A memory footprint optimization framework for Python applications targeting edge devices

The advantages of processing data at the source motivate developers to offload computations at the edges of IoT networks. However, the computational resource constraints of edge computing devices limit the opportunities for deploying applications developed in high-level languages at the edge. In con...

Full description

Saved in:
Bibliographic Details
Published in:Journal of systems architecture Vol. 142; p. 102936
Main Authors: Katsaragakis, Manolis, Papadopoulos, Lazaros, Konijnenburg, Mario, Catthoor, Francky, Soudris, Dimitrios
Format: Journal Article
Language:English
Published: Elsevier B.V 01.09.2023
Subjects:
Edge computing
Memory management
Python
Resource management
Resource management
Memory management
Edge computing
Python
ISSN:1383-7621, 1873-6165
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The advantages of processing data at the source motivate developers to offload computations at the edges of IoT networks. However, the computational resource constraints of edge computing devices limit the opportunities for deploying applications developed in high-level languages at the edge. In contrast to C/C++, applications developed in Python and other dynamic high-level languages, often require an increased amount of memory size, due to their inherent static memory management approach. Therefore, developers targeting the deployment of applications in dynamic high-level languages at the edge need support by methodologies and tools, which will allow these applications to exploit the limited memory resources efficiently. This work presents a memory optimization framework for applications developed in the Python programming language targeting edge devices. Aiming to avoid the inherent pitfalls of static memory management that Python’s integrated memory manager imposes, the framework targets the reduction of the required memory footprint, by integrating a set of static and dynamic optimizations. The evaluation results, based on a set of representative real-life benchmark suites and applications, show 64% average memory footprint reduction, over the CPython’s minimum baseline of 24 MB. Additionally, we investigate the impact of memory size reduction on execution time and energy consumption. The results show 51% lower execution time and 47.3% reduction in the energy consumed.
ISSN:1383-7621
1873-6165
DOI:10.1016/j.sysarc.2023.102936