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ACM SIGCOMM Conference

作者： Ashok, Sachin Harsh, Vipul Godfrey, Brighten Mittal, Radhika Parthasarathy, Srinivasan Shwartz, Larisa Univ Illinois Champaign IL 61820 USA Broadcom San Jose CA USA IBM Res Armonk NY USA

ISBN: (纸本)9798400706141

Monitoring and debugging modern cloud-based applications is challenging since even a single API call can involve many interdependent distributed microservices. To provide observability for such complex systems, distributed tracing frameworks track request flow across the microservice call tree. However, such solutions require instrumenting every component of the distributed application to add and propagate tracing headers, which has slowed adoption. This paper explores whether we can trace requests without any application instrumentation, which we refer to as request trace reconstruction. To that end, we develop TraceWeaver, a system that incorporates readily available information from production settings (e.g., timestamps) and test environments(e.g., call graphs) to reconstruct request traces with usefully high accuracy. At the heart of TraceWeaver is a reconstruction algorithm that uses request-response timestamps to effectively prune the search space for mapping requests and applies statistical timing analysis techniques to reconstruct traces. Evaluation with (1) benchmark microservice applications and (2) a production microservice dataset demonstrates that TraceWeaver can achieve a high accuracy of similar to 90% and can be meaningfully applied towards multiple use cases (e.g., finding slow services and A/B testing).

关键词： Non-Intrusive distributed tracing Graph Analysis Microservices

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Optimizing distributed data stream processing by tracing

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 90卷 578-591页

作者： Zvara, Zoltan Szabo, Peter G. N. Balazs, Barnabas Benczur, Andras Hungarian Acad Sci Inst Comp Sci & Control MTA SZTAKI 13-17 Kende U H-1111 Budapest Hungary

Heterogeneous mobile, sensor, loT, smart environment, and social networking applications have recently started to produce unbounded, fast, and massive-scale streams of data that have to be processed "on the fly". Systems that process such data have to be enhanced with detection for operational exceptions and with triggers for both automated and manual operator actions. In this paper, we illustrate how tracing in distributed data processing systems can be applied to detecting changes in data and operational environment to maintain the efficiency of heterogeneous data stream processing systems under potentially changing data quality and distribution. By the tracing of individual input records, we can (1) identify outliers in a web crawling and document processing system and use the insights to define URL filtering rules;(2) identify heavy keys, such as NULL, that should be filtered before processing;(3) give hints to improve the key-based partitioning mechanisms;and (4) measure the limits of overpartitioning if heavy thread-unsafe libraries are imported. By using Apache Spark as illustration, we show how various data stream processing efficiency issues can be mitigated or optimized by our distributed tracing engine. We describe and qualitatively compare two different designs, one based on reporting to a distributed database and another based on trace piggybacking. Our prototype implementation consists of wrappers suitable for JVM environments in general, with minimal impact on the source code of the core system. Our tracing framework is the first to solve tracing in multiple systems across boundaries and to provide detailed performance measurements suitable for automated optimization, not just debugging. (C) 2018 Elsevier B.V. All rights reserved.

关键词： distributed data processing Data stream processing distributed tracing Data provenance Apache Spark

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Transtracer: Socket-Based tracing of Network Dependencies among Processes in distributed Applications 44

Transtracer: Socket-Based Tracing of Network Dependencies am...

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44th Annual IEEE-Computer-Society International Conference on Computers, Software, and Applications (COMPSAC)

作者： Tsubouchi, Yuuki Furukawa, Masahiro Matsumoto, Ryosuke SAKURA Internet Inc Osaka Japan Hatena Co Ltd Kyoto Japan

ISBN: (纸本)9781728173030

distributed applications in web services have become increasingly complex in response to various user demands. Consequently, system administrators have difficulty understanding inter-process dependencies in distributed applications. When parts of the system are changed or augmented, they cannot identify the area of influence by the change, which might engender a more damaging outage than expected. Therefore, they must trace dependencies automatically among unknown processes. An earlier method discovered the dependency by detecting the transport connection using the Linux packet filter on the hosts at ends of the network connection. However, the extra delay to the application traffic increases because of the additional processing inherent in the packet processing in the Linux kernel. As described herein, we propose an architecture of monitoring network sockets, which are endpoints of TCP connections, to trace the dependency. As long as applications use the TCP protocol stack in the Linux kernel, the dependencies are discovered by our architecture. Therefore, monitoring processing only reads the connection information from network sockets. The processing is independent of the application communication. Therefore, the monitoring does not affect the network delay of the applications. Our experiments confirmed that our architecture reduced the delay overhead by 13-20 % and the resource load by 43.5 % compared to earlier reported methods.

关键词： distributed tracing Linux Monitoring Observability Site Reliability Engineering

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A tracing Based Model to Identify Bottlenecks in Physically distributed Applications 36

A Tracing Based Model to Identify Bottlenecks in Physically ...

