Skip to main content

Advertisement

SpringerLink
Log in

Performance maximization of energy-variable self-powered (m, k)-firm real-time systems

  • Published:
Real-Time Systems Aims and scope Submit manuscript

Abstract

Many of today’s embedded devices, e.g. pacemakers or nodes in a monitoring sensor network, are expected to work energy perpetual, i.e. to be self-powered by energy harvesting from renewable sources. The major issue of such systems is the uncertainty of the available energy, influencing the application performance predictability. In many such applications, different performance levels are defined according to the patterns of job skipping. This paper proposes a performance maximization method for self-powered energy-intermittent (m, k)-firm systems via appropriate switching between the performance levels. To formally examine the impact of performance switch time on the performance-related criteria, we introduce the energy supply and energy demand functions. A sufficient schedulability test for one hyperperiod is also proposed for the preemptive fixed-priority as-soon-as-possible (\(PFP_{ASAP}\)) scheduling algorithm under variable-rate energy harvesting. We then propose a performance maximization heuristic, and compare its effectiveness to the optimal performance. The extensive simulations show that our proposed method is fast, whereas it effectively approximates the optimal solution.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. For example, when the energy storage unit has a large (almost unlimited) capacity and we have a huge energy arrival rate.

  2. We ignore \(0\) from the interval, because it corresponds to a rate change at the end of previous hyperperiod.

  3. \(E(\varPi )\) is the initial available energy in the storage unit for the next hyperperiod, and hence, it influences the performance of the system.

  4. Because \(\alpha _i^l\) and \(\beta _i^l\) are independent of \(t\), we use them for simpler presentation of (7).

  5. For larger lifetimes, the optimal solution takes considerable computation time.

References

  • Abdallah M, Chetto M, Queudet A (2013) Energy-aware schedulers for real-time energy harvesting systems with quality of service requirements. In: 2nd international conference on advances in computational tools for engineering applications. IEEE, pp 342–347

  • Abdeddaïm Y, Chandarli Y, Masson D (2013) The optimality of PFPasap algorithm for fixed-priority energy-harvesting real-time systems. In: Proceedings of the 25th euromicro conference on real-time systems. pp 47–56

  • Abdeddaïm Y, Chandarli Y, Davis RI, Masson D (2016) Response time analysis for fixed priority real-time systems with energy-harvesting. Real-Time Syst 52:125–160. https://doi.org/10.1007/s11241-015-9239-7

    Article  MATH  Google Scholar 

  • Anasuya NJ (2014) Battery capacity management in wireless sensor network rechargeable sensor nodes. Int J Eng Comput Sci 3:8129–8135

    Google Scholar 

  • Bambagini M, Marinoni M, Aydin H, Buttazzo G (2016) Energy-aware scheduling for real-time systems: a survey. ACM Trans Embedded Comput Syst (TECS) 15:1–34

    Article  Google Scholar 

  • Baruah S, Chen D, Gorinsky S, Mok A (1999) Generalized multiframe tasks. Real-Time Syst 17:5–22

    Article  Google Scholar 

  • Bernat G, Burns A, Liamosi A (2001) Weakly hard real-time systems. IEEE Trans Comput 50:308–321

    Article  MathSciNet  Google Scholar 

  • Bini E, Buttazzo GC (2005) Measuring the performance of schedulability tests. Real-Time Syst 30:129–154

    Article  Google Scholar 

  • Bruggen GVD, Chen KH, Huang WH, Chen JJ (2016) Systems with dynamic real-time guarantees in uncertain and faulty execution environments. In: IEEE real-time systems symposium. IEEE

  • Chen YS, Chiu SC (2018) New method of automatic control for vehicle headlights. Optik 157:718–723

    Article  Google Scholar 

  • Chetto H, Chetto M (1989) Some results of the earliest deadline scheduling algorithm. IEEE Trans Softw Eng 15:1261–1269

