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.

The integration of edge and cloud computing is critical for resource-intensive applications which require low-latency communication, high reliability, and efficient resource utilisation. The service placement problem in these environments poses significant challenges owing to dynamic network conditions, heterogeneous resource availability, and the necessity for real-time decision-making. Because determining an optimal service placement in such networks is an NP-complete problem, the existing solutions rely on fast but suboptimal heuristics or computationally intensive metaheuristics. Neither approach meets the real-time demands of online scenarios, owing to its inefficiency or high computational overhead. In this study, we propose a lightweight learning-based approach for the online placement of services with multi-version components in edge-to-cloud computing. The proposed approach utilises a Shallow Neural Network (SNN) with both weight and power coefficients optimised using a Genetic Algorithm (GA). The use of an SNN ensures low computational overhead during the training phase and almost instant inference when deployed, making it well suited for real-time and online service placement in edge-to-cloud environments where rapid decision-making is crucial. The proposed method (SNN-GA) is specifically evaluated in AR/VR-based remote repair and maintenance scenarios, developed in collaboration with our industrial partner, and demonstrated robust performance and scalability across a wide range of problem sizes. The experimental results show that SNN-GA reduces the service response time by up to 27% compared to metaheuristics and 55% compared to heuristics at larger scales. It also achieves over 95% platform reliability, outperforming heuristics (which remain below 85%) and metaheuristics (which decrease to 90% at larger scales).

The evolution of edge-to-cloud networks has significantly increased the complexity of determining optimal service placement across these infrastructures, a challenge identified as an NP-complete problem. To address such problems, exact algorithms are impractical at larger scales owing to their computational demands. Heuristics exhibit faster runtimes but lower solution quality, whereas metaheuristics provide high-quality solutions at the cost of increased runtime. In this study, service placement in edge-to-cloud systems is investigated and formulated as an optimisation problem, where each service component is provided by different vendors and is available in multiple versions. The inclusion of multi-version components adds an additional layer of complexity, making the placement problem even more challenging. Specifically, this study addresses the service placement problem in Augmented Reality (AR)- and Virtual Reality (VR)-based remote repair and maintenance use cases, where service response time and system reliability are critical performance metrics. To optimise both metrics, we propose a novel hybrid metaheuristic algorithm (E-PSOGA) which combines the fast convergence of Particle Swarm Optimisation (PSO) with the global search capabilities of Genetic Algorithms (GA). A custom healing operator is also introduced to further enhance the solution quality and reduce the algorithm runtime. A comprehensive performance assessment shows that E-PSOGA reduces the response time by 37% compared with the other implemented baseline algorithms. E-PSOGA achieved 98% platform and 97% service reliability while maintaining a reasonable algorithm runtime. These results indicate that the proposed approach is well-suited for large-scale and time-sensitive scenarios requiring both computational efficiency and high solution quality. 

Augmented Reality (AR) and Virtual Reality (VR) systems involve computationally intensive image processing algorithms that can burden end-devices with limited resources, leading to poor performance in providing low latency services. Edge-to-cloud computing overcomes the limitations of end-devices by offloading their computations to nearby edge devices or remote cloud servers. Although this proves to be sufficient for many applications, optimal placement of latency sensitive AR/VR services in edge-to-cloud infrastructures (to provide desirable service response times and reliability) remain a formidable challenging. To address this challenge, this paper develops a Multi-Objective Genetic Algorithm (MOGA) to optimize the placement of AR/VR-based services in multi-tier edge-to-cloud environments. The primary objective of the proposed MOGA is to minimize the response time of all running services, while maximizing the reliability of the underlying system from both software and hardware per spectives. To evaluate its performance, we mathematically modeled all components and developed a tailor-made simulator to assess its effectiveness on various scales. MOGA was compared with several heuristics to prove that intuitive solutions, which are usually assumed sufficient, are not efficient enough for the stated problem. The experimental results indicated that MOGA can significantly reduce the response time of deployed services by an average of 67% on different scales, compared to other heuristic methods. MOGA also ensures reliability of the 97% infrastructure (hardware) and 95% services (software).