Aggregated time series are widely used in business and economics, where top-level sequences (e.g., category sales) aggregated from underlying sequences (e.g., individual items) often exhibit clearer trends and are therefore typically the primary focus of forecasting tasks. However, treating top-level sequences as ordinary multivariate time series is inappropriate in the presence of coupled aggregation constraints. The core challenge arises in coupled aggregation structures, where a single underlying sequence contributes to multiple top-level sequences, as simple nonnegativity constraints of underlying sequences induce highly complex constraints among top-level sequences. Existing methods fail to achieve high accuracy while satisfying these constraints. To address this, we propose ProCAST, a projection-based framework that adjusts forecasts from any multivariate base model to satisfy coupled aggregation constraints. By introducing virtual underlying sequences and leveraging orthogonal and oblique projection, our method ensures that the top-level forecasts are feasible without explicitly deriving complex constraints. Theoretically, we prove that the proposed method guarantees improved accuracy under distance-based loss functions. Experiments on real-world datasets show that our method completely eliminates constraint violations while achieving higher accuracy than current state-of-the-art approaches.

The development of large language models (LLMs) has catalyzed unprecedented demand on the computing network, specifically for large-scale, few-hops, and low-latency, which directly underpin LLM task efficiency. However, mainstream topologies such as Clos suffer from costs and latency, while topologies with good scalability have symmetric or collective communication issues. In order to achieve a favorable balance among design metrics, we propose a novel topology named the Balanced Sparse Tree (BST), which is a topology characterized by symmetric design and sparse connections, motivated by hypergraph theory and Steiner Systems. Its degree-diameter upper-bound approaches the Moore Bound for Bipartite Biregular graphs, larger than other known dia-meter-2 topologies. Furthermore, we incorporate differentiated routing, deadlock freedom, and topology-affined deployment into BST. Testbed experiments, simulations, together with modeling analysis, demonstrate the superiority of BST over the state-of-the-art in network scale, latency, bandwidth, and cost. With equivalent scales, BST outperforms Clos with a 50% cost reduction while maintaining comparable performance for AI workloads. Furthermore, BST delivers a 3.9%–11.8% gain in collective communications and has 13.4% improvement over state-of-the-art topologies.

Multi-tenancy in modern cloud service colocates multiple virtual machines (VMs) into physical machines (PMs) to improve resource efficiency. However, co-location introduces interference among VMs, potentially degrading the quality-ofservice (QoS) for users. Previous methods predict QoS degradation and schedule VMs accordingly, but they often overlook important information provided by VM metrics and are hard to integrate with real-world cloud schedulers. Considering the above factors, we present TRACE, a novel QoS-aware, lightweight, and decoupled recommender for VM scheduling. Firstly, TRACE employs a dual-tower feature extraction mechanism that independently extracts metrics from VMs and PMs, thereby reducing the time complexity of the model. Secondly, the dual-tower is enhanced by Deep & Cross Networks to explicitly model cross-feature interactions, and we further incorporate a Set Transformer to process overlooked multi-VM metrics from the PM. Thirdly, TRACE designs a trainable similarity gate and an adaptive mask to filter suboptimal migrations, decoupling it from the scheduler for easy integration. Experimental results on data collected from real-world clusters show that TRACE outperforms state-of-the-art methods in QoS prediction accuracy and ranking quality, achieving at least 6.3 % QoS improvements.