ServiceRank: Root Cause Identification of Anomaly in Large-Scale Microservice Architectures

Ma, Meng; Lin, Weilan; Pan, Disheng; Wang, Ping

doi:10.1109/tdsc.2021.3083671

Cited by 23 publications

(11 citation statements)

References 28 publications

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“…-Machine learning algorithms including Iforest [82] used in [50], deep learning [52] used in [22], decision tree [83] employed in [20]. -Mathematical models counting service power model used in [18] [29], knee point [84] deployed in [24], random walk [85] used in studies [24] [26] [29], queuing theory [86] employed in [22] [23], and outlier detection [87] reported in [20]. -Modeling algorithms whose main objective is to model elastic rules, resource usage, etc.…”

Section: [46] [62] [44] [50] [34] [18] [19] [20] [22] [23] [24] [26] ...mentioning

confidence: 99%

“…-Graph models such as Directed Acyclic Graph (DAG) [88] used in [24] [26], Completed Partially Directed Acyclic Graph (CPDAG) [89] employed in [24] [26], Causal Bayesian Networks (CBN) [90] illustrated in [23], Graphical Variational Auto-Encoder (GVAE) [91] deployed in [23], and PC-algorithm [92] used in [24]. -Others adopted techniques and algorithms that do not belong to the previously detailed classes.…”

Section: [46] [62] [44] [50] [34] [18] [19] [20] [22] [23] [24] [26] ...mentioning

confidence: 99%

“…-Others adopted techniques and algorithms that do not belong to the previously detailed classes. Indeed, they include resource correlation with request flow technique, consisting in linking the resource consumption with the requests flow, which was employed in numerous studies [43] [29] [30], couterfactual queries [93] reported in [23], threshold based method used in [30], execution history employed in [20] [23] [29], remote procedure calls (RPC) level tracing [94] used in [22] [23], circuit breaker pattern [95] adopted in [24], correlation calibration mechanism employed in [24], and redundancy checking illustrated in [29].…”

Section: [46] [62] [44] [50] [34] [18] [19] [20] [22] [23] [24] [26] ...mentioning

confidence: 99%

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Cloud Elasticity of Microservices-based Applications: A Survey

Fourati,

Marzouk,

Jmaiel

2024

Preprint

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Elasticity is an essential treatment in Cloudenvironment employed in academic and industrial contexts. The main purpose of elasticity is to reduce thedeployment cost while optimizing computing resources.Multiple studies were conducted to tackle classic applications using monolithic architecture deployed withvirtual machines (VMs). However, with the spread ofmicroservice pattern, recent studies have been investigating this new trend using containers. This paperclassifies and discusses existing approaches dealing withcloud elasticity. It provides a novel taxonomy for elasticapproaches while focusing on microservices-based solutions. We additionally specify the strength and theshortcomings of each class of works. As a conclusion,we report the challenges for microservices-based applications elasticity and provide requirements for futureinvestigations.

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Section: [46] [62] [44] [50] [34] [18] [19] [20] [22] [23] [24] [26] ...mentioning

confidence: 99%

Section: [46] [62] [44] [50] [34] [18] [19] [20] [22] [23] [24] [26] ...mentioning

confidence: 99%

Section: [46] [62] [44] [50] [34] [18] [19] [20] [22] [23] [24] [26] ...mentioning

confidence: 99%

See 1 more Smart Citation

Cloud Elasticity of Microservices-based Applications: A Survey

Fourati,

Marzouk,

Jmaiel

2024

Preprint

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“…Disheng Pan [81] Peking University China Cesare Pautasso [58] University of Lugano Switzerland Dan Pei [102] Tsinghua University, Beijing National Research Center for Information Science and Technology…”

