Journal of Grid Computing

Papers
(The H4-Index of Journal of Grid Computing is 18. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2021-11-01 to 2025-11-01.)
ArticleCitations
A Load-Aware Energy-Efficient Clustering Algorithm in Sensor-Cloud59
An Effective Prediction of Resource Using Machine Learning in Edge Environments for the Smart Healthcare Industry55
Software License Consolidation and Resource Optimization in Container-based Virtualized Data Centers49
ICSMPC: Design of an Iterative-Learning Contextual Side Chaining Model for Improving Security of Priority-Aware Cloud Resources43
Healthcare and Fitness Services: A Comprehensive Assessment of Blockchain, IoT, and Edge Computing in Smart Cities38
Placement Combination between Heterogeneous Services and Heterogeneous Capacitated Servers in Edge Computing36
Edge Computing Empowered Smart Healthcare: Monitoring and Diagnosis with Deep Learning Methods34
Exploring the Synergy of Blockchain, IoT, and Edge Computing in Smart Traffic Management across Urban Landscapes31
Adversarial Attacks on Visual Objects Using the Fast Gradient Sign Method29
Optimizing Accounting Informatization through Simultaneous Multi-Tasking across Edge and Cloud Devices using Hybrid Machine Learning Models24
Energy-Constrained DAG Scheduling on Edge and Cloud Servers with Overlapped Communication and Computation23
Energy-Efficient and Load-Aware VM Placement in Cloud Data Centers23
A Market-based Framework for Resource Management in Cloud Federation23
Blockchain Assisted Cloud Security and Privacy Preservation using Hybridized Encryption and Deep Learning Mechanism in IoT-Healthcare Application22
Mayfly Taylor Optimization-Based Graph Attention Network for Task Scheduling in Edge Computing22
A Review of Machine Learning Network in Human Motion Biomechanics20
A Federated Framework for Edge Computing Devices with Collaborative Fairness and Adversarial Robustness18
Adaptive Resource Scheduling in Multi-Cloud Computing Using Recurrent Neural Forecasting and Memory-Based Metaheuristic Optimization18
Enhancing Machine Learning-Based Autoscaling for Cloud Resource Orchestration18
Signature-based Adaptive Cloud Resource Usage Prediction Using Machine Learning and Anomaly Detection18
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