An Environment for Automated Measuring of Energy Consumed by Android Mobile Devices

Dzhagaryan, Armen; Milenkoviź, Aleksandar; Milošević, Mladen; Jovanov, Emil

doi:10.5220/0005950800280039

Cited by 6 publications

(6 citation statements)

References 9 publications

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“…Power profilers are built to accomplish a very specific task energy-wise. For instance, they might be used to measure the consumed energy of the device [37], or some specific component [38], or monitor the device for anomalies [16,39]. Therefore, the granularity-the level at which the profiler can deliver power measurement or estimation [25]-would differ from one case to another.…”

Section: Defining the Purpose Of The Profilermentioning

confidence: 99%

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Power profiling and monitoring in embedded systems: A comparative study and a novel methodology based on NARX neural networks

Djedidi

Djeziri

2020

Journal of Systems Architecture

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Power consumption in electronic systems is an essential feature for the management of energy autonomy, performance analysis, and the aging monitoring of components. Thus, several research studies have been devoted to the development of power models and profilers for embedded systems. Each of these models is designed to fit a specific usage context. This paper is a part of a series of works dedicated to modeling and monitoring embedded systems in airborne equipment. The objective of this paper is twofold. Firstly, it presents an overview of the most used models in the literature. Then, it offers a comparative analysis of these models according to a set of criteria, such as the modeling assumptions, the necessary instrumentation necessary, the accuracy, and the complexity of implementation.Secondly, we introduce a new power estimator for ARM-Based embedded systems, with component-level granularity. The estimator is based on NARX neural networks and used to monitor power for diagnosis purposes. The obtained experimental results highlight the advantages and limitations of the models presented in the literature and demonstrate the effectiveness of the proposed NARX, having obtained the best results in its class for a smartphone (An online Mean Absolute Percentage Error = 2.2%).

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Section: Defining the Purpose Of The Profilermentioning

confidence: 99%

“…Additionally, N 3 caused no issues or bottlenecks for the system, as indicated by its overhead. The overhead is computed by running the system with the profiler and then comparing its power consumption and load to data from instances that had no profiling [37]. N 3 it had only a 3% load overhead during the test.…”

Section: Performance Evaluation and Comparisonmentioning

confidence: 99%

Power profiling and monitoring in embedded systems: A comparative study and a novel methodology based on NARX neural networks

Djedidi

Djeziri

2020

Journal of Systems Architecture

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“…These devices are abundantly available, and easy to program. More importantly, they come with a limited power supply, which incited developers to study the battery life [5], construct energy and power consumption models [6][7][8], and closely follow the influence of user actions and applications on power consumption [9]. Furthermore, studying the embedded systems' power profile has led to improving energy efficiency [10], reliability [11,12], and also monitoring and detecting anomalies [13][14][15] and energy hogs [16].…”

Section: Power Consumption Modeling In Embedded Socsmentioning

confidence: 99%

Incremental Modeling and Monitoring of Embedded CPU-GPU Chips

Djedidi

Djeziri

2020

Processes

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This paper presents a monitoring framework to detect drifts and faults in the behavior of the central processing unit (CPU)-graphics processing unit (GPU) chips powering them. To construct the framework, an incremental model and a fault detection and isolation (FDI) algorithm are hereby proposed. The reference model is composed of a set of interconnected exchangeable subsystems that allows it to be adapted to changes in the structure of the system or operating modes, by replacing or extending its components. It estimates a set of variables characterizing the operating state of the chip from only two global inputs. Then, through analytical redundancy, the estimated variables are compared to the output of the system in the FDI module, which generates alarms in the presence of faults or drifts in the system. Furthermore, the interconnected nature of the model allows for the direct localization and isolation of any detected abnormalities. The implementation of the proposed framework requires no additional instrumentation as the used variables are measured by the system. Finally, we use multiple experimental setups for the validation of our approach and also proving that it can be applied to most of the existing embedded systems.

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“…For the evaluation, we use Google's Nexus 4 (Google, 2014c, p. 4) and OnePlus One (OnePlus, 2015) smartphones and the measurement setup described in (Dzhagaryan et al, 2016.…”

Section: File Transfers In Mobile Cloudmentioning

confidence: 99%

“…An OnePlus One smartphone is used to initiate a series of file uploads to a server and a series of file downloads from a server. For each file transfer, the execution time and the energy consumed are measured using a measurement setup that involves a battery simulator (Dzhagaryan et al, 2016. The smartphone is connected to the Internet over its WLAN interface, and file transfers take place over a secure shell ( Figure 2(a) shows the measured effective throughput for uncompressed uploads as a function of the file size, US, and the network connection throughput for uploads, Th.UP.…”

Section: Model Verificationmentioning

confidence: 99%

Analytical Models for Evaluating Effectiveness of Compressed File Transfers in Mobile Computing

Dzhagaryan

Milenkoviź

2016

Proceedings of the 6th International Joint Conference on Pervasive and Embedded Computing and Communication Systems

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The importance of optimizing data transfers between mobile computing devices and the cloud is increasing with an exponential growth of mobile data traffic. Lossless data compression can be essential in increasing communication throughput, reducing communication latency, achieving energy-efficient communication, and making effective use of available storage. In this paper we introduce analytical models for estimating effective throughput and energy efficiency of uncompressed data transfers and compressed data transfers that utilize common compression utilities. The proposed analytical models are experimentally verified using state-of-the-art mobile devices. These models are instrumental in developing a framework for seamless optimization of data file transfers.

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An Environment for Automated Measuring of Energy Consumed by Android Mobile Devices

Cited by 6 publications

References 9 publications

Power profiling and monitoring in embedded systems: A comparative study and a novel methodology based on NARX neural networks

Power profiling and monitoring in embedded systems: A comparative study and a novel methodology based on NARX neural networks

Incremental Modeling and Monitoring of Embedded CPU-GPU Chips

Analytical Models for Evaluating Effectiveness of Compressed File Transfers in Mobile Computing

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