Pivot tracing

Mace, Jonathan; Roelke, Ryan; Fonseca, Rodrigo

doi:10.1145/2815400.2815415

Cited by 139 publications

(24 citation statements)

References 39 publications

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“…As can be seen from Mace et al [16] and Rehfeld et al [20], performance analysis from disparate samples is conducted predominantly by comparing timestamps. We have emphasised portability and simplicity, however at haptics rates the amount of data to be processed can become prohibitive.…”

Section: Discussionmentioning

confidence: 99%

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Profiling Distributed Virtual Environments by Tracing Causality

Friston

Griffith

Swapp

et al. 2018

2018 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)

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Real-time interactive systems such as virtual environments have high performance requirements, and profiling is a key part of the optimisation process to meet them. Traditional techniques based on metadata and static analysis have difficulty following causality in asynchronous systems. In this paper we explore a new technique for such systems. Timestamped samples of the system state are recorded at instrumentation points at runtime. These are assembled into a graph, and edges between dependent samples recovered. This approach minimises the invasiveness of the instrumentation, while retaining high accuracy. We describe how our instrumentation can be implemented natively in common environments, how its output can be processed into a graph describing causality, and how heterogeneous data sources can be incorporated into this to maximise the scope of the profiling. Across three case studies, we demonstrate the efficacy of this approach, and how it supports a variety of metrics for comprehensively bench-marking distributed virtual environments.

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Section: Discussionmentioning

confidence: 99%

“…Mace et al [16] proposed Pivot Tracing. They introduced the happened-before join, allowing the construction of chains of arbitrary samples for causal tracing, as done in tools such as milliScope.…”

Section: Previous Workmentioning

confidence: 99%

Profiling Distributed Virtual Environments by Tracing Causality

Friston

Griffith

Swapp

et al. 2018

2018 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)

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“…Like in earlier systems [22,35], monitoring in Scrub takes the form of formulating and executing high-level queries over events defined and generated by the application under study. Scrub provides an API for the application developer to define and generate events, and supports a query language for the troubleshooter to express queries over these events.…”

Section: Design Philosophymentioning

confidence: 99%

Scrub

Satish

Shiou

Zhang

et al. 2018

Proceedings of the Thirteenth EuroSys Conference

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Scrub is a troubleshooting tool for distributed applications that operate under strict SLOs common in production environments. It allows users to formulate queries on events occurring during execution in order to assess the correctness of the application's operation. Scrub has been in use for two years at Turn, where developers and users have relied on it to resolve numerous issues in its online advertisement bidding platform. This platform spans thousands of * This work was done when the author was at Turn Inc.

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“…Pivot Tracing [40] provides system diagnosis by combining dynamic instrumentation with a novel relational operator. OpenTracing 6 provides system diagnosis by using a directed acyclic graph.…”

Section: Related Workmentioning

confidence: 99%

Enabling Dependability-Driven Resource Use and Message Log-Analysis for Cluster System Diagnosis

Chuah

Jhumka

Alt

et al. 2017

2017 IEEE 24th International Conference on High Performance Computing (HiPC)

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Recent work have used both failure logs and resource use data separately (and together) to detect system failureinducing errors and to diagnose system failures. System failure occurs as a result of error propagation and the (unsuccessful) execution of error recovery mechanisms. Knowledge of error propagation patterns and unsuccessful error recovery is important for more accurate and detailed failure diagnosis, and knowledge of recovery protocols deployment is important for improving system reliability. This paper presents the CORRMEXT framework which carries failure diagnosis another significant step forward by analyzing and reporting error propagation patterns and degrees of success and failure of error recovery protocols. CORRMEXT uses both error messages and resource use data in its analyses. Application of CORRMEXT to data from the Ranger supercomputer have produced new insights. CORRMEXT has: (i) identified correlations between resource use counters that capture recovery attempts after an error, (ii) identified correlations between error events to capture error propagation patterns within the system, (iii) identified error propagation and recovery paths during system execution to explain system behaviour, (iv) showed that the earliest times of change in system behaviour can only be identified by analyzing both the correlated resource use counters and correlated errors. CORRMEXT will be installed on the HPC clusters at the Texas Advanced Computing Center in Autumn 2017.

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Pivot tracing

Cited by 139 publications

References 39 publications

Profiling Distributed Virtual Environments by Tracing Causality

Profiling Distributed Virtual Environments by Tracing Causality

Scrub

Enabling Dependability-Driven Resource Use and Message Log-Analysis for Cluster System Diagnosis

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