AERO: Adaptive Edge-Cloud Orchestration With a Sub-1K-Parameter Forecasting Model
Effective resource management in edge-cloud networks is crucial for meeting Quality of Service (QoS) requirements while minimizing operational costs. However, dynamic and fluctuating workloads pose significant challenges for accurate workload prediction and efficient resource allocation, particularl...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Transactions on Machine Learning in Communications and Networking |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10935743/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849337848479612928 |
|---|---|
| author | Berend J. D. Gort Godfrey M. Kibalya Angelos Antonopoulos |
| author_facet | Berend J. D. Gort Godfrey M. Kibalya Angelos Antonopoulos |
| author_sort | Berend J. D. Gort |
| collection | DOAJ |
| description | Effective resource management in edge-cloud networks is crucial for meeting Quality of Service (QoS) requirements while minimizing operational costs. However, dynamic and fluctuating workloads pose significant challenges for accurate workload prediction and efficient resource allocation, particularly in resource-constrained edge environments. In this paper, we introduce AERO (Adaptive Edge-cloud Resource Orchestration), a novel lightweight forecasting model designed to address these challenges. AERO features an adaptive period detection mechanism that dynamically identifies dominant periodicities in multivariate workload data, allowing it to adjust to varying patterns and abrupt changes. With fewer than 1,000 parameters, AERO is highly suitable for deployment on edge devices with limited computational capacity. We formalize our approach through a comprehensive system model and extend an existing simulation framework with predictor modules to evaluate AERO’s performance in realistic cloud-edge environments. Our extensive evaluations on real-world cloud workload datasets demonstrate that AERO achieves comparable prediction accuracy to complex state-of-the-art models with millions of parameters, while significantly reducing model size and computational overhead. In addition, simulations show that AERO improves orchestration performance, reducing energy consumption and response times compared to existing proactive and reactive approaches. Our live deployment experiments further validate these findings, demonstrating that AERO consistently delivers superior performance. These results highlight AERO as an effective solution for improving resource management and reducing operational costs in dynamic cloud-edge environments. |
| format | Article |
| id | doaj-art-cd33a92d5a4e481899c1c4ea57a917a9 |
| institution | Kabale University |
| issn | 2831-316X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Machine Learning in Communications and Networking |
| spelling | doaj-art-cd33a92d5a4e481899c1c4ea57a917a92025-08-20T03:44:33ZengIEEEIEEE Transactions on Machine Learning in Communications and Networking2831-316X2025-01-01346347810.1109/TMLCN.2025.355310010935743AERO: Adaptive Edge-Cloud Orchestration With a Sub-1K-Parameter Forecasting ModelBerend J. D. Gort0https://orcid.org/0009-0007-4649-3890Godfrey M. Kibalya1https://orcid.org/0000-0002-7053-3756Angelos Antonopoulos2https://orcid.org/0000-0002-3546-1080Nearby Computing, Barcelona, SpainNearby Computing, Barcelona, SpainNearby Computing, Barcelona, SpainEffective resource management in edge-cloud networks is crucial for meeting Quality of Service (QoS) requirements while minimizing operational costs. However, dynamic and fluctuating workloads pose significant challenges for accurate workload prediction and efficient resource allocation, particularly in resource-constrained edge environments. In this paper, we introduce AERO (Adaptive Edge-cloud Resource Orchestration), a novel lightweight forecasting model designed to address these challenges. AERO features an adaptive period detection mechanism that dynamically identifies dominant periodicities in multivariate workload data, allowing it to adjust to varying patterns and abrupt changes. With fewer than 1,000 parameters, AERO is highly suitable for deployment on edge devices with limited computational capacity. We formalize our approach through a comprehensive system model and extend an existing simulation framework with predictor modules to evaluate AERO’s performance in realistic cloud-edge environments. Our extensive evaluations on real-world cloud workload datasets demonstrate that AERO achieves comparable prediction accuracy to complex state-of-the-art models with millions of parameters, while significantly reducing model size and computational overhead. In addition, simulations show that AERO improves orchestration performance, reducing energy consumption and response times compared to existing proactive and reactive approaches. Our live deployment experiments further validate these findings, demonstrating that AERO consistently delivers superior performance. These results highlight AERO as an effective solution for improving resource management and reducing operational costs in dynamic cloud-edge environments.https://ieeexplore.ieee.org/document/10935743/Edge computingcloud-edge orchestrationadaptive resource managementlightweight forecasting modelsworkload predictiondynamic periodicity detection |
| spellingShingle | Berend J. D. Gort Godfrey M. Kibalya Angelos Antonopoulos AERO: Adaptive Edge-Cloud Orchestration With a Sub-1K-Parameter Forecasting Model IEEE Transactions on Machine Learning in Communications and Networking Edge computing cloud-edge orchestration adaptive resource management lightweight forecasting models workload prediction dynamic periodicity detection |
| title | AERO: Adaptive Edge-Cloud Orchestration With a Sub-1K-Parameter Forecasting Model |
| title_full | AERO: Adaptive Edge-Cloud Orchestration With a Sub-1K-Parameter Forecasting Model |
| title_fullStr | AERO: Adaptive Edge-Cloud Orchestration With a Sub-1K-Parameter Forecasting Model |
| title_full_unstemmed | AERO: Adaptive Edge-Cloud Orchestration With a Sub-1K-Parameter Forecasting Model |
| title_short | AERO: Adaptive Edge-Cloud Orchestration With a Sub-1K-Parameter Forecasting Model |
| title_sort | aero adaptive edge cloud orchestration with a sub 1k parameter forecasting model |
| topic | Edge computing cloud-edge orchestration adaptive resource management lightweight forecasting models workload prediction dynamic periodicity detection |
| url | https://ieeexplore.ieee.org/document/10935743/ |
| work_keys_str_mv | AT berendjdgort aeroadaptiveedgecloudorchestrationwithasub1kparameterforecastingmodel AT godfreymkibalya aeroadaptiveedgecloudorchestrationwithasub1kparameterforecastingmodel AT angelosantonopoulos aeroadaptiveedgecloudorchestrationwithasub1kparameterforecastingmodel |