Performance Monitoring and Root Cause Analysis for Cloud-hosted Web Applications

Jayathilaka, Hiranya; Krintz, Chandra; Wolski, Rich

doi:10.1145/3038912.3052649

Cited by 67 publications

(23 citation statements)

References 21 publications

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“…The technical scalability metrics that we used in this paper allow exploring in more detail the contribution to the system scalability of various components and techniques used in software systems. By instrumenting the software system [40] it becomes possible to determine these contributions and using this information to improve the system. Potentially, different components, technologies or technical solutions may fit different degree with the cloud platform's provisions.…”

Section: Discussionmentioning

confidence: 99%

Scalability analysis comparisons of cloud-based software services

Ahmad

András

2019

J Cloud Comp

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Performance and scalability testing and measurements of cloud-based software services are necessary for future optimizations and growth of cloud computing. Scalability, elasticity, and efficiency are interrelated aspects of cloudbased software services' performance requirements. In this work, we use a technical measurement of the scalability of cloud-based software services. Our technical scalability metrics are inspired by metrics of elasticity. We used two cloud-based systems to demonstrate the usefulness of our metrics and compare their scalability performance in two cloud platforms: Amazon EC2 and Microsoft Azure. Our experimental analysis considers three sets of comparisons: first we compare the same cloud-based software service hosted on two different public cloud platforms; second we compare two different cloud-based software services hosted on the same cloud platform; finally, we compare between the same cloud-based software service hosted on the same cloud platform with two different auto-scaling policies. We note that our technical scalability metrics can be integrated into a previously proposed utility oriented metric of scalability. We discuss the implications of our work.

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Section: Discussionmentioning

confidence: 99%

Scalability analysis comparisons of cloud-based software services

Ahmad

András

2019

J Cloud Comp

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“…For example, using mutation testing [28] we can test the impact of small changes to particular components on the scalability performance. Alternatively, by instrumenting the whole code of the system [29] and then measuring its scalability through a range of demand scenarios we can identify the components of the system at various resolutions (e.g. units, classes, functions, methods) that contribute critically to variations in scalability performance.…”

Section: Discussion and Limitationsmentioning

confidence: 99%

Measuring and Testing the Scalability of Cloud-based Software Services

Ahmad

András

2018

2018 Fifth International Symposium on Innovation in Information and Communication Technology (ISIICT)

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Performance and scalability testing and measurements of cloud-based software services are critically important in the context of rapid growth of cloud computing and supporting the delivery of these services. Cloud-based software services performance aspects are interrelated, both elasticity and efficiency are depending on the delivery of a sufficient level of scalability performance. In this work, we focused on testing and measuring the scalability of cloud-based software services in technical terms. This paper uses technical scalability metrics that address both volume and quality scaling, that inspired by earlier technical metrics of elasticity. We show how our technical scalability metrics can be integrated into an earlier utility oriented metric of scalability. We demonstrate the application of the metrics using a practical example and discuss the importance of them.

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“…There is extensive related work on monitoring systems that has shown that execution traces can help diagnose performance, efficiency, and even security problems in large-scale systems [12,24,26,42,54,77,85,93,97]. For example, X-Trace is a tracing framework that provides a comprehensive view of the behavior of services running on large-scale, potentially shared clusters.…”

Section: Cloud Tracing and Diagnosticsmentioning

confidence: 99%

“…Root cause analysis of performance abnormalities in the cloud has also gained increased attention over the past few years, as the number of interactive, latency-critical services hosted in cloud systems has increased. Jayathilaka et al [54], for example, developed Roots, a system that automatically identifies the root cause of performance anomalies in web applications deployed in Platform-as-a-Service (PaaS) clouds. Roots tracks events within the PaaS cloud using a combination of metadata injection and platform-level instrumentation.…”

Section: Cloud Tracing and Diagnosticsmentioning

confidence: 99%

Leveraging Deep Learning to Improve Performance Predictability in Cloud Microservices with Seer

Gan

Zhang

et al. 2019

SIGOPS Oper. Syst. Rev.

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Performance unpredictability is a major roadblock towards cloud adoption, and has performance, cost, and revenue ramifications. Predictable performance is even more critical as cloud services transition from monolithic designs to microservices. Detecting QoS violations after they occur in systems with microservices results in long recovery times, as hotspots propagate and amplify across dependent services.We present Seer, an online cloud performance debugging system that leverages deep learning and the massive amount of tracing data cloud systems collect to learn spatial and temporal patterns that translate to QoS violations. Seer combines lightweight distributed RPC-level tracing, with detailed low-level hardware monitoring to signal an upcoming QoS violation, and diagnose the source of unpredictable performance. Once an imminent QoS violation is detected, Seer notifies the cluster manager to take action to avoid performance degradation altogether. We evaluate Seer both in local clusters, and in large-scale deployments of end-to-end applications built with microservices with hundreds of users. We show that Seer correctly anticipates QoS violations 91% of the time, and avoids the QoS violation to begin with in 84% of cases. Finally, we show that Seer can identify applicationlevel design bugs, and provide insights on how to better architect microservices to achieve predictable performance.

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Performance Monitoring and Root Cause Analysis for Cloud-hosted Web Applications

Cited by 67 publications

References 21 publications

Scalability analysis comparisons of cloud-based software services

Scalability analysis comparisons of cloud-based software services

Measuring and Testing the Scalability of Cloud-based Software Services

Leveraging Deep Learning to Improve Performance Predictability in Cloud Microservices with Seer

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