A Thread Behavior-Based Memory Management Framework on Multi-core Smartphone

Zhu, Zongwei; Li, Xi; Liu, Hengchang; Ji, Cheng; Xu, Yuan; Zhou, Xuehai; Sun, Beilei

doi:10.1109/iceccs.2014.21

Cited by 1 publication

(2 citation statements)

References 13 publications

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“…The previous research [10] we performed differs from others since it either optimizes the algorithm's time-complexity to improve allocation efficiency, but it does not modify the structure of the algorithm to manage fragmentation. In this paper, we optimized the original MMBTB design framework based on the needs of users, which can meet the user's requirements for selecting plug-ins to achieve different optimization goals and has clear boundaries with the threads and the OS.…”

Section: Related Workmentioning

confidence: 99%

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A Thread-Oriented Memory Resource Management Framework for Mobile Edge Computing

Zhu

et al. 2019

IEEE Access

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Increasing the number of cores is one of the most effective methods to enhance performance. However, an extensive experimental study on mobile edge computing (e.g., Android devices) indicates that the memory management system has gradually become a key performance bottleneck. Studies on improving memory management mainly focus on exploring the trade-off between avoiding fragmentation and improving allocation efficiency. From our previous research, we know that the fragmentation is no longer a crucial bottleneck; instead, inter-and intra-thread behavior should be focused on, and thus, we introduce memory management based on thread behaviors (MMBTB). Unfortunately, it lacks a unified optimization program interface and good architecture. Consequently, in this paper, we propose a memory resource management at operating system (OS) layer of mobile edge computing, called the thread-oriented memory management layer (TOMML) to address this problem, which follows the microkernel architecture pattern and can meet the user's requirements for selecting plug-ins to achieve different optimization goals. This paper is divided into several sections as follows. First, we demonstrate the efficiency of TOMML through theoretical simulation and experimentation. The experimental result is that TOMML can improve memory allocation efficiency by 12%-20%. Furthermore, we introduce a plug-in to save power, which can further promote 6%-25% bank free compared with previous excellent research.

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Section: Related Workmentioning

confidence: 99%

“…Therefore, in the previous study, we introduced MMBTB to enhance memory management efficiency depending on thread behavior. Unfortunately, due to the traditional architecture adopted by MMBTB [10], it lacks good scalability and sufficient overall agility. This makes MMBTB unable to adapt to the fast-changing Android devices.…”

Section: Introductionmentioning

confidence: 99%

A Thread-Oriented Memory Resource Management Framework for Mobile Edge Computing

Zhu

et al. 2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

A Thread Behavior-Based Memory Management Framework on Multi-core Smartphone

Cited by 1 publication

References 13 publications

A Thread-Oriented Memory Resource Management Framework for Mobile Edge Computing

A Thread-Oriented Memory Resource Management Framework for Mobile Edge Computing

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