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| Format: | Recurso digital |
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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.17926850 |
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| _version_ | 1866901600412368896 |
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| author | Ruksar Fatima Suhana Anjum Shaista Fatima Junaidi Ruqayya Rafa |
| author_facet | Ruksar Fatima Suhana Anjum Shaista Fatima Junaidi Ruqayya Rafa |
| contents | <p>The continuum of edge and cloud computing has emerged as a vital computing model for enabling latency-sensitive, data-heavy, and geographically scattered applications. As billions of devices generate massive volumes of data, efficient resource management across heterogeneous, distributed infrastructures has become essential. This study presents a systematic review of 68 research articles published between 2019 and 2024 that address resource distribution, task delegation, scheduling, orchestration, and optimization within the edge–cloud continuum. The paper highlights emerging themes such as AI-driven orchestration, multi-agent reinforcement learning, federated optimization, and serverless edge computing. We evaluate the performance, precision, scalability, and flexibility of traditional heuristics, mathematical models, and RL techniques under varying workloads. Although these significant advancements have been made, several open problems still exist—mobility-aware scheduling, cross-layer security integration with over-the-air encrypted computation results, and the absence of general ML models and benchmarks along with large-scale real-world deployment. The paper also ends by emphasizing the future research directions required to create and implement intelligent, autonomous, and scalable resource management frameworks that are designed for 6G/enhanced mobile broadband (eMBB), IoT/operating on devices over Bluetooth, autonomous systems/enabled by local cloudlets, and immersive applications/such as immersive gaming.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17926850 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Resource Management in the Edge–Cloud Continuum: Trends, Algorithms, and Open Challenges Ruksar Fatima Suhana Anjum Shaista Fatima Junaidi Ruqayya Rafa <p>The continuum of edge and cloud computing has emerged as a vital computing model for enabling latency-sensitive, data-heavy, and geographically scattered applications. As billions of devices generate massive volumes of data, efficient resource management across heterogeneous, distributed infrastructures has become essential. This study presents a systematic review of 68 research articles published between 2019 and 2024 that address resource distribution, task delegation, scheduling, orchestration, and optimization within the edge–cloud continuum. The paper highlights emerging themes such as AI-driven orchestration, multi-agent reinforcement learning, federated optimization, and serverless edge computing. We evaluate the performance, precision, scalability, and flexibility of traditional heuristics, mathematical models, and RL techniques under varying workloads. Although these significant advancements have been made, several open problems still exist—mobility-aware scheduling, cross-layer security integration with over-the-air encrypted computation results, and the absence of general ML models and benchmarks along with large-scale real-world deployment. The paper also ends by emphasizing the future research directions required to create and implement intelligent, autonomous, and scalable resource management frameworks that are designed for 6G/enhanced mobile broadband (eMBB), IoT/operating on devices over Bluetooth, autonomous systems/enabled by local cloudlets, and immersive applications/such as immersive gaming.</p> |
| title | Resource Management in the Edge–Cloud Continuum: Trends, Algorithms, and Open Challenges |
| url | https://doi.org/10.5281/zenodo.17926850 |