A Tracing Based Model to Identify Bottlenecks in Physically Distributed Applications

Casse, Clement; Berthou, Pascal; Owezarski, Philippe; Josset, Sébastien

doi:10.1109/icoin53446.2022.9687217

Cited by 12 publications

(19 citation statements)

References 14 publications

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“…In particular, t i is the score defined in Equation (9) for the endpoint e i , 𝜂 i j,k is the effect size deriving from the application of the Wilcoxon signed-rank test to the samples containing the response times for the endpoint e i obtained by deploying the SUT according to D C j and D C k , and 𝛽 i j,k is a factor introduced to properly take 𝜂 i j,k into account. The latter is defined as follows:…”

Section: Test Results Evaluationmentioning

confidence: 99%

“…‡ The collected traces can be processed to obtain a graph model for representing the relationships and dependencies between the constituent microservices of an application and to identify potential areas of complexity or risk, for example, tightly coupled microservices or potential single points of failure. Up to now, distributed tracing has been applied to microservices-based systems for different goals: visualization, 7 performance analysis, 8,9 fault diagnosis, 10 anomaly or smell detection. [11][12][13] In this paper, we exploit distributed tracing to define a big data pipeline to dynamically collect tracing data from running applications that are used to identify a given number k of microservices groups that allow for keeping low the response times of the most critical operations under a defined workload, when they are deployed onto different virtual or physical machines, for example, during DevOps stages.…”

Section: Introductionmentioning

confidence: 99%

“…The collected traces can be processed to obtain a graph model for representing the relationships and dependencies between the constituent microservices of an application and to identify potential areas of complexity or risk, for example, tightly coupled microservices or potential single points of failure. Up to now, distributed tracing has been applied to microservices‐based systems for different goals: visualization , 7 performance analysis , 8,9 fault diagnosis , 10 anomaly or smell detection 11–13 …”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A distributed tracing pipeline for improving locality awareness of microservices applications

Colarusso,

De Caro,

Falco

et al. 2024

Softw Pract Exp

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The microservices architectural style aims at improving software maintenance and scalability by decomposing applications into independently deployable components. A common criticism about this style is the risk of increasing response times due to communication, especially with very granular entities. Locality‐aware placement of microservices onto the underlying hardware can contribute to keeping response times low. However, the complex graphs of invocations originating from users' calls largely depend on the specific workload (e.g., the length of an invocation chain could depend on the input parameters). Therefore, many existing approaches are not suitable for modern infrastructures where application components can be dynamically redeployed to take into account user expectations. This paper contributes to overcoming the limitations of static or off‐line techniques by presenting a big data pipeline to dynamically collect tracing data from running applications that are used to identify a given number of microservices groups whose deployment allows keeping low the response times of the most critical operations under a defined workload. The results, obtained in different working conditions and with different infrastructure configurations, are presented and discussed to draw the main considerations about the general problem of defining boundary, granularity, and optimal placement of microservices on the underlying execution environment. In particular, they show that knowing how a specific workload impacts the constituent microservices of an application, helps achieve better performance, by effectively lowering response time (e.g., up to a reduction), through the exploitation of locality‐driven clustering strategies for deploying groups of services.

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Section: Test Results Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A distributed tracing pipeline for improving locality awareness of microservices applications

Colarusso,

De Caro,

Falco

et al. 2024

Softw Pract Exp

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“…In [19], the authors propose a solution for correlating information that is present in multiple traces, considering all traces as a single graph and decomposing tracing data into multiple vertices and edges. In [20], OpenTelemetry traces are processed to identify bottlenecks in the performance of a distributed application that is deployed across the cloud continuum. A data-centric perspective on tracing is detailed in [21], where the authors implement three different approaches to data-centric distributed tracing in a distributed data processing system built using microservices, and discuss their advantages and disadvantages.…”

Section: Related Workmentioning

confidence: 99%

Data Fusion of Observability Signals for Assisting Orchestration of Distributed Applications

Tzanettis

Androna

Zafeiropoulos

et al. 2022

Sensors

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Nowadays, various frameworks are emerging for supporting distributed tracing techniques over microservices-based distributed applications. The objective is to improve observability and management of operational problems of distributed applications, considering bottlenecks in terms of high latencies in the interaction among the deployed microservices. However, such frameworks provide information that is disjoint from the management information that is usually collected by cloud computing orchestration platforms. There is a need to improve observability by combining such information to easily produce insights related to performance issues and to realize root cause analyses to tackle them. In this paper, we provide a modern observability approach and pilot implementation for tackling data fusion aspects in edge and cloud computing orchestration platforms. We consider the integration of signals made available by various open-source monitoring and observability frameworks, including metrics, logs and distributed tracing mechanisms. The approach is validated in an experimental orchestration environment based on the deployment and stress testing of a proof-of-concept microservices-based application. Helpful results are produced regarding the identification of the main causes of latencies in the various application parts and the better understanding of the behavior of the application under different stressing conditions.

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“…Existing approaches do not meet our needs [14]- [16]. They do not collect the needed data of our objectives.…”

mentioning

confidence: 99%

Telemetry of Legacy Web Applications: An Industrial Case Study

Shatnawi,

Rima,

Alshara

et al. 2023

Preprint

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<p>Berger-Levrault, like many companies, has legacy web applications that still bring great values, and cannot be easily replaced. To maintain these applications, it needs data about user navigation, backend actions and client-server data exchange. Berger-Levrault has relied on a traditional logging approach that partially collects these data, requires modifying the application code and heavily impacts its performance. To address the limitations of this logging approach, we propose to replace it by a modern software telemetry approach. Existing telemetry approaches do not meet our needs, they should be extended based on our objectives, technological constraints and industrial regulations. In this paper, we report our experience in instrumenting real, large-scale, industrial legacy web applications based on a telemetry approach. Our goal is to automatically instrument legacy web applications to collect data fulfilling our industrial needs. We extend the automatic instrumentation capabilities of OpenTelemetry agents to instrument our applications without modifying their code. We define a telemetry architecture to integrate telemetry components with legacy web applications. Also, we empirically evaluate the performance overhead produced by our agents. The results show that there is no significant overhead when using OpenTelemetry agents. However, this overhead is sensitive to the size of data being serialized when instrumenting client-server data exchange. Moreover, we discuss lessons learned about the technical challenges we faced during the industrialization of our approach. Also, we empirically evaluate the performance overhead produced by our agents. The results show that there is no significant overhead when using Open Telemetry agents. However, this overhead is sensitive to the size of data being serialized when instrumenting client-server data exchange. Moreover, we discuss lessons learned about some technical challenges we faced during the industrialization of our telemetry approach.</p>

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A Tracing Based Model to Identify Bottlenecks in Physically Distributed Applications

Cited by 12 publications

References 14 publications

A distributed tracing pipeline for improving locality awareness of microservices applications

A distributed tracing pipeline for improving locality awareness of microservices applications

Data Fusion of Observability Signals for Assisting Orchestration of Distributed Applications

Telemetry of Legacy Web Applications: An Industrial Case Study

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