WIET: Harmonizing Group-aware Model Weighting and Worker Allocation for Ensemble Temporal Prediction MaaS

Abstract

Ensemble Temporal Prediction Model-as-a-Service (ETP-MaaS) has become crucial in fields like financial modeling and cloud monitoring. Existing solutions fail to co-optimally address a two-fold challenge of dynamic collaboration and heterogeneity, treating models as independent entities and employing simplistic worker allocation rules. However, at the model level, data volatility means that optimal performance requires identifying and weighting constantly shifting subgroups of base models, not just individual ones; at the system level, these model groups must be efficiently mapped to a pool of heterogeneous and dynamically available workers. To this end, we introduce WIET, an efficient ETP-MaaS system that co-optimizes model weighting and worker allocation. For adaptive weighting, WIET identifies evolving group behaviors among base models and propose a novel group temporal locality-enhanced weighting method. Additionally, WIET develops an efficient, multi-dimensional worker allocation method powered by hybrid heuristic optimization, effectively reducing bottlenecks and resource waste. Experiments show WIET consistently outperforms state-of-the-art methods in terms of accuracy, latency, and resource usage across various workloads and tasks.