Adaptive Cloud-Edge Scheduler Using Lightweight AI Models for Real-Time IoT Streams

Internet of Things-Centric Optimized Service Provisioning in Multi-Cloud Environment

A lightweight, QoS-aware service placement algorithm that evaluates latency, bandwidth, and node load in real time is introduced that yields reduced latency and more consistent wait times relative to heuristic and genetic baselines.

Intelligent Portable Edge-Cloud Computing Ar-chitecture for Secure Data Analysis and Adaptive Resource Optimization Using AI-Driven Resource Scheduling

This paper introduced an Intelligent Portable Edge – Cloud Computing Architecture (IPECA) that combines the portable computing hardware, AI-based workload prediction, adaptive resource optimization, container-based virtualization and secure edge-cloud collaboration into a single computing architecture.

Graph Convolution Network – Long Short Term Memory based Reliable and Intelligent Fault Aware Fog Edge Computing Framework for Resource Constrained IoT Devices

The rapid growth of Internet of Things (IoT) applications has increased the demand for predictable, high-performance Edge–Fog–Cloud computing to support seamless data acquisition, transmission, and processing across billions of resource-constrained distributed devices. Edge–Fog-driven IoT architectures, positioned between IoT devices and centralized cloud infrastructure, improve reliability and efficiency by extending network services to the Internet edge and supporting the storage and processing of sensor data. Fog nodes deployed near end users and devices offer mobility support, low latency, bandwidth conservation, reduced energy consumption, and location awareness. Therefore, an efficient computing framework for scheduling fog nodes for edge users and IoT end devices is essential. Existing fog-node scheduling approaches based on metaheuristic techniques and advanced artificial intelligence address load balancing, computational offloading, and service orchestration; however, they often fail to proactively manage dynamic workload fluctuations, network anomalies, and unpredictable node failures. This paper proposes a multi-tier deep learning framework based on a Graph Convolutional Network (GCN) and Long Short-Term Memory (LSTM) model for reliable, fault-aware fog-edge computing. The architecture supports intelligent task delegation and predictive fault management for lightweight edge offloading on resource-constrained IoT devices. The IoT ecosystem, consisting of resource-constrained devices, fog nodes, and end users, is first represented as a graph. In this graph, IoT devices, edge gateways, and fog servers act as nodes, while their relationships, defined by parameters such as bandwidth and latency, act as edges. An adjacency matrix quantifies link weights, and a feature matrix captures each node’s resource characteristics, such as CPU utilization and battery status, at a given time. The proposed model is evaluated in a Python environment using the Google cluster dataset, a benchmark trace-log dataset. Experimental results show that the model effectively detects dynamic workload fluctuations, network anomalies, and unpredictable node failures. Performance analysis confirms that the proposed framework achieves 98.9% accuracy in detecting node failures and workload fluctuations.

Multi-application operator placement in cloud-edge infrastructure for big data stream processing

AI-Driven Cloud Analytics and Hardware-Assisted Edge Intelligence for Real-Time Cyber-Physical Infrastructure Management

Real-time monitoring and smart decision-making are required in cyber-physical infrastructures such as smart grids, transportation systems, and industrial automation systems to ensure process efficiency and system resilience. However, higher latency, bandwidth constraints, and a lack of responsiveness to time-sensitive data streams haunt traditional cloud-based architectures. To address these challenges, a coherent framework for integrating AI-based cloud analytics with edge intelligent hardware for managing cyber-physical infrastructure is proposed in the following paper. The architecture is based on distributed edge nodes, with hardware accelerators for very low-latency inference, and cloud layers that perform large-scale analytics and optimisation of global models. A hybrid resource allocation strategy is a self-regulating plan for the allocation of computing workloads across cloud and edge environments. Also, an adaptive learning mechanism improves prediction accuracy in changing operational environments. The framework is mathematically designed to achieve optimal latency, throughput, and computational efficiency. The proposed system reduces latency by 145ms to 92 (≈36.5) units and increases prediction accuracy from 81.2% to 96.4% when applied to a dynamic workload. The convergence analysis shows that standardized quicker convergence occurs after 35 iterations as opposed to 60 iterations in the models at the baseline. Additionally, the throughput is proceeding at 520 requests to 780 requests and error rates are falling by a factor of around 41, ensuring better reliability. Relative performance analysis across various scenarios indicates consistent improvement in both edge-dominant and cloud-dominant setups. The results of this study demonstrate that, when combined with hardware-based edge intelligence, AI-powered cloud analytics can significantly enhance the responsiveness, scalability, and decision accuracy in cyber-physical infrastructure systems.

Energy-Efficient Distributed Machine Learning in Edge Computing Architectures

This study investigates energy-efficient distributed machine learning techniques, including federated learning, model compression, adaptive resource management, dynamic task offloading, and communication-efficient optimization, and proposes a distributed learning framework that integrates local model training, adaptive communication scheduling, gradient compression, and workload balancing to minimize energy consumption while maintaining learning accuracy.