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36th International Conference on Information Networking (ICOIN)

作者： Casse, Clement Berthou, Pascal Owezarski, Philippe Josset, Sebastien Univ Toulouse CNRS LAAS UPS Toulouse France Orange Labs Blagnac France

ISBN: (纸本)9781665413329

The Cloud computing paradigm has become the new industry standard way of designing large scale applications. Over the past years, we observe an increased adoption of this technology on numerous IoT - Edge applications. And while this technology comes with its promises and benefits, considering almost infinite scalability, it also comes along with its drawbacks and challenges. Detecting partial failures or bottlenecks are new obstacles that arose with the adoption of Cloud Applications. distributed tracing now allows developers to gain insight on the composition of services within a distributed Application. Today we observe an increased adoption of this technology on numerous cloud-native architectures. The project OpenTelemetry proposes a specification for traces that normalizes this new monitoring data format. In this publication we present an approach that leverages these traces to identify bottlenecks at the scale of a physically distributed application. We propose an extension of our model that builds a hierarchical property graph to exhibit bottlenecks in an application that follows the layered Cloud - IoT network model. Based on OpenTelemetry traces we can maintain a model at runtime of the whole application and compute bottlenecks. Their identification relies on the scores provided by centrality algorithms.

关键词： distributed tracing Multi-zones Cloud Edge-IoT Property Graph Graph Rewriting Hierarchical Model

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tracing Service Request Processing in Cloud

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SOFTWARE TESTING VERIFICATION & RELIABILITY 2025年第2期35卷

作者： Zhao, Yinqin Liu, Chang Yu, Tao Wang, Long Shi, Xuanqing Yang, Yong Li, Ying Wang, Zhengang Shangguan, Dongdong Tsinghua Univ REASONS Lab Beijing Peoples R China Zhongguancun Lab Beijing Peoples R China Peking Univ Natl Engn Ctr Software Engn Beijing Peoples R China Huawei Corp Lab 2012 Dongguan Peoples R China

Nowadays, more and more IT services are being hosted on cloud systems, which render cloud systems to grow into a huge complex with millions of physical servers, multi-layer software stacks and the processing of cloud service requests across many servers and software layers. It is highly demanded for cloud service providers to have the capability of getting the knowledge on cloud service behaviour directly from the service execution instead of from people's expertise. This paper studies the problem of tracing cloud service's processing of requests across components in cloud environments and proposes cloud tracing mechanisms for this purpose. We also developed model-based studies of our proposed mechanisms for analysing certain designs of the mechanisms. The implementation of the proposed cloud tracing is deployed onto the environments of OpenStack, Kubernetes and Hadoop, and the experiments on these environments demonstrate that our mechanisms effectively trace cloud service behaviour and generate a single complete request execution path, while without our mechanisms the cloud tracing either fails to work or results in thousands of path segments. Our mechanisms have a low performance overhead (2.3%) in the experiments.

关键词： cloud cloud tracing distributed tracing request execution path service request

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iTCRL: Causal-Intervention-Based Trace Contrastive Representation Learning for Microservice Systems

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2024年第10期50卷 2583-2601页

作者： Tian, Xiangbo Ying, Shi Li, Tiangang Yuan, Mengting Wang, Ruijin Zhao, Yishi Shang, Jianga Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu 610054 Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China

Nowadays, microservice architecture has become mainstream way of cloud applications delivery. distributed tracing is crucial to preserve the observability of microservice systems. However, existing trace representation approaches only concentrate on operations, relationships and metrics related to service invocations. They ignore service events that denotes meaningful, singular point in time during the service's duration. In this paper, we propose iTCRL, a novel trace contrastive representation learning approach based on causal intervention. This approach first constructs a unified graph representation for each trace to describe the runtime status of service events in traces and the complex relationships between them. Then, Causal-intervention-based Trace Contrastive Learning is proposed, which learns trace representations from causal perspective based on the unified graph representations of traces. It uses causal intervention to generate contrastive views, heterogeneous graph neural network-based trace encoder to learn trace representations, and direct causal effect to guide the training of trace encoder. Experimental results on three datasets show that iTCRL outperforms all baselines in terms of trace classification, trace anomaly detection, trace sampling and noise robustness, and also validate the contribution of Causal-intervention-based Trace Contrastive Learning.

关键词： distributed tracing Microservice systems trace representation causal intervention causal intervention graph contrastive learning graph contrastive learning causal intervention graph contrastive learning

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TraceGra: A trace-based anomaly detection for microservice using graph deep learning

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COMPUTER COMMUNICATIONS 2023年第1期204卷 109-117页

作者： Chen, Jian Liu, Fagui Jiang, Jun Zhong, Guoxiang Xu, Dishi Tan, Zhuanglun Shi, Shangsong South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Peng Cheng Lab Shenzhen Peoples R China

Trace is widely used to detect anomalies in distributed microservice systems because of the capability of precisely reconstructing user request paths. However, most existing trace-based anomaly detection approaches treat the trace as a sequence of microservice invocations with response time information, which ignores the graph structure of trace and abnormal resource consumption of the complex distributed deployment environment of microservice. In this paper, we propose TraceGra, an unsupervised encoder-decoder anomaly detection approach. TraceGra first provides a unified graph representation to combine traces and performance metrics of the container. Then, it introduces the graph neural network (GNN) and long short-term memory network (LSTM) to extract the topology and temporal features, respectively. Finally, it adds the two-part loss value with two hyperparameters as the anomaly score. The evaluation results on an open-source dataset and a local dataset collected from an ARM server cluster show that TraceGra achieves a high precision (0.97) and recall (0.93), outperforming some state-of-the-art approaches with an average increase of 0.1 in F1-score.

