Edge–Cloud Continuums for Latency-Sensitive Tasks
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I4P121Keywords:
Edge Computing, Cloud Computing, Edge–Cloud Continuum, Fog Computing, Latency-Sensitive Applications, Low-Latency Systems, Real-Time Processing, Distributed Computing, Task Offloading, Resource Orchestration, Network Optimization, 5G and Beyond Networks, Internet of Things (IoT), Cyber-Physical Systems, AI at the Edge, Service Placement, Workload Scheduling, Quality of Service (QoS), Quality of Experience (QoE), Adaptive Resource ManagementAbstract
Latency-sensitive applications such as autonomous driving, augmented reality, and real-time industrial control increasingly exceed the performance limits of traditional cloud infrastructures. To address these constraints, recent research highlights the emergence of edge–cloud continuums that distribute computation across heterogeneous layers to reduce communication overhead, improve responsiveness, and enhance reliability [16, 19]. Edge computing brings processing closer to data sources, while cloud resources provide large-scale computation and centralized intelligence, forming a hybrid architecture capable of supporting strict end-to-end latency requirements [15, 1]. However, ensuring optimal task offloading, dynamic resource allocation, secure data transmission, and QoS guarantees across distributed nodes remains an open challenge. Existing studies demonstrate that effective orchestration requires jointly modeling network conditions, workload characteristics, and application-level latency budgets [6, 12]. This paper surveys architectural models, scheduling strategies, optimization frameworks, and emerging trends enabling robust edge–cloud integrations for latency-sensitive tasks. Furthermore, it identifies research gaps in real-time prediction, cross-layer optimization, and scalable multi-tenant orchestration that must be addressed to support the next generation of ultra-low-latency systems.
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