*Result*: Impact of virtual threads and garbage collection on energy efficiency of Java applications for battery powered IoT devices.
Title:
Impact of virtual threads and garbage collection on energy efficiency of Java applications for battery powered IoT devices.
Authors:
Shanjai Kumar S; School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, 632014, India., Sanjai BN; School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, 632014, India., Etheeswar Kaarthi S; School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, 632014, India., Sivakumar V; School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, 632014, India. sivakumar.v@vit.ac.in., Jagadeesan S; School of Computer Science Engineering and Information Systems (SCORE), Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, 632014, India. s.jagadeesan@vit.ac.in.
Source:
Scientific reports [Sci Rep] 2026 Mar 14. Date of Electronic Publication: 2026 Mar 14.
Publication Model:
Ahead of Print
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
Imprint Name(s):
Original Publication: London : Nature Publishing Group, copyright 2011-
References:
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Malik, U. M., Javed, M. A., Zeadally, S. & Islam, S. Energy-efficient fog computing for 6G-enabled massive IoT: Recent trends and future opportunities. IEEE Internet Things J. 9(16), 14572–14594. https://doi.org/10.1109/JIOT.2021.3068056 (2022).
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Zakarya, M. et al. CoLocateMe: Aggregation-based, energy, performance and cost aware VM placement and consolidation in heterogeneous IaaS clouds. IEEE Trans. Serv. Comput. 16, 1023–1038. https://doi.org/10.1109/TSC.2022.3181375 (2023).
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Sinha, A. & Chandrakasan, A. Dynamic power management in wireless sensor networks. IEEE Des. Test Comput. 18, 62–74. https://doi.org/10.1109/54.914626 (2001).
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Venu, S. & Zubair Rahman, A. M. J. M. Energy and cluster based efficient routing for broadcasting in mobile ad hoc networks. Clust. Comput. 22(Suppl 1), 661–671. https://doi.org/10.1007/s10586-018-2255-3 (2019).
Khan, A. A., Zakarya, M. & Khan, R. H2—A hybrid heterogeneity aware resource orchestrator for cloud platforms. IEEE Syst. J. 13, 3873–3876. https://doi.org/10.1109/JSYST.2019.2899913 (2019).
Zakarya, M. et al. PerficientCloudSim: A tool to simulate large-scale computation in heterogeneous clouds. J. Supercomput. 77, 3959–4013. https://doi.org/10.1007/s11227-020-03425-5 (2021).
Khan, A. A. et al. A migration aware scheduling technique for real-time aperiodic tasks over multiprocessor systems. IEEE Access 7, 27859–27873. https://doi.org/10.1109/ACCESS.2019.2901411 (2019).
Ali, H. et al. An energy and performance aware scheduler for real-time tasks in cloud datacentres. IEEE Access 8, 161288–161303. https://doi.org/10.1109/ACCESS.2020.3020843 (2020).
Ali, H., Zakarya, M., Rahman, I. U., Khan, A. A. & Buyya, R. FollowMe@LS: Electricity price and source aware resource management in geographically distributed heterogeneous datacenters. J. Syst. Softw. 175, 110907. https://doi.org/10.1016/j.jss.2021.110907 (2021).
ARM Ltd. Energy efficiency in embedded systems. White Paper, Cambridge, UK. Available at: https://www.arm.com/why-arm/energy-efficiency (2020).
Texas Instruments. Low power design for IoT devices. TI White Paper, Dallas, TX, USA. Available at: https://www.ti.com.cn/lit/pdf/TIDUDA8 (2021).
Nordic Semiconductor. Battery life optimization in BLE devices. Technical Guide, Trondheim, Norway. Available at: https://www.nordicsemi.com/Products/Low-power-solutions (2022).
Raspberry Pi Foundation. Measuring power consumption on embedded devices. Raspberry Pi Documentation, Cambridge, UK. Available at: https://www.raspberrypi.com/documentation/computers/raspberry-pi.html (2021).
Google Android Team. Power management overview. Android Developer Documentation, Mountain View, CA, USA. Available at: https://developer.android.com/topic/performance/power (2023).
SPEC Consortium. SPECjvm2008 benchmark suite (Standard Performance Evaluation Corporation, 2020). Available at: https://www.spec.org/jvm2008/docs/.
