arXiv:2607.02524v1 Announce Type: cross Abstract: Accurate cloud workload forecasting is pivotal for efficient resource management but remains challenging as workloads are highly volatile and prone to sudden bursts. Although wavelets preserve temporal locality, rigid fixed bases struggle with complex patterns and isolated processing neglects critical spatial dependencies. To address this, we propose SWIFT, a pure convolutional framework designed for high-efficiency workload forecasting. We introduce a Learnable Cascaded Wavelet Path that reformulates the traditional fixed wavelet bases into ad
The rapid expansion of cloud infrastructure and AI applications necessitates more sophisticated resource management to handle increasing workload volatility and bursts. This research directly addresses a critical and immediate operational need.
Improved workload forecasting directly translates to more efficient cloud resource utilization, lowering operational costs, enhancing service reliability, and enabling more effective scaling of AI and other data-intensive services.
The SWIFT framework offers a pure convolutional approach that accounts for both temporal locality and spatial dependencies, potentially evolving how cloud providers and large-scale AI operators manage their computational resources.
- · Cloud Providers
- · Hyperscalers
- · AI Infrastructure Companies
- · Data Center Operators
- · Inefficient Cloud Resource Management Strategies
- · Legacy Forecasting Tools
More accurate cloud workload forecasting leads to reduced operational costs and improved service level agreements (SLAs) for cloud users.
The cost efficiencies gained could enable broader access to cloud computing resources, accelerating AI development and deployment for smaller players.
As resource management becomes hyper-optimized, marginal gains in efficiency might become a key competitive differentiator in the cloud computing market, influencing further consolidation or specialization.
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Read at arXiv cs.AI