Log-based AIOps is a widely researched topic aiming at reducing the developer burden in system maintenance. Since industrial developers prefer lightweight supervised solutions for log-based AIOps, the strong dependence of these solutions on labeled data creates significant challenges for teams new to building log-based AIOps capabilities, such as high labeling costs, inconsistent annotations, and manual management issues. Log-labeling faces challenges in integrating existing artifacts to reduce labeling costs and manage labels effectively. To the best of our knowledge, no prior research addresses of assisting log-labeling problem. In this article, we propose a new approach called LogLabeler to assist developers in annotating and managing log labels. LogLabeler leverages existing artifacts for initial-label-acquisition, minimizes labeling costs by automatically generating all log labels, and shields developers from manual label management through a human-in-the-loop refinement approach. Evaluations on real-world datasets from Alibaba and open-source datasets show that LogLabeler can effectively supplement log labels, achieving comparable accuracy to existing baselines while operating more efficiently. Furthermore, we demonstrate LogLabeler's practical effectiveness at Alibaba through a case study, highlighting its benefits to developers.

To reduce uploading bandwidth and address privacy concerns, deep learning at the network edge has been an emerging topic. Typically, edge devices collaboratively train a shared model using real-time generated data through the Parameter Server framework. Although all the edge devices can share the computing workloads, the distributed training processes over edge networks are still time-consuming due to the parameters and gradients transmission procedures between parameter servers and edge devices. Focusing on accelerating distributed Convolutional Neural Networks (CNNs) training at the network edge, we present DynaComm, a novel scheduler that dynamically decomposes each transmission procedure into several segments to achieve optimal layer-wise communications and computations overlapping during run-time. Through experiments, we verify that DynaComm manages to achieve optimal layer-wise scheduling for all cases compared to competing strategies while the model accuracy remains untouched.

Replicated metadata server cluster (RMSC) is highly efficient to be used in distributed filesystems while facing data-driven scenarios (e.g., massive-scale distributed machine learning tasks). Yet, when considering cost-effectiveness and system utilization, the cluster scale is commonly restricted in practice. Within this context, servers in the cluster start to suffer from load-oscillations at higher system utilization due to clients’ congestion-unaware behaviors and unintelligent selection strategies (i.e., servers in the cluster are preferred then evaded intermittently). The consequences brought by load-oscillations degrade the overall performance of the whole system to some extent. One solution to tackle this problem is having clients share a part of the responsibility and behave more wisely for stability concerns. So in this paper, we present a Congestion-Aware Request Dispatching scheme, CARD, which is mainly conducted at clients and directed by a rate control mechanism. Through extensive experiments, we verify that CARD is highly efficient in resolving load-oscillations in RMSC. Apart from this, our results show that RMSC with our congestion-aware based optimization achieves better scalability compared to previous implementations under targeted workloads, especially in heterogeneous environments.