    Article  MathSciNet  Google Scholar 

  • Chetto M (2015) Graceful overload management in firm real-time systems. J Inf Technol Softw Eng 5:3. https://doi.org/10.4172/2165-7866.1000e124

    Article  Google Scholar 

  • Chetto M, Marchand A (2007) Dynamic scheduling of skippable periodic tasks in weakly-hard real-time systems. In: 14th annual IEEE international conference and workshops on the engineering of computer-based systems. IEEE

  • Dang N, Bozorgzadeh E, Park M (2015) Multi-level QoS support with variable window size in weakly hard real-time systems. In: Proceedings of the 3rd international conference on cyber-physical systems, networks, and applications. IEEE. pp 25–30

  • Dong X, Xu C, Xie Y, Jouppi NP (2012) NVSim: a circuit-level performance, energy, and area model for emerging nonvolatile memory. IEEE Trans Comput-Aid Des Integr Circuits Syst 31:994–1007

    Article  Google Scholar 

  • Florio SD, Gill E, D’Amico S (2009) Performance comparison of microprocessors for space-based navigation applications. In: 7th IAA symposium on small satellite for earth observation

  • Fridley D (2010) Nine challenges of alternative energy. Post Carbon Institute

  • Gettings O, Quinton S, Davis RI (2015) Mixed criticality systems with weakly-hard constraints. In: Proceedings of the 23rd international conference on real time and networks systems. ACM, pp 237–246

  • Ghor HE, Chetto M, HageChehade R (2011) A real-time scheduling framework for embedded systems with environmental energy harvesting. Comput Electr Eng 37:498–510

    Article  Google Scholar 

  • Goossens J, Macq C (2001) Limitation of the hyper-period in real-time periodic task set generation. In: Proceedings of the RTS embedded system (RTS’01), pp 133–147

  • Guthaus MR, Ringenberg JS, Ernst DJ, Austin TM, Mudge TN, Brown RB (2001) MiBench: a free, commercially representative embedded benchmark suite. In: IEEE international workshop on workload characterization. IEEE, pp 3–14

  • Haeberlin A, Zurbuchen A, Walpen S, Schaerer J, Niederhauser T, Huber C, Tanner H, Servatius H, Seiler J, Haeberlin H, Fuhrer J, Vogel R (2015) The first batteryless, solar-powered cardiac pacemaker. Heart Rhythm 12:1317–1323

    Article  Google Scholar 

  • Hamdaoui M, Ramanathan P (1995) A dynamic priority assignment technique for streams with (m, k)-firm deadlines. IEEE Trans Comput 44:1443–1451

    Article  Google Scholar 

  • Jayaseelan R, Mitra T, Li X (2006) Estimating the worst-case energy consumption of embedded software. In: 12th IEEE real-time and embedded technology and applications symposium (RTAS’06). IEEE, pp 81–90

  • Kooti H, Dang N, Mishra D, Bozorgzadeh E (2012) Energy budget management for energy harvesting embedded systems. In: 18th international conference on embedded and real-time computing systems and applications. IEEE, pp 320–329

  • Koren G, Shasha D (1995) An approach to handling overloaded systems that allow skips. In: IEEE real time systems symposium. IEEE, pp 110–119

  • LEON3 datasheet. Leon3 multiprocessing cpu core. URL http://www.gaisler.com/doc/leon3_product_sheet.pdf. Accessed 6 Jan 2019

  • Li Y, Wang Z, Song Y (2006) Wireless sensor network design for wildfire monitoring. In: 6th world congress on intelligent control and automation. IEEE

  • Liestman AL, Campbell RH (1986) A fault-tolerant scheduling problem. IEEE Trans Softw Eng 12:1089–1095

    Article  Google Scholar 

  • Lo BPL, Thiemjarus S, King RC, Yang G (2005) Body sensor network – a wireless sensor platform for pervasive healthcare monitoring

