Data Freshness in Mixed-Memory Intermittently-Powered Systems

Age of Information (AoI) is a key metric to understand data freshness in Internet of Things (IoT) devices. In this paper we analyse an intermittently—powered IoT sensor-with mixed-memory (volatile and non-volatile) architecture—that uses a Time-Dependent Checkpointing (TDC) scheme. We derive the average Peak Age of Information (PAoI) and average AoI of the system, and use these metrics to understand which device parameters most significantly influence performance. We go on to consider how the average PAoI of a mixed-memory system compares with entirely volatile or entirely non-volatile architecture, and also introduce an alternative TDC strategy to improve system resilience in unpredictable environmental conditions.

[1]  Omur Ozel Timely Status Updating Through Intermittent Sensing and Transmission , 2020, 2020 IEEE International Symposium on Information Theory (ISIT).

[2]  Anthony Ephremides,et al.  Age of information under random updates , 2013, 2013 IEEE International Symposium on Information Theory.

[3]  Luca Benini,et al.  Hibernus++: A Self-Calibrating and Adaptive System for Transiently-Powered Embedded Devices , 2016, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[4]  Gokcen Kestor,et al.  Toward a General Theory of Optimal Checkpoint Placement , 2017, 2017 IEEE International Conference on Cluster Computing (CLUSTER).

[5]  Omur Ozel,et al.  Active Status Update Packet Drop Control in an Energy Harvesting Node , 2019, 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[6]  Natalie D. Enright Jerger,et al.  The What's Next Intermittent Computing Architecture , 2019, 2019 IEEE International Symposium on High Performance Computer Architecture (HPCA).

[7]  Brandon Lucia,et al.  Intermittent Computing: Challenges and Opportunities , 2017, SNAPL.

[8]  Jacob Sorber,et al.  The Future of Sensing is Batteryless, Intermittent, and Awesome , 2017, SenSys.

[9]  Jing Yang,et al.  Sening Information Through Status Updates , 2018, 2018 IEEE International Symposium on Information Theory (ISIT).

[10]  Narayanan Vijaykrishnan,et al.  Nonvolatile processors: Why is it trending? , 2017, Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017.

[11]  Limin Jia,et al.  Towards a formal foundation of intermittent computing , 2020, Proc. ACM Program. Lang..

[12]  Franck Cappello,et al.  Optimization of Multi-level Checkpoint Model for Large Scale HPC Applications , 2014, 2014 IEEE 28th International Parallel and Distributed Processing Symposium.

[13]  Lih-Yih Chiou,et al.  An energy-efficient nonvolatile microprocessor considering software-hardware interaction for energy harvesting applications , 2016, 2016 International Symposium on VLSI Design, Automation and Test (VLSI-DAT).

[14]  Abu Bakar,et al.  Time-sensitive Intermittent Computing Meets Legacy Software , 2020, ASPLOS.

[15]  Josiah D. Hester,et al.  Reliable Timekeeping for Intermittent Computing , 2020, ASPLOS.

[16]  Roy D. Yates,et al.  Age of Information: An Introduction and Survey , 2020, IEEE Journal on Selected Areas in Communications.

[17]  Shanika Karunasekera,et al.  A Utilization Model for Optimization of Checkpoint Intervals in Distributed Stream Processing Systems , 2020, Future Gener. Comput. Syst..

[18]  Kevin Fu,et al.  Mementos: system support for long-running computation on RFID-scale devices , 2011, ASPLOS XVI.

[19]  Roy D. Yates,et al.  Real-time status: How often should one update? , 2012, 2012 Proceedings IEEE INFOCOM.

[20]  Eytan Modiano,et al.  Age of information: Design and analysis of optimal scheduling algorithms , 2016, 2017 IEEE International Symposium on Information Theory (ISIT).

[21]  Roy D. Yates,et al.  Age of Information: Updates with Priority , 2018, 2018 IEEE International Symposium on Information Theory (ISIT).