关键词： Anomaly detection Microservice distributed tracing Graph neural network

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Vnode: Low-Overhead Transparent tracing of ***-Based Microservice Architectures

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FUTURE INTERNET 2024年第1期16卷 13页

作者： Kabamba, Herve M. Khouzam, Matthew Dagenais, Michel R. Polytech Montreal Comp & Software Engn Dept Montreal PQ H3T 1J4 Canada Ericsson Canada Montreal PQ H4S 0B6 Canada

tracing serves as a key method for evaluating the performance of microservices-based architectures, which are renowned for their scalability, resource efficiency, and high availability. Despite their advantages, these architectures often pose unique debugging challenges that necessitate trade-offs, including the burden of instrumentation overhead. With *** emerging as a leading development environment recognized for its rapidly growing ecosystem, there is a pressing need for innovative performance debugging approaches that reduce the telemetry data collection efforts and the overhead incurred by the environment's instrumentation. In response, we introduce a new approach designed for transparent tracing and performance debugging of microservices in cloud settings. This approach is centered around our newly developed Internal Transparent tracing and Context Reconstruction (ITTCR) technique. ITTCR is adept at correlating internal metrics from various distributed trace files to reconstruct the intricate execution contexts of microservices operating in a *** environment. Our method achieves transparency by directly instrumenting the *** virtual machine, enabling the collection and analysis of trace events in a transparent manner. This process facilitates the creation of visualization tools, enhancing the understanding and analysis of microservice performance in cloud environments. Compared to other methods, our approach incurs an overhead of approximately 5% on the system for the trace collection infrastructure while exhibiting minimal utilization of system resources during analysis execution. Experiments demonstrate that our technique scales well with very large trace files containing huge numbers of events and performs analyses in very acceptable timeframes.

关键词： cloud microservices distributed tracing transparent tracing trace analysis debugging performance monitoring Node.js trace context

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Systemizing and Mitigating Topological Inconsistencies in Alibaba's Microservice Call-graph Datasets 24

Systemizing and Mitigating Topological Inconsistencies in Al...

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15th ACM/SPEC International Conference on Performance Engineering (ICPE)

作者： Huye, Darby Liu, Lan Sambasivan, Raja R. Tufts Univ Medford MA 02155 USA

ISBN: (纸本)9798400704444

Alibaba's 2021 and 2022 microservice datasets are the only publicly available sources of request-workflow traces from a large-scale microservice deployment. They have the potential to strongly influence future research as they provide much-needed visibility into industrial microservices' characteristics. We conduct the first systematic analyses of both datasets to help facilitate their use by the community. We find that the 2021 dataset contains numerous inconsistencies preventing accurate reconstruction of full trace topologies. The 2022 dataset also suffers from inconsistencies, but at a much lower rate. Tools that strictly follow Alibaba's specs for constructing traces from these datasets will silently ignore these inconsistencies, misinforming researchers by creating traces of the wrong sizes and shapes. Tools that discard traces with inconsistencies will discard many traces. We present Casper, a construction method that uses redundancies in the datasets to sidestep the inconsistencies. Compared to an approach that discards traces with inconsistencies, Casper accurately reconstructs an additional 25.5% of traces in the 2021 dataset (going from 58.32% to 83.82%) and an additional 12.18% in the 2022 dataset (going from 86.42% to 98.6%).

关键词： distributed tracing Cloud Computing Mitigating data loss

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Method of Constructing Petri Net Service Model Using distributed Trace Data of Microservices 22

Method of Constructing Petri Net Service Model Using Distrib...

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22nd Asia-Pacific Network Operations and Management Symposium (APNOMS)

作者： Sakai, Masaru Takahashi, Kensuke Kondoh, Satoshi NTT Corp NTT Network Serv Syst Labs Tokyo Japan

ISBN: (纸本)9784885523328

Microservice architectures, in which services are divided into small pieces, communicate with each other, and operate in a chain, have been spreading. distributed tracing, which tracks the processing of user requests, has been used to analyze microservices. To extract the service structure, we previously proposed a method of estimating the dependency between services and expressing it as a Petri net model. In this paper, we propose an extension of this method for the Petri net model to handle the non-deterministic processes of services. We evaluated the applicability of modeling to services with non-deterministic behaviors and confirmed that various non-deterministic processes can be modeled with our method.

关键词： distributed tracing Service Dependency Graph Process Mining Petri nets

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