Blackburn, S. M. et al. The DaCapo benchmarks: Java benchmarking development and analysis. In Proc. 21st ACM SIGPLAN Conf. on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA), Portland, OR, USA. Available at: https://www.steveblackburn.org/pubs/papers/dacapo-oopsla-2006.pdf (2006).
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Uber Engineering. JVM profiler: Performance and energy metrics. Uber Tech Blog, San Francisco, CA, USA. Available at: https://eng.uber.com/jvm-profiler/ (2020).
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V., S., R., S. & V., Y. An IoT-based energy meter for energy level monitoring, predicting, and optimization. In Challenges and Opportunities for the Convergence of IoT, Big Data, and Cloud Computing (ed. Velayutham, S.) 48–65 (IGI Global Scientific Publishing, 2021). https://doi.org/10.4018/978-1-7998-3111-2.ch004.
Intel Developer Zone. Power Gadget tool for energy profiling. Intel Corporation, Santa Clara, CA, USA. Available at: https://www.intel.com/content/www/us/en/developer/tools/power-gadget.html(2022).
Evans, J. Understanding GC logs. Plumbr Blog, Tallinn, Estonia. Available at: https://plumbr.io/blog/gc/gc-log-analysis (2020).
Azul Systems. GC tuning for energy efficiency. Technical Guide, Azul Inc., Sunnyvale, CA, USA. Available at: https://www.azul.com/gc-tuning-guide/ (2022).
Hubblo Org. Scaphandre: Power consumption agent for JVM. GitHub Repository, France. Available at: https://github.com/hubblo-org/scaphandre (2023).
OpenJDK Contributors. ZGC: Scalable low-latency garbage collector. OpenJDK Wiki. Available at: https://wiki.openjdk.org/display/zgc/Main (2022).
Red Hat Developers. Shenandoah GC: low pause, low power. Developer Blog, Red Hat Inc., Raleigh, NC, USA. Available at: https://developers.redhat.com/blog/2021/03/15/shenandoah-garbage-collector-low-pause-and-low-power (2021).
Sivakumar, V., Shanjai Kumar, S., Sanjai, B. N. & Etheeswar Kaarthi, S. An energy-efficient Java-based Internet of Things (IoT) system and a method for improving energy efficiency in Java-based IoT systems. Application No. 202541119285, Vellore Institute of Technology (2025).
Turkmanovic, H., Popovic, I., Drajic, D. & Cica, Z. Green computing for IoT – Software approach. Facta Univ. Ser. Electron. Energ. 35, 541–555. https://doi.org/10.2298/FUEE2204541T (2022).
Thanh, N. H. et al. Energy-aware service function chain embedding in edge–cloud environments for IoT applications. IEEE Internet Things J. 8, 13465–13486. https://doi.org/10.1109/JIOT.2021.3064986 (2021).
Eclipse Foundation. Energy measurement in Java. Eclipse Newsletter, Ottawa, Canada. Available at: https://www.eclipse.org/community/eclipse_newsletter/2022/september/energy/ (2022).
Guntreddi, V. & Sivakumar, V. Deep learning–based glaucoma detection using majority voting ensemble of ResNet50, VGG16, and Swin Transformer. Res. Eng. 28, 107229. https://doi.org/10.1016/j.rineng.2025.107229 (2025).
Ournani, Z., Belgaid, M. C., Rouvoy, R., Rust, P. & Penhoat, J. Evaluating the impact of java virtual machines on energy consumption. In Proceedings of the 15th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM) (ESEM '21) 1–11 (Association for Computing Machinery, 2021). https://doi.org/10.1145/3475716.3475774.
Turkmanović, H., Popović, I. & Rajović, V. Toward energy efficient battery state of charge estimation on embedded platforms. Electronics 13(21), 4256. https://doi.org/10.3390/electronics13214256 (2024).
Malik, U. M., Javed, M. A., Zeadally, S. & Islam, S. Energy-efficient fog computing for 6G-enabled massive IoT: Recent trends and future opportunities. IEEE Internet Things J. 9(16), 14572–14594. https://doi.org/10.1109/JIOT.2021.3068056 (2022).
Drosopoulou, E. Energy-efficient Java: how to measure and optimize JVM carbon/lifecycle footprint. Java Code Geeks. Available at: https://www.javacodegeeks.com/2025/08/energy-efficient-java-green-software-how-to-measure-and-optimize-jvm-carbon-lifecycle-footprint.html (2025).
