ZeroTracer: In-Band eBPF-Based Trace Generator With Zero Instrumentation for Microservice Systems

IF 5.6 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Wanqi Yang;Pengfei Chen;Kai Liu;Huxing Zhang
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引用次数: 0

Abstract

Microservice enables agility in modern cloud-native applications but introduces challenges in fault troubleshooting due to its complex service coordination and cooperation. To tackle these challenges, distributed tracing has emerged for end-to-end request tracing and system understanding. However, existing tracing solutions often suffer from code instrumentation, trace loss and inaccuracy. To overcome these limitations, we introduce ZeroTracer, an in-kernel online distributed tracing system equipped with an eBPF-based (extended Berkeley Packet Filter) trace generator. ZeroTracer tailors for tracking HTTP requests due to its popularity in microservice systems. In our evaluations, ZeroTracer achieves remarkable trace accuracy (i.e., over 91% ) and maintains stable performance under different workload concurrency. Moreover, ZeroTracer outperforms other non-invasive approaches which fail to reconcile accurate request causality. Notably, ZeroTracer effectively tracks end-to-end requests in multi-threaded microservice applications, which is absent in existing invasive distributed tracing systems with third-party library instrumentation. Moreover, ZeroTracer introduces a negligible overhead, with latency increasing by only 0.5% –1.2% and a modest 3% –5.8% increase in CPU and memory consumption.
零跟踪器:带内基于ebpf的跟踪发生器与零仪表微服务系统
微服务在现代云原生应用程序中实现了敏捷性,但由于其复杂的服务协调和合作,给故障排除带来了挑战。为了应对这些挑战,出现了用于端到端请求跟踪和系统理解的分布式跟踪。然而,现有的跟踪解决方案经常受到代码插装、跟踪丢失和不准确的影响。为了克服这些限制,我们引入了ZeroTracer,这是一个内核内的在线分布式跟踪系统,配备了一个基于ebpf(扩展伯克利包过滤)的跟踪生成器。ZeroTracer专门用于跟踪HTTP请求,因为它在微服务系统中很流行。在我们的评估中,ZeroTracer实现了显著的跟踪精度(超过91%),并在不同的工作负载并发性下保持稳定的性能。此外,ZeroTracer优于其他无法协调准确请求因果关系的非侵入性方法。值得注意的是,ZeroTracer在多线程微服务应用程序中有效地跟踪端到端请求,这在现有的带有第三方库工具的侵入式分布式跟踪系统中是不存在的。此外,ZeroTracer引入的开销可以忽略不计,延迟仅增加0.5% -1.2%,CPU和内存消耗仅增加3% -5.8%。
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来源期刊
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems 工程技术-工程:电子与电气
CiteScore
11.00
自引率
9.40%
发文量
281
审稿时长
5.6 months
期刊介绍: IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to: a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing. b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems. c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation. d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.
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