Section: Declarationsmentioning

confidence: 99%

“…Rajan [100] IBM research USA Michael K. Reiter [76] University of North Carolina at Chapel USA Justin Reynolds [83] Netflix USA Ruud de Rooij [83] Netflix USA Casey Rosenthal [83, 92] [60] Netflix USA Barbara Russo [80] Free University of Bozen-Bolzano Italy Stefano Russo [94] University of Naples Federico II Italy Amleto Di Salle [98] University of L'Aquila Italy Ivan Salvadori [78] Federal University of Santa Catarina USA Chris Sanden [60] Netflix USA Henning Schulz [80] NovaTec Consulting GmbH Germany Itai Segall [88] Nokia Bell Labs USA Vyas Sekar [76] Carnegie Mellon University USA Saurabh Sinha [93,97] IBM USA Frank Siqueira [78] Federal University of Santa Catarina USA Tiago Boldt Sousa [79] University of Porto Portugal Jun Sun [53] Singapore University of Technology and Design Singapore Peter F. Sweeney [100] IBM Research USA Alexander Tarvo [100] IBM Research USA Johan Tordsson [77] Elastisys AB, Distributed and Operating Systems Group Sweden, Germany Luca Ussi [88] University of Milano Italy Mgr. Martin Vecera [85] Masaryk University Czech Republic Jay Vyas [95] Red Hat Inc USA Ping Wang [81] Peking University China Jun Wei [90] State Key Laboratory of Computer Science, University of Chinese Academy of Sciences China Guoquan Wu [90] State Key Laboratory of Computer Science, University of Chinese Academy of Sciences China Li Wu [77] Distributed and Operating Systems Group Sweden, Germany Michael Wurster [86] University of Stuttgart Germany Tao Xie [53] University of Illinois USA Haowen Xu [102] Tsinghua University, Beijing National Research Center for Information Science and Technology…”

Section: Declarationsmentioning

confidence: 99%

A Systematic Review on Microservice Testing

Panahande,

Miller

2023

Preprint

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Microservices have emerged to change software architecture into a style of loosely coupled facilities cooperating via a lightweight way. This architecture makes a more scalable and resilient artifact that is easier to evolve and deploy. However, how can we ensure that microservices are defect-free and satisfy expected behaviors? Like other software styles, microservices must be tested in various ways. Employing heterogeneous platforms in microservice development and microservices characteristics, such as scalability and resiliency demand different test approaches from other software applications. This paper produces a systematic literature review on articles published from 2011 on microservice testing. Of the 98 relevant studies found in the literature, 35 have been included in this survey. Primary studies have been summarized by their novelty, benefits, and gaps. Moreover, they are compared in terms of their techniques, outcomes, and evaluations. Studying the current test method’s limitations identifies open problems discussed during the paper. Results of this study identify the current achievements and future possible directions in the microservice testing domain. This survey finds resiliency testing and finding abnormal components in a production environment as the most common approaches for testing and validating microservice integrations and behavior. However, there is still room for generalizing fault injection methods and addressing microservice-specific features in test approaches.

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Cloud Elasticity of Microservices‐Based Applications: A Survey

Fourati,

Marzouk,

Jmaiel

2024

Concurrency and Computation

View full text Add to dashboard Cite

Elasticity is an essential treatment in Cloud environment employed in academic and industrial contexts. The main purpose of elasticity is to reduce the deployment cost while optimizing computing resources. Multiple studies were conducted to tackle classic applications using monolithic architecture deployed with virtual machines (VMs). However, with the spread of microservice pattern, recent studies have been investigating this new trend using containers. This paper classifies and discusses existing approaches dealing with cloud elasticity. It provides a novel taxonomy for elastic approaches while focusing on microservices‐based solutions. We additionally specify the strength and the shortcomings of each class of works. As a conclusion, we report the challenges for microservices‐based applications elasticity and provide requirements for future investigations.

show abstract

ServiceRank: Root Cause Identification of Anomaly in Large-Scale Microservice Architectures

Cited by 23 publications

References 28 publications

Cloud Elasticity of Microservices-based Applications: A Survey

Cloud Elasticity of Microservices-based Applications: A Survey

A Systematic Review on Microservice Testing

Cloud Elasticity of Microservices‐Based Applications: A Survey

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