Green Software Foundation. Software carbon intensity specification. GSF Technical Standard. Available at: https://greensoftware.foundation/software-carbon-intensity/ (2022).
Alioto, M. Energy-efficient IoT systems. IEEE Trans. Circuits Syst. I Reg. Pap. 65, 2600–2611. https://doi.org/10.1109/TCSI.2018.2843535 (2018).
Hauswirth, M. & Chilimbi, T. M. Low-overhead memory leak detection using adaptive statistical profiling. In Proc. 11th Int. Conf. on Architectural Support for Programming Languages and Operating Systems (ASPLOS), Boston, MA, USA. Available at: https://web.stanford.edu/class/archive/cs/cs295/cs295.1086/papers/p156-hauswirth.pdf (2004).
Casto, G. The energy efficiency of JVMs and the role of GraalVM. DZone Technical Article, DZone Media. Available at: https://dzone.com/articles/energy-efficiency-jvms-role-graalvm (2025).
Raza, S. et al. Energy-aware IoT protocols. ACM Trans. Sens. Netw. 10, 1–28. https://doi.org/10.1145/2638728 (2014).
JetBrains. Asynchronous programming in Java. JetBrains Blog, Prague, Czech Republic. Available at: https://blog.jetbrains.com/idea/2023/03/asynchronous-programming-in-java/ (2023).
Oracle Labs. Project Loom: virtual threads explained. Technical Specification, Oracle Corporation, Redwood Shores, CA, USA. Available at: https://openjdk.org/projects/loom/ (2023).
Hussain, H. et al. Energy efficient real-time tasks scheduling on high-performance edge-computing systems using genetic algorithm. IEEE Access 12, 54879–54892. https://doi.org/10.1109/ACCESS.2024.3388837 (2024).
Zakarya, M. et al. CoLocateMe: Aggregation-based, energy, performance and cost aware VM placement and consolidation in heterogeneous IaaS clouds. IEEE Trans. Serv. Comput. 16, 1023–1038. https://doi.org/10.1109/TSC.2022.3181375 (2023).
Hasan, B. T. & Idrees, A. K. Edge computing for IoT. In Learning Techniques for the Internet of Things (eds Donta, P. K. et al.) (Springer, 2024).
Sinha, A. & Chandrakasan, A. Dynamic power management in wireless sensor networks. IEEE Des. Test Comput. 18, 62–74. https://doi.org/10.1109/54.914626 (2001).
Koomey, J., Schmidt, Z. & Das, T. Electricity demand growth and data centers: A guide for the perplexed. Bipartisan Policy Center, Washington, DC, USA. Available at: https://bipartisanpolicy.org/wp-content/uploads/2025/02/BPC-Report-Electricity-Demand-Growth-and-Data-Centers-A-Guide-for-the-Perplexed.pdf (2025).
Venu, S. & Zubair Rahman, A. M. J. M. Energy and cluster based efficient routing for broadcasting in mobile ad hoc networks. Clust. Comput. 22(Suppl 1), 661–671. https://doi.org/10.1007/s10586-018-2255-3 (2019).
Khan, A. A., Zakarya, M. & Khan, R. H2—A hybrid heterogeneity aware resource orchestrator for cloud platforms. IEEE Syst. J. 13, 3873–3876. https://doi.org/10.1109/JSYST.2019.2899913 (2019).
Zakarya, M. et al. PerficientCloudSim: A tool to simulate large-scale computation in heterogeneous clouds. J. Supercomput. 77, 3959–4013. https://doi.org/10.1007/s11227-020-03425-5 (2021).
Khan, A. A. et al. A migration aware scheduling technique for real-time aperiodic tasks over multiprocessor systems. IEEE Access 7, 27859–27873. https://doi.org/10.1109/ACCESS.2019.2901411 (2019).
Ali, H. et al. An energy and performance aware scheduler for real-time tasks in cloud datacentres. IEEE Access 8, 161288–161303. https://doi.org/10.1109/ACCESS.2020.3020843 (2020).
Ali, H., Zakarya, M., Rahman, I. U., Khan, A. A. & Buyya, R. FollowMe@LS: Electricity price and source aware resource management in geographically distributed heterogeneous datacenters. J. Syst. Softw. 175, 110907. https://doi.org/10.1016/j.jss.2021.110907 (2021).
