Exploiting Pretended Networking for Energy Saving in Automotive Networks

In this paper we propose an approach on energy management in automotive CAN networks based on static scheduling of AUTOSAR runnables. The approach exploits the energy saving potential of the pretended networking mechanism defined by AUTOSAR. The mechanism selectively turns the Electronic Control Units (ECUs) into a lower power mode while they are not permanently used during operation. In the first step, our approach allocates as much continuous slack time as possible to enter pretended mode by static scheduling. In the second step, further optimization is achieved by exploiting the varying response times of the CAN messages in different vehicle states. At last, an online algorithm puts the ECUs into pretended mode dynamically during vehicle operation. Experimental results of the first optimization step show up to 31.3% saving potential of the ECUs energy consumption. Additional 8.2% energy saving can be achieved by the second optimization step. The proposed online algorithm can save up to 30.3% more energy compared with the pure static approach in the first step.

[1]  Mani B. Srivastava,et al.  Predictive system shutdown and other architectural techniques for energy efficient programmable computation , 1996, IEEE Trans. Very Large Scale Integr. Syst..

[2]  Jochen Zimmermann Applikationsspezifische Analyse und Optimierung der Energieeffizienz eingebetteter Hardware/Software-Systeme , 2013 .

[3]  Wang Yi,et al.  Fixed-Priority Multiprocessor Scheduling with Liu and Layland's Utilization Bound , 2010, 2010 16th IEEE Real-Time and Embedded Technology and Applications Symposium.

[4]  Rami G. Melhem,et al.  Energy aware scheduling for distributed real-time systems , 2003, Proceedings International Parallel and Distributed Processing Symposium.

[5]  James W. Layland,et al.  Scheduling Algorithms for Multiprogramming in a Hard-Real-Time Environment , 1989, JACM.

[6]  C. Siva Ram Murthy,et al.  New Algorithms for Resource Reclaiming from Precedence Constrained Tasks in Multiprocessor Real-Time Systems , 1997, J. Parallel Distributed Comput..

[7]  Andreas Krüger,et al.  Energy efficiency in automotive networks: Assessment and concepts , 2010, 2010 International Conference on High Performance Computing & Simulation.

[8]  Rafael Martí,et al.  Experimental Testing of Advanced Scatter Search Designs for Global Optimization of Multimodal Functions , 2005, J. Glob. Optim..

[9]  Daniel Moss,et al.  Compiler-assisted dynamic power-aware scheduling for real-time applications , 2000 .

[10]  Luca Benini,et al.  A survey of design techniques for system-level dynamic power management , 2000, IEEE Trans. Very Large Scale Integr. Syst..

[11]  Thomas D. Burd,et al.  The simulation and evaluation of dynamic voltage scaling algorithms , 1998, Proceedings. 1998 International Symposium on Low Power Electronics and Design (IEEE Cat. No.98TH8379).

[12]  Thomas Nolte,et al.  Towards hierarchical scheduling in AUTOSAR , 2009, 2009 IEEE Conference on Emerging Technologies & Factory Automation.

[13]  Thomas Liebetrau,et al.  Energy Saving in Automotive E / E Architectures , 2013 .

[14]  Scott Shenker,et al.  Scheduling for reduced CPU energy , 1994, OSDI '94.

[15]  Alan Burns,et al.  Controller Area Network (CAN) schedulability analysis: Refuted, revisited and revised , 2007, Real-Time Systems.

[16]  Luca Benini,et al.  Dynamic power management using adaptive learning tree , 1999, 1999 IEEE/ACM International Conference on Computer-Aided Design. Digest of Technical Papers (Cat. No.99CH37051).

[17]  Sébastien Saudrais,et al.  Applying Holistic Distributed Scheduling to AUTOSAR Methodology , 2010 .

[18]  Daniel F. García,et al.  Minimum and maximum utilization bounds for multiprocessor rate monotonic scheduling , 2004, IEEE Transactions on Parallel and Distributed Systems.

[19]  Wolfgang Rosenstiel,et al.  STELLaR - A case-study on SysTEmaticaLLy embedding a Traffic Light Recognition , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[20]  Alan Burns,et al.  Calculating controller area network (can) message response times , 1994 .

[21]  Françoise Simonot-Lion,et al.  Multisource Software on Multicore Automotive ECUs—Combining Runnable Sequencing With Task Scheduling , 2012, IEEE Transactions on Industrial Electronics.

[22]  Wolfgang Rosenstiel,et al.  State-based power optimization using mixed-criticality filter for automotive networks , 2015, 2015 IEEE Intelligent Vehicles Symposium (IV).