  • Marcy H, Agre J, Chien C, Clare L, Romanov N, Twarowski A (1999) Wireless sensor networks for area monitoring and integrated vehicle health management applications. In: AIAA guidance, navigation, and control conference and exhibit

  • Mohaqeqi M, Nasri M, Xu Y, Cervin A, Årzén KE (2018) Optimal harmonic period assignment: complexity results and approximation algorithms. Real-Time Syst 54:830–860

    Article  Google Scholar 

  • Moser C, Brunelli D, Thiele L, Benini L (2006) Real-time scheduling with regenerative energy. In: 18th Euromicro conference on real-time systems. IEEE

  • MSP430 datasheet. URL https://www.ti.com/lit/ds/symlink/msp430g2553.pdf. Accessed 29 May 2019

  • MTS400 datasheet. URL https://www.memsic.com/wireless-sensor-networks/MTS400. Accessed 29 May 2019

  • Priya S, Inman DJ (2009) Energy harvesting technologies. Springer, New York

    Book  Google Scholar 

  • Quan G, Hu X (2000) Enhanced fixed-priority scheduling with (m, k)-firm guarantee. In: Proceedings of the 21st IEEE conference on real-time systems symposium. ACM, pp 79–88

  • Ramanathan P (1999) Overload management in real-time control applications using (m, k)-firm guarantee. IEEE Trans Parallel Distrib Syst 10:549–559

    Article  Google Scholar 

  • Ramos J, Andreas A (2011) University of Texas Panamerican (UTPA): Solar Radiation Lab (SRL); Edinburg, Texas (Data). In NREL Report No. DA-5500-56514

  • Saewong S, Rajkumar R (2003) Practical voltage-scaling for fixed-priority RT-systems. In: Proceedings of the 9th IEEE real-time and embedded technology and applications symposium. IEEE, pp 134–139

  • Salehi M, Tavana MK, Rehman S, Shafique M, Ejlali A, Henkel J (2016) Two-state checkpointing for energy-efficient fault tolerance in hard real-time systems. IEEE Trans Very Large Scale Integr (VLSI) Syst 24:2426–2437

    Article  Google Scholar 

  • Shirazi M, Kargahi M, Thiele L (2017) Resilient scheduling of energy-variable weakly-hard real-time systems. In: Proceedings of the 25th international conference on real-time networks and systems. ACM, pp 297–306

  • Sun Y, Natale MD (2017) Weakly hard schedulability analysis for fixed priority scheduling of periodic real-time tasks. ACM Trans Embedded Comput Syst (TECS). https://doi.org/10.1145/3126497

    Article  Google Scholar 

  • Yun HS, Kim J (2003) On energy-optimal voltage scheduling for fixed-priority hard real-time systems. ACM Trans Embedded Comput Syst (TECS) 2:393–430

    Article  Google Scholar 

  • Zhou J, Yan J, Wei T, Chen M, Hu XS (2017) Energy-adaptive scheduling of imprecise computation tasks for QoS optimization in real-time MPSoC systems. In: Proceedings of the conference on design, automation and test in Europe, ACM, pp 1406–1411

Download references

Author information

Authors and Affiliations

  1. School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran

    Mahmoud Shirazi & Mehdi Kargahi

  2. School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Mehdi Kargahi

  3. Computer Engineering and Networks Laboratory, ETH Zurich, Zurich, Switzerland

    Lothar Thiele

Authors
  1. Mahmoud Shirazi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mehdi Kargahi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lothar Thiele
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mehdi Kargahi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

A Notations

A Notations

In this section, we summarize the notations used in the paper as shown in Table 15

Table 15 Notations
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shirazi, M., Kargahi, M. & Thiele, L. Performance maximization of energy-variable self-powered (m, k)-firm real-time systems. Real-Time Syst 56, 64–111 (2020). https://doi.org/10.1007/s11241-020-09344-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11241-020-09344-1

Keywords

Search

Navigation