Elasticity unleashed: Fine-grained cloud scaling through distributed three-way decision fusion with multi-head attention

Abstract

With the proliferation of cloud computing, elastic resource scaling has emerged as a critical challenge. Cloud platforms must efficiently allocate resources to match dynamic workloads. This study addresses the key trade-off between responsiveness and prudence in scaling a novel distributed three-way decision fusion approach. Distributed three-way decisions of immediate, delayed, or no scaling are made at coarse and fine-time granularity levels. Subsequently, a multi-head attention mechanism intelligently fuses these decisions, weighing the relevance of the different granularities to synthesize an integrated scaling strategy. The reactiveness of sudden workload changes are balanced during fusion by considering long-term trends. Comprehensive experiments on real-world data demonstrated that the proposed fusion strategy substantially improves resource efficiency and adherence to service-level agreements compared to existing methods. Multi-head attention imparts autonomy in adapting to diverse operating conditions. The proposed principled methodology and attention-based decision aggregation hold significance for efficient, adaptive, and interpretable cloud scaling mechanisms.