journal article·2026-01-31Open access

An Overview of Technical Aspects and Challenges in Designing Edge-Cloud Systems

Mohammadsadeq Garshasbi Herabad, Javid Taheri, Bestoun S. Ahmed, Calin Curescu

Applied Sciences· Volume 16· Issue 3

Abstract

Edge–cloud computing has emerged as a key enabling paradigm for augmented and virtual reality (AR/VR) systems because of the stringent computational and ultra-low-latency requirements of AR/VR workloads. Designing efficient edge–cloud systems for such workloads involves multiple technical aspects, including communication technologies, service placement, task offloading and caching, service migration, and security and privacy. This paper provides a structured and technical analysis of these aspects from an AR/VR perspective. We adopt a two-stage literature analysis, in which Google Scholar is used to identify fundamental technical aspects and solution approaches, followed by a focused analysis of recent research trends and future directions using academic databases (e.g., IEEE Xplore, ACM Digital Library, and ScienceDirect). We present an organized classification of the core technical aspects and investigate existing solution approaches, including heuristic, metaheuristic, learning-based, and hybrid strategies. Rather than introducing application-specific designs, the analysis focuses on workload-driven challenges and trade-offs that arise in AR/VR systems. Based on this classification, we analyze recent research trends, identify underexplored technical areas, and highlight key research gaps that hinder the efficient deployment of AR/VR services over edge–cloud infrastructures. The findings of this study provide practical insights for researchers and system designers and help guide future research toward more responsive, scalable, and reliable edge–cloud AR/VR systems.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: IoT and Edge/Fog Computing · Software-Defined Networks and 5G · Robotics and Automated Systems

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Mathematics and Computer Science, Karlstad University, 651 88 Karlstad, Sweden

School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Belfast BT7 1NN, UK

Ericsson AB, 164 83 Stockholm, Sweden

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