Distributed traces contain valuable information but are often massive in volume, posing a core challenge in tracing framework design: balancing the tradeoff between preserving essential trace information and reducing trace volume. To address this tradeoff, previous approaches typically used a '1 or 0' sampling strategy: retaining sampled traces while completely discarding unsampled ones. However, based on an empirical study on real-world production traces, we discover that the '1 or 0' strategy actually fails to effectively balance this tradeoff.

As large language models (LLMs) are widely applied across various domains, improving the quality of LLM inference services is essential. In this paper, we find that optimizing configuration parameters of LLM inference engines can significantly improve LLM inference performance in terms of latency and throughput. Therefore, we propose LLMConf, an automated performance tuning system that optimizes multiple LLM inference performance metrics by searching for the optimal configuration parameters of the LLM inference engine. We first introduce a knowledge-enhanced approach to identify the set of configuration parameters (LLMConfigs) that most significantly impact LLM performance from the adjustable parameters provided by the LLM inference engine. We then perform automated data collection to build functional relationships between LLMConfigs and each performance metric. Additionally, LLMConf employs a multi-objective optimization module to obtain optimal LLMConfigs for simultaneously optimizing multiple performance metrics. The experimental results show that LLMConf significantly outperforms existing methods. Compared to the default configuration parameters of the LLM inference engine, LLMConf achieves an average improvement of 20.1% across 7 key performance metrics. Moreover, experiments demonstrate that LLMConf has strong transferability across diverse datasets, varying concurrency levels and different LLM base models.

Consensus is critical for distributed databases as it ensures the consistency of states across nodes, reinforcing the robustness of the overall system. However, faults related to the consensus protocols such as Paxos can lead to serious issues in distributed databases. Such consensus issues impact the correctness and availability of these databases. Therefore, to automatically uncover consensus issues in distributed databases, we propose Conan, a framework designed with fuzzing-driven fault injection. Conan applies a state-guided fuzzing algorithm to effectively explore the fault search space. Moreover, Conan employs hybrid fault sequences that combines fine-grained message-level faults and coarse-grained system-level faults to enhance fault injection. We implement and evaluate Conan on 3 widely-used distributed databases, including etcd, rqlite and openGauss. Finally, Conan has successfully uncovered previously unknown consensus issues, some of which are not detected by existing approaches.

Serverless becomes popular as a novel computing paradigms for cloud native services. However, the complexity and dynamic nature of serverless applications present significant challenges to ensure system availability and performance. There are many root cause analysis (RCA) methods for microservice systems, but they are not suitable for precise modeling serverless applications. This is because: (1) Compared to microservice, serverless applications exhibit a highly dynamic nature. They have short lifecycle and only generate instantaneous pulse-like data, lacking long-term continuous information. (2) Existing methods solely focus on analyzing the running stage and overlook other stages, failing to encompass the entire lifecycle of serverless applications. To address these limitations, we propose FaaSRCA, a full lifecycle root cause analysis method for serverless applications. It integrates multi-modal observability data generated from platform and application side by using Global Call Graph. We train a Graph Attention Network (GAT) based graph autoencoder to compute reconstruction scores for the nodes in global call graph. Based on the scores, we determine the root cause at the granularity of the lifecycle stage of serverless functions. We conduct experimental evaluations on two serverless benchmarks, the results show that FaaSRCA outperforms other baseline methods with a top-k precision improvement ranging from 21.25% to 81.63%.

Serverless computing, also known as Function-as-a-Service (FaaS), is a significant development trend in modern software system architecture. The workflow composition of multiple short-lived functions has emerged as a prominent pattern in FaaS, exposing a considerable resources configuration challenge compared to individual independent serverless functions. This challenge unfolds in two ways. Firstly, workflows frequently encounter dynamic and concurrent user workloads, increasing the risk of QoS violations. Secondly, the performance of a function can be affected by the resource reprovision of other functions within the workflow.