On Stochastic Optimization for Smartphone CPU Energy Consumption Decrease
Extending smartphone working time is an ongoing endeavour becoming more and more important with each passing year. It could be achieved by more advanced hardware or by introducing energy-aware practices to software, and the latter is a more accessible approach. As the CPU is one of the most power-hu...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Russian Academy of Sciences, St. Petersburg Federal Research Center
2023-09-01
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| Series: | Информатика и автоматизация |
| Subjects: | |
| Online Access: | http://ia.spcras.ru/index.php/sp/article/view/15865 |
| _version_ | 1827806974867144704 |
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| author | Makar Pelogeiko Stanislav Sartasov Oleg Granichin |
| author_facet | Makar Pelogeiko Stanislav Sartasov Oleg Granichin |
| author_sort | Makar Pelogeiko |
| collection | DOAJ |
| description | Extending smartphone working time is an ongoing endeavour becoming more and more important with each passing year. It could be achieved by more advanced hardware or by introducing energy-aware practices to software, and the latter is a more accessible approach. As the CPU is one of the most power-hungry smartphone devices, Dynamic Voltage Frequency Scaling (DVFS) is a technique to adjust CPU frequency to the current computational needs, and different algorithms were already developed, both energy-aware and energy-agnostic kinds. Following our previous work on the subject, we propose a novel DVFS approach to use simultaneous perturbation stochastic approximation (SPSA) with two noisy observations for tracking the optimal frequency and implementing several algorithms based on it. Moreover, we also address an issue of hardware lag between a signal for the CPU to change frequency and its actual update. As Android OS could use a default task scheduler or an energy-aware one, which is capable of taking advantage of heterogeneous mobile CPU architectures such as ARM big.LITTLE, we also explore an integration scheme between the proposed algorithms and OS schedulers. A model-based testing methodology to compare the developed algorithms against existing ones is presented, and a test suite reflecting real-world use case scenarios is outlined. Our experiments show that the SPSA-based algorithm works well with EAS with a simplified integration scheme, showing CPU performance comparable to other energy-aware DVFS algorithms and a decreased energy consumption. |
| first_indexed | 2024-03-11T21:50:28Z |
| format | Article |
| id | doaj.art-65f9f4b74a3640159683b97272c586a5 |
| institution | Directory Open Access Journal |
| issn | 2713-3192 2713-3206 |
| language | English |
| last_indexed | 2024-03-11T21:50:28Z |
| publishDate | 2023-09-01 |
| publisher | Russian Academy of Sciences, St. Petersburg Federal Research Center |
| record_format | Article |
| series | Информатика и автоматизация |
| spelling | doaj.art-65f9f4b74a3640159683b97272c586a52023-09-26T09:02:03ZengRussian Academy of Sciences, St. Petersburg Federal Research CenterИнформатика и автоматизация2713-31922713-32062023-09-012251004103310.15622/ia.22.5.315865On Stochastic Optimization for Smartphone CPU Energy Consumption DecreaseMakar Pelogeiko0Stanislav Sartasov1Oleg Granichin2St. Petersburg State University (SPbSU)St. Petersburg State University (SPbSU)St. Petersburg State University (SPbSU)Extending smartphone working time is an ongoing endeavour becoming more and more important with each passing year. It could be achieved by more advanced hardware or by introducing energy-aware practices to software, and the latter is a more accessible approach. As the CPU is one of the most power-hungry smartphone devices, Dynamic Voltage Frequency Scaling (DVFS) is a technique to adjust CPU frequency to the current computational needs, and different algorithms were already developed, both energy-aware and energy-agnostic kinds. Following our previous work on the subject, we propose a novel DVFS approach to use simultaneous perturbation stochastic approximation (SPSA) with two noisy observations for tracking the optimal frequency and implementing several algorithms based on it. Moreover, we also address an issue of hardware lag between a signal for the CPU to change frequency and its actual update. As Android OS could use a default task scheduler or an energy-aware one, which is capable of taking advantage of heterogeneous mobile CPU architectures such as ARM big.LITTLE, we also explore an integration scheme between the proposed algorithms and OS schedulers. A model-based testing methodology to compare the developed algorithms against existing ones is presented, and a test suite reflecting real-world use case scenarios is outlined. Our experiments show that the SPSA-based algorithm works well with EAS with a simplified integration scheme, showing CPU performance comparable to other energy-aware DVFS algorithms and a decreased energy consumption.http://ia.spcras.ru/index.php/sp/article/view/15865android osdynamic voltage frequency scalingstochastic optimizationspsaenergy consumption |
| spellingShingle | Makar Pelogeiko Stanislav Sartasov Oleg Granichin On Stochastic Optimization for Smartphone CPU Energy Consumption Decrease Информатика и автоматизация android os dynamic voltage frequency scaling stochastic optimization spsa energy consumption |
| title | On Stochastic Optimization for Smartphone CPU Energy Consumption Decrease |
| title_full | On Stochastic Optimization for Smartphone CPU Energy Consumption Decrease |
| title_fullStr | On Stochastic Optimization for Smartphone CPU Energy Consumption Decrease |
| title_full_unstemmed | On Stochastic Optimization for Smartphone CPU Energy Consumption Decrease |
| title_short | On Stochastic Optimization for Smartphone CPU Energy Consumption Decrease |
| title_sort | on stochastic optimization for smartphone cpu energy consumption decrease |
| topic | android os dynamic voltage frequency scaling stochastic optimization spsa energy consumption |
| url | http://ia.spcras.ru/index.php/sp/article/view/15865 |
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