: a cooperative intelligent real-time control architecture. IEEE Trans Syst Man Cybern 23(6):1561–1574

    Article  Google Scholar 

  • Polatoglou M, Nicopolitidis P, Papadimitriou GI (2014) On low-complexity adaptive wireless push-based data broadcasting. Int J Commun Syst 27:194–200

    Article  Google Scholar 

  • Potts CM, Krebsbach KD, Thayer JT, Musliner DJ (2013) Improving trust estimates in planning domains with rare failure events. In: AAAI Spring symposium: trust and autonomous systems

  • Stankovic J (1995) The many faces of multi-level real-time scheduling. In: Proceedings of 2nd international workshop on real-time computing systems and applications RTCSA, Tokyo, Japan

  • Svegliato J, Wray KH, Zilberstein S (2018) Meta-level control of anytime algorithms with online performance prediction. In: IJCAI

  • Tiakas E, Ougiaroglou S, Nicopolitidis P (2009) Efficient algorithms for constructing broadcast disks programs in asymmetric communication environments. Telecommun Syst 41:185–209

    Article  Google Scholar 

  • Torras C (2002) Neural computing increases robot adaptivity. Nat Comput 1:391–425

    Article  MathSciNet  MATH  Google Scholar 

  • Xu H, Mueller F (2018) Work-in-progress: making machine learning real-time predictable. In: 2018 IEEE real-time systems symposium (RTSS), pp 157–160

  • Xu J, Tang X, Lee WC (2006) Time-critical on-demand broadcast: algorithms, analysis and performance evaluation. IEEE Trans Parallel Distrib Syst 17(1):3–14

    Article  Google Scholar 

  • Xuan P, Sen S, Gonzalez O, Fernandez J, Ramamritham K (1997) Efficient and timely dissemination of data in mobile environments. In: Proceedings of the third IEEE real time technology and applications symposium, Montreal, Canada

  • Zhong J, Wu W, Gao X, Shi Y, Yue X (2013) Evaluation and comparison of various indexing schemes in single-channel broadcast communication environment. Knowl Inf Syst 40:375–409

    Article  Google Scholar 

  • Zhou L, Geller B, Zheng B, Wei A, Cui J (2009) System scheduling for multi-description video streaming over wireless multi-hop networks. IEEE Trans Broadcast 55:731–741

    Article  Google Scholar 

  • Zilberstein S (1993) Operational rationality through compilation of anytime algorithms. Ph.D. Dissertation, Computer Science Department, Berkeley

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Authors and Affiliations

  1. GSyC Department, ETSIT, Universidad Rey Juan Carlos, Fuenlabrada, Madrid, Spain

    Jesus Fernandez-Conde & Pedro Cuenca-Jimenez

  2. Space Science and Engineering Lab, Department of Electronics and Computer Architecture, ETSIT, Universidad Politécnica de Cartagena, Cartagena, Spain

    Rafael Toledo-Moreo

Authors
  1. Jesus Fernandez-Conde
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  2. Pedro Cuenca-Jimenez
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  3. Rafael Toledo-Moreo
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Correspondence to Jesus Fernandez-Conde.

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Fernandez-Conde, J., Cuenca-Jimenez, P. & Toledo-Moreo, R. A multi-level AI-based scheduler to increase adaptiveness in time-constrained mobile communication environments. Nat Comput 21, 525–535 (2022). https://doi.org/10.1007/s11047-020-09813-3

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  • DOI: https://doi.org/10.1007/s11047-020-09813-3

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