Dynamic frequency scaling on android platforms for energy consumption reduction

Bezerra, Pâmela Thays Lins; Araujo, Leandro A.B.; Ribeiro, Giovane Boaviagem; Neto, Antonio Correia de Sa Barreto; Silva-Filho, Abel G.; Siebra, Clauirton; Silva, Fábio Q. B. da; Santos, André L. M.; Mascaro, Angelica A.; Costa, Paulo

doi:10.1145/2512840.2512867

Cited by 13 publications

(3 citation statements)

References 3 publications

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“…Tseng et al [214] adapted the allocation of CPU resources to applications based on their delay-sensitivity. Further approaches adjust the frequency and voltage of devices (Dynamic Voltage and Frequency Scaling) [212], [213], CPU frequency [218] or the number of cores [216]. Muhuri et al [217] considered linguistic feedback from users to adapt CPU frequency accordingly.…”

Section: Optimization Approachesmentioning

confidence: 99%

A Survey of Performance Optimization for Mobile Applications

Hort

Kechagia

Sarro

et al. 2022

IIEEE Trans. Software Eng.

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To ensure user satisfaction and success of mobile applications, it is important to provide highly performant applications. This is particularly important for resource-constrained systems such as mobile devices. Thereby, non-functional performance characteristics, such as energy and memory consumption, play an important role for user satisfaction. This paper provides a comprehensive survey of non-functional performance optimization for Android applications. We collected 156 unique publications, published between 2008 and 2020, that focus on the optimization of performance of mobile applications. We target our search at four performance characteristics: responsiveness, launch time, memory and energy consumption. For each performance characteristic, we categorize optimization approaches based on the method used in the corresponding publications. Furthermore, we identify research gaps in the literature for future work.

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Section: Optimization Approachesmentioning

confidence: 99%

A Survey of Performance Optimization for Mobile Applications

Hort

Kechagia

Sarro

et al. 2022

IIEEE Trans. Software Eng.

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“…DVFS has been traditionally used for personal computers and is now making its way to MEC servers and smart consumer devices including smartphones and tablets to conserve energy [6].…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Computation Offloading in Delay-Constrained Massive MIMO Enabled Edge Network Using Data Partitioning

Malik

2020

IEEE Trans. Wireless Commun.

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We study a wireless edge-computing system which allows multiple users to simultaneously offload computation-intensive tasks to multiple massive-MIMO access points, each with a collocated multiaccess edge computing (MEC) server. Massive-MIMO enables simultaneous uplink transmissions from all users, significantly shortening the data offloading time compared to sequential protocols, and makes the three phases of data offloading, computing, and downloading have comparable durations. Based on this three-phase structure, we formulate a novel problem to minimize a weighted sum of the energy consumption at both the users and the MEC server under a round-trip latency constraint, using a combination of data partitioning, transmit power control and CPU frequency scaling at both the user and server ends. We design a novel nested primal-dual algorithm using two different methods to solve this problem efficiently. Optimized solutions show that for larger requests, more data is offloaded to the MECs to reduce local computation time in order to meet the latency constraint, despite higher energy cost of wireless transmissions. Massive-MIMO channel estimation errors under pilot contamination also causes more data to be offloaded to the MECs. Compared to binary offloading, partial offloading with data partitioning is superior and leads to significant reduction in the overall energy consumption.

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“…Most apps on smartphones are not delay tolerant, and their acceleration is often at higher expenses of energy consumption. In order to balance performance and power consumption for these apps, smartphones are usually designed with dynamic voltage scaling (DVS) by integrating static CMOS logic into microprocessors [8], [9]. DVS is a powerful technique to reduce energy consumption and is widely employed in various embedded systems [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Energy Optimization With Dynamic Task Scheduling Mobile Cloud Computing

Li¹,

Chen

Dai

et al. 2017

IEEE Systems Journal

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The smartphone is a typical cyberphysical system (CPS). It must be low energy consuming and highly reliable to deal with the simple but frequent interactions with the cloud, which constitutes the cloud-integrated CPS. Dynamic voltage scaling (DVS) has emerged as a critical technique to leverage power management by lowering the supply voltage and frequency of processors. In this paper, based on the DVS technique, we propose a novel Energy-aware Dynamic Task Scheduling (EDTS) algorithm to minimize the total energy consumption for smartphones, while satisfying stringent time constraints and the probability constraint for applications. Experimental results indicate that the EDTS algorithm can significantly reduce energy consumption for CPS, as compared to the critical path scheduling method and the parallelism-based scheduling algorithm. Index Terms-Cloud-integrated cyberphysical system (CPS), CPS, dynamic scheduling, dynamic voltage scaling (DVS). 1932-8184

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Dynamic frequency scaling on android platforms for energy consumption reduction

Cited by 13 publications

References 3 publications

A Survey of Performance Optimization for Mobile Applications

A Survey of Performance Optimization for Mobile Applications

Energy-Efficient Computation Offloading in Delay-Constrained Massive MIMO Enabled Edge Network Using Data Partitioning

Energy Optimization With Dynamic Task Scheduling Mobile Cloud Computing

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