Making Android Run on Time

Yan, Yin; Dantu, Karthik; Ko, Steven Y.; Vítek, Jan; Ziarek, Lukasz

doi:10.1109/rtas.2017.38

Cited by 7 publications

(5 citation statements)

References 20 publications

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“…This article extends our previous work [6,32] and presents a comprehensive overview of the RTDroid programming model. The main additions are the implementation of a real-time sound processing framework (Section 4), two additional applications (acoustic ranging and augmented reality), the reproduction of results from [6] (surround sound), and two new microbenchmarks measuring sound latency estimation and acoustic ranging performance.…”

Section: Introductionmentioning

confidence: 53%

Can Android Run on Time? Extending and Measuring the Android Platform's Timeliness

Yan

Gokul

Dantu

et al. 2018

ACM Trans. Embed. Comput. Syst.

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Time predictability is difficult to achieve in the complex, layered execution environments that are common in modern embedded devices such as smartphones. We explore adopting the Android programming model for a range of embedded applications that extends beyond mobile devices, under the constraint that changes to widely used libraries should be minimized. The challenges we explore include the interplay between real-time activities and the rest of the system, how to express the timeliness requirements of components, and how well those requirements can be met on stock embedded platforms. We detail the design and implementation of our modifications to the Android framework along with a real-time VM and OS, and we provide experimental data validating feasibility over five applications.

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Section: Introductionmentioning

confidence: 53%

Can Android Run on Time? Extending and Measuring the Android Platform's Timeliness

Yan

Gokul

Dantu

et al. 2018

ACM Trans. Embed. Comput. Syst.

Self Cite

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“…A whole implementation of the proposed architecture is successfully tested with RTDroid [38] as a real-time based Linux distribution and a dedicated RBS implementation with Drools for dynamic controllers reconfiguration. The proposed architecture may be applied to a various domain of application for digital industry, autonomous car or unmanned vehicle, defence purpose and smart cities where adaptive control should be enhanced by expert domain implemented in a knowledge base.…”

Section: Discussionmentioning

confidence: 99%

Dynamic Reconfiguration of Intelligence for High Behaviour Adaptability of Autonomous Distributed Discrete-Event Systems

Gharsellaoui

Khalgui

2019

IEEE Access

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This paper deals with the intelligence adaptation of distributed real-time embedded control systems when scenarios of reconfiguration happen in their hardware or software level. The reconfiguration process is a composition of controllers reconfiguration by adding/deleting or updating tasks and intelligence reconfiguration by adding/deleting and updating the rule base. The system architecture is composed of an application layer implemented as the real-time periodic tasks and an intelligence layer for autonomous and adaptive control behavior. In this research work, a rule-based system is used as the artificial intelligence component, where we propose to optimize the inference process by splitting the rule base into two sub-bases; the effective one and the general meta base. With this facility, the coordination in decision making for the distributed platform and system Quality of Service (QoS) in complex and robust system implementation should be faced by new strategies and correct policies. In this sense, we propose a new protocol for the coordination in the two levels of the reconfiguration process to get correctness in the system results. Dealing with performance, we propose to supervise the intelligence QoS of the whole distributed system. Also, we present the correctness of the coordination between the different decisions by the field of the coordination factor. An implementation of the paper contribution with Drools as an integrated rule-based system framework to RTDroid and a discussion of the inferring time and the memory consumption are presented in this paper. INDEX TERMS Real-time embedded control systems, intelligence reconfiguration, rule-based system, intelligence QoS, coordination factor, intelligence impact.

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“…Further proposed modications for enhanced real-time support in Android include the works by Kalkov et al [43] and Yan et al [44,45], adopting a real-time Java Virtual Machine run-time platform to control interferences due to the garbage collector, enhancing the memory allocator, and improving the accessibility of scheduling services from unprivileged applications, without the need for a Binder call to a privileged process. Besides these, an additional work by Yan et al [46] proposed to extend the Android interfaces for the development of soft real-time applications, by introducing statically specied memory bounds and priority awareness. 6 For details, refer to commit history of binder.c as available at: https://android.googlesource.…”

Section: Energy-aware Real-time Schedulingmentioning

confidence: 99%

Energy-efficient low-latency audio on android

Balsini

Cucinotta

Abeni

et al. 2019

Journal of Systems and Software

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Counting more than two billion devices, Android is nowadays one of the most popular open-source general-purpose operating systems, based on Linux. Because of the diversity of applications that can be installed, it manages a number of dierent workloads, many of them requiring performance/QoS guarantees. When running audio processing applications, the user would like an uninterrupted, glitch-free, output stream that reacts to the user input, typically with a delay not bigger than 4 − 10 ms, while keeping the energy consumption of the mobile device as low as possible. This work focuses on improvements to the real-time audio processing performance on Android that preserves energy-eciency. Such improvements are achieved by using a deadline based scheduler and an adaptive scheduling strategy that dynamically and proactively modulates the allocated runtime. The proposed strategy is evaluated through an extensive experimentation, showing that 1) compared to the existing way to ensure low-latency audio processing, the proposed mechanism provides an energy saving of almost 40%, and 2) compared to the existing way to achieve a good balance between power consumption and latency in a glitch-free audio processing experience, the proposed solution reduces audio latency from 26.67 ms to 2.67 ms, at the expense of a limited

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Making Android Run on Time

Cited by 7 publications

References 20 publications

Can Android Run on Time? Extending and Measuring the Android Platform's Timeliness

Can Android Run on Time? Extending and Measuring the Android Platform's Timeliness

Dynamic Reconfiguration of Intelligence for High Behaviour Adaptability of Autonomous Distributed Discrete-Event Systems

Energy-efficient low-latency audio on android

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