Towards efficient program execution on edge-cloud computing platforms

• Published in: Journal of parallel and distributed computing Vol. 205; p. 105135
• Main Authors: Dollinger, Jean-François, Vauchey, Vincent
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.11.2025
• Subjects: Distributed computing; Edge-cloud computing; High-performance computing; Performance analysis; Program optimization; Task scheduling; Edge-cloud computing; High-performance computing; Program optimization; 0000; 1111; Task scheduling; Performance analysis; Distributed computing
• ISSN: 0743-7315
• DOI: 10.1016/j.jpdc.2025.105135

This paper investigates techniques dedicated to the performance of edge-cloud infrastructures and identifies the challenges to address to maximize their efficiency. Unlike traditional cloud-only processing, edge-cloud platforms meet the stringent requirements of real-time applications via additional computing resources close to the data source. Yet, due to numerous performance factors, it is a complex task to perform efficient computations on such platforms. Thus, we identify the main performance bottlenecks induced by traditional approaches and extensively discuss the performance characteristics of edge computing platforms. Based on these insights, we design an automated framework capable of achieving end-to-end efficacy of edge-cloud applications. We argue that achieving performance on edge-cloud infrastructures requires adaptive offloading of programs based on computational requirements. Thus, we comprehensively study three performance-critical aspects forming the performance workflow of applications: i) performance modelling, ii) program optimization iii) task scheduling. First, we explore performance modelling techniques, forming the foundation of most cost models, to accurately predict and achieve robust code optimization and scheduling. We then cover the whole program optimization chain, from hotspot detection to code optimization, focusing on memory locality, code parallelization, and acceleration. Finally, we discuss task scheduling techniques for selecting the best computing resource and ensuring a balanced workload distribution. Overall, our study provides insights by covering the above performance workflow referencing prominent state-of-the-art works, particularly focusing on those not yet applied in the context of edge-cloud computing. Additionally, we conducted experiments to further validate our findings. Finally, for each topic of interest, we identify the addressed scientific obstacles and outline the open research challenges yet to be overcome. •Edge-cloud performance relies on accurate modelling, adaptive program optimization, and dynamic scheduling.•Efficient use of edge-cloud platforms requires appropriate data routing, parallelization, and accelerator utilization.•Accurate performance modelling must extensively characterize execution environment, processing, and networking dynamics.•Fine-grain program offloading and adaptive code optimization are critical for best edge-cloud hardware exploitation.•Edge-cloud applications require multi-level, collaborative, and resilient task scheduling to meet performance demands.