Huidong Ma, Xinyan Shi, Sun Hui, Xiaofei Yue, Xiaoguang Liu, Gang Wang, Wentong Cai. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Serverless computing promises fine-grained resource elasticity and billing, making it an attractive way to build complex applications as multi-stage workflows. Nonetheless, existing workflow orchestration ignores the heterogeneous demands of the computation and communication parts within a stage, potentially resulting in resource inefficiency on either side. In this paper, we advocate forcomputation-communication-separated orchestrationto unleash hybrid resource (i.e., compute and network) elasticity. We present HyFaaS, a serverless workflow orchestrator that improves performance while ensuring cost efficiency. It seamlessly decouples computation and communication as a series of hybrid stages re-expressed within HyDAG, a novel workflow abstraction. HyFaaS uses a gray-box profiling model to identify their Pareto-optimal saturated configurations, and then deploys the saturated workflow to juggle communication and scaling overheads through two-level HyDAG partitioning. Along with event-driven runtime fine-tuning, HyFaaS further scales down the non-critical stages to reduce cost via branch-aware coordination. Experimental results show that HyFaaS surpasses existing solutions by 32.7%–50.4% on end-to-end latency, while lowering cost by up to 1.37×.

With the flourishing of the Agricultural Internet of Things (AIoT), analyzing large-volume sensor data has become a regular requirement for agricultural decision-making. Federated learning (FL), which facilitates scattered AIoT devices to train models collaboratively, has gained significant attention. However, traditional FL poses challenges in AIoT scenarios, such as wide geo-distribution, heterogeneous data distribution, and high-device risks. Existing works tend to be one-sided and remain unclear on how to tackle these issues thoroughly in AIoT. To fill the gap, we present Bc2FL, a double-layer blockchain-based FL framework, which enhances both learning efficiency and security for AIoT. The double-layer blockchain, coupled with a two-stage consensus algorithm, drives the hierarchical FL process to enable efficient and reliable agricultural knowledge-sharing. In addition, Bc2FL adopts an adaptive model aggregation algorithm to dynamically tune noise levels based on the model quality, further improving the learning security and model credibility. Finally, the extensive experimental results demonstrate that Bc2FL not only improves the model accuracy by up to 21.17% compared with the state-of-the-art baselines, but also enhances the privacy protection within an additional error of only 2.1%.

With the flourishing of global services, low-latency analytics on large-volume geo-distributed data has been a regular requirement for application decision-making. Serverless computing, with its rapid function start-up and lightweight deployment, provides a compelling way for geo-distributed analytics. However, existing research focuses on elastic resource scaling at the stage granularity, struggling to heterogeneous resource demands across component functions in wide-area settings. The neglect potentially results in the cost inefficiency and Service Level Objective (SLO) violations. In this paper, we advocate for fine-grained function orchestration to exploit wide-area resource elasticity. We thereby present Demeter, a fine-grained function orchestrator that saves job execution costs for geo-distributed serverless analytics while ensuring SLO compliance. By learning from volatile and bursty environments, Demeter jointly makes per-function placement and resource allocation decisions using a well-optimized multi-agent reinforcement learning algorithm with a pruning mechanism. It prevent the irreparable performance loss by function congestion control. Ultimately, we implement Demeter and evaluate it with the realistic workloads. Experimental results reveal that Demeter outperforms the baselines by up to 46.6% on cost, while reducing SLO violation by over 23.7% and bringing it to below 15%.

In the era of global services, low-latency analytics on large-volume geo-distributed data has been a regular demand for application decision-making. Serverless computing facilitates fast function start-up and deployment, making it an attractive way for geo-distributed analytics. We argue that the serverless paradigm holds the potential to breach current performance bottlenecks via fine-grained function orchestration. However, how to configure it for geo-distributed analytics remains ambiguous. To fill this gap, we present Demeter, a scalable fine-grained function orchestrator for geo-distributed serverless analytics systems. Demeter aims to minimize the composite cost of co-existing jobs while meeting the user-specific Service Level Objectives (SLO). To handle the volatile environments and learn the diverse function demands, a Multi-Agent Reinforcement Learning (MARL) solution is used to co-optimize the per-function placement and resource allocation. The MARL extracts holistic and compact states via hierarchical graph neural networks, and then designs a novel actor network to shrink the huge decision space and model complexity. Finally, we implement Demeter and evaluate it using realistic workloads. The experimental results reveal that Demeter significantly saves costs by 23.3%∼32.7%, while reducing SLO violations by over 27.4%, surpassing state-of-the-art solutions.