用矩阵补全方法精准预测边缘计算任务运行时间，还能量化干扰影响。
Interference-Aware Edge Runtime Prediction with Conformal Matrix Completion
- 基于矩阵分解思想，融合干扰信息建模边缘任务运行时。
- 在24台设备上测试，误差仅5.2%，比现有方法提升2倍。
- 适合资源受限、多租户的边缘计算环境，可提供可信置信区间。
准确估计工作负载运行时间是计算机系统长期追求的目标，在高效资源分配、延迟最小化等系统管理任务中至关重要。在日益复杂的分布式系统中，为降低延迟，越来越多处理被推向边缘，这使得运行时预测尤为关键。现有边缘系统运行时预测方法普遍存在数据效率低或需大量仪器化的问题，而在异构边缘计算环境中，历史运行时数据往往稀疏，仪器化又难以实现。此外，边缘资源有限导致多租户共存，任务间可能产生干扰，进一步增加预测难度。本文结合机器学习与系统设计思路，提出一种受矩阵分解启发的方法，可在不牺牲精度的前提下生成考虑干扰的预测，并提供可证明的紧致不确定性边界。我们在24台不同设备上收集的新型WebAssembly运行时数据集上验证该方法，预测误差仅为5.2%，较直接应用现有方法提升2倍。
原文摘要 · Abstract (English)
Accurately estimating workload runtime is a longstanding goal in computer systems, and plays a key role in efficient resource provisioning, latency minimization, and various other system management tasks. Runtime prediction is particularly important for managing increasingly complex distributed systems in which more sophisticated processing is pushed to the edge in search of better latency. Previous approaches for runtime prediction in edge systems suffer from poor data efficiency or require intensive instrumentation; these challenges are compounded in heterogeneous edge computing environments, where historical runtime data may be sparsely available and instrumentation is often challenging. Moreover, edge computing environments often feature multi-tenancy due to limited resources at the network edge, potentially leading to interference between workloads and further complicating the runtime prediction problem. Drawing from insights across machine learning and computer systems, we design a matrix factorization-inspired method that generates accurate interference-aware predictions with tight provably-guaranteed uncertainty bounds. We validate our method on a novel WebAssembly runtime dataset collected from 24 unique devices, achieving a prediction error of 5.2% -- 2x better than a naive application of existing methods.
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