ARM Ltd. Energy efficiency in embedded systems. White Paper, Cambridge, UK. Available at: https://www.arm.com/why-arm/energy-efficiency (2020).
Texas Instruments. Low power design for IoT devices. TI White Paper, Dallas, TX, USA. Available at: https://www.ti.com.cn/lit/pdf/TIDUDA8 (2021).
Nordic Semiconductor. Battery life optimization in BLE devices. Technical Guide, Trondheim, Norway. Available at: https://www.nordicsemi.com/Products/Low-power-solutions (2022).
Raspberry Pi Foundation. Measuring power consumption on embedded devices. Raspberry Pi Documentation, Cambridge, UK. Available at: https://www.raspberrypi.com/documentation/computers/raspberry-pi.html (2021).
Google Android Team. Power management overview. Android Developer Documentation, Mountain View, CA, USA. Available at: https://developer.android.com/topic/performance/power (2023).
SPEC Consortium. SPECjvm2008 benchmark suite (Standard Performance Evaluation Corporation, 2020). Available at: https://www.spec.org/jvm2008/docs/.
Blackburn, S. M. et al. The DaCapo benchmarks: Java benchmarking development and analysis. In Proc. 21st ACM SIGPLAN Conf. on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA), Portland, OR, USA. Available at: https://www.steveblackburn.org/pubs/papers/dacapo-oopsla-2006.pdf (2006).
Green Software Foundation. Green software patterns: energy-aware design. Open Source Catalog. Available at: https://patterns.greensoftware.foundation/ (2022).
Uber Engineering. JVM profiler: Performance and energy metrics. Uber Tech Blog, San Francisco, CA, USA. Available at: https://eng.uber.com/jvm-profiler/ (2020).
Oracle Blogs. Virtual threads in Java. Java Magazine, Oracle Corporation, Redwood Shores, CA, USA. Available at: https://blogs.oracle.com/javamagazine/post/java-virtual-threads (2023).
V., S., R., S. & V., Y. An IoT-based energy meter for energy level monitoring, predicting, and optimization. In Challenges and Opportunities for the Convergence of IoT, Big Data, and Cloud Computing (ed. Velayutham, S.) 48–65 (IGI Global Scientific Publishing, 2021). https://doi.org/10.4018/978-1-7998-3111-2.ch004.
Intel Developer Zone. Power Gadget tool for energy profiling. Intel Corporation, Santa Clara, CA, USA. Available at: https://www.intel.com/content/www/us/en/developer/tools/power-gadget.html(2022).
Evans, J. Understanding GC logs. Plumbr Blog, Tallinn, Estonia. Available at: https://plumbr.io/blog/gc/gc-log-analysis (2020).
Azul Systems. GC tuning for energy efficiency. Technical Guide, Azul Inc., Sunnyvale, CA, USA. Available at: https://www.azul.com/gc-tuning-guide/ (2022).
Hubblo Org. Scaphandre: Power consumption agent for JVM. GitHub Repository, France. Available at: https://github.com/hubblo-org/scaphandre (2023).
OpenJDK Contributors. ZGC: Scalable low-latency garbage collector. OpenJDK Wiki. Available at: https://wiki.openjdk.org/display/zgc/Main (2022).
Red Hat Developers. Shenandoah GC: low pause, low power. Developer Blog, Red Hat Inc., Raleigh, NC, USA. Available at: https://developers.redhat.com/blog/2021/03/15/shenandoah-garbage-collector-low-pause-and-low-power (2021).
Sivakumar, V., Shanjai Kumar, S., Sanjai, B. N. & Etheeswar Kaarthi, S. An energy-efficient Java-based Internet of Things (IoT) system and a method for improving energy efficiency in Java-based IoT systems. Application No. 202541119285, Vellore Institute of Technology (2025).
Contributed Indexing:
Keywords: Embedded systems; Energy efficiency; Event-driven design; IoT devices; Low-power computing; Software architecture
Entry Date(s):
Date Created: 20260315 Latest Revision: 20260315
Update Code:
20260315
DOI:
10.1038/s41598-026-40112-6
PMID:
41832169
Database:
MEDLINE
*Further Information*
*Declarations. Competing interests: The authors declared that they have no conflicts of interest to this work. The authors declare no competing financial interests. The authors are inventors on an Indian patent application related to the work reported in this manuscript, Reference No:45.*