Papers for
edge computing platform developers
Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.
Learning based scheduling improves workflow efficiency on edge cloud devices
Learning based Scalable Multi-Objective Scheduling of Workflows across UD-IaaS-FaaS Systems
Abstract: Workflow scheduling in user device-centric edge-cloud computing has emerged as a challenging problem due to the need to jointly optimize execution performance, energy consumption, and monetary cost. Modern workflows consist of heterogeneous tasks, where compute-intensive components are well-suited for Infrastructure-as-a-Service (IaaS) resources, while fine-grained, event-driven tasks are more efficiently executed using the Function-as-a-Service (FaaS) model. We formulate this scheduling problem for application workflows over a hybrid User Device (UD)-IaaS-FaaS computing continuum as a multi-objective Mixed-Integer Linear Program (MILP). The formulation models the workflow execution mechanism by incorporating cumulative capacity constraints for IaaS resources and elastic resource provisioning for FaaS. To overcome the intractability of MILP, we propose a learning-based framework that combines graph neural networks, surrogate performance modeling, and a multi-sample REINFORCE approach. The proposed approach is workflow-aware, leveraging task pairing to improve data locality and structured task placement decisions. Agent training is organized into three stages: small-scale learning of UD-IaaS trade-offs, intermediate locality aware co-location, and large-scale handling of parallelism and aggregation bottlenecks in workflows. The proposed framework is validated through real-world experiments using the Pegasus Montage workflow on a custom UD-IaaS-FaaS testbed comprising a user device, multiple IaaS nodes, and serverless FaaS instances. Extensive experimental results demonstrate the improved trade-offs across makespan, energy consumption, and cost. Compared with the genetic algorithm and greedy scheduling approaches, the proposed approach reduces makespan by 10-17.5%, energy consumption by 3-9%, and cost by 4-10%, while remaining within 3.5-5.6% of the MILP optimum.
Xronos speeds up large language model fine-tuning on diverse edge CPUs
Xronos: Heterogeneity-Aware Tensor Parallelism for Collaborative LLM Fine-Tuning on Edge CPUs
Abstract: Collaborative fine-tuning on edge devices adapts large language models to domain-specific data while keeping each device's data local. State-of-the-art (SOTA) collaborative fine-tuning techniques are largely designed for GPU-based edge devices and rely on pipeline parallelism (PP). However, many edge platforms, including IoT gateways, smart-home hubs, and in-vehicle computers, are primarily CPU-based. This paper reports that PP is ineffective on CPU-based edge devices because the same CPU handles both model computation and communication, which causes severe CPU contention. Our analysis shows that this leads to 5.75$\times$ higher computation stall ratios than on GPU devices on average. Tensor parallelism (TP) can alleviate this contention by separating computation and communication, but existing TP techniques assume homogeneous devices. On heterogeneous CPU edge devices, we find that this assumption causes faster workers to remain idle for up to 34% while waiting for slower devices at synchronization points. To address the limitations, we propose Xronos, a collaborative fine-tuning framework for heterogeneous CPU edge devices. Xronos uses TP as its execution backbone and combines lightweight profiling with heterogeneity-aware tensor partitioning to reduce the straggler bottleneck. Across diverse devices, models, and benchmark tasks, Xronos reduces fine-tuning time by 18% (TP) to 56% (PP) and the ratio of device idle time by $\sim$5.9$\times$ over SOTA techniques, while maintaining the accuracy.