STARIXNet: Multivariate and Multi-attribute Deep Learning Approach to Real-Time Resource Allocation in Cloud Platforms
arXiv:2606.07565v1 Announce Type: new Abstract: Intelligent scaling of microservices in cloud platforms is crucial for mitigating escalating compute costs while avoiding service disruptions. Current solutions are limited to the univariate space, typically focusing on CPU usage alone to drive scaling decisions. Moreover, they address the problem as a purely forecasting task, focusing on prediction precision while neglecting the greater risks of underestimation and delays in system responsiveness. Alternative solutions are computationally complex, making them impractical for large-scale, real-ti
The increasing complexity and cost of cloud infrastructure, coupled with the computational demands of advanced AI, makes efficient resource allocation a critical problem, driving innovation in real-time solutions.
Efficient cloud resource allocation directly impacts operational costs, service reliability, and the ability to scale AI workloads, crucial for enterprises and AI developers alike.
Current reactive and univariate scaling approaches in cloud platforms will be superseded by more proactive, multivariate, and multi-attribute deep learning methods that dynamically optimize resource use.
- · Cloud platform providers
- · Enterprises with large cloud footprints
- · AI/ML developers
- · Deep learning infrastructure companies
- · Companies with inefficient cloud operations
- · Legacy cloud resource management solutions
- · Manual IT operations
Reduced cloud compute costs and improved service stability for organizations adopting advanced resource allocation strategies.
Increased adoption of microservices and complex AI workloads due to lower operational barriers and better cost predictability.
Further concentration of AI development and cloud usage on platforms offering the most sophisticated and cost-effective management capabilities, potentially creating new competitive moats.
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Read at arXiv cs.LG