RECORD
Dogfooding: use IBM Cloud services to monitor IBM Cloud infrastructure
Distributed, Parallel, and Cluster Computing · Submitted 13 Jul 2019
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim This implies that one needs to automate the process of monitoring and analysis (e.g., using machine learning and artificial intelligence).
The compiled record
The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
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The abstract, sentence by sentence
The stability and performance of Cloud platforms are essential as they directly impact customers' satisfaction. Cloud service providers use Cloud monitoring tools to ensure that rendered services match the quality of service requirements indicated in established contracts such as service-level agreements. Given the enormous number of resources that need to be monitored, highly scalable and capable monitoring tools are designed and implemented by Cloud service providers such as Amazon, Google, IBM, and Microsoft. Cloud monitoring tools monitor millions of virtual and physical resources and continuously generate logs for each one of them. Considering that logs magnify any technical issue, they can be used for disaster detection, prevention, and recovery. However, logs are useless if they are not assessed and analyzed promptly. Thus, we argue that the scale of Cloud-generated logs makes it impossible for DevOps teams to analyze them effectively. This implies that one needs to automate the process of monitoring and analysis (e.g., using machine learning and artificial intelligence). If the automation will witness an anomaly in the logs --- it will alert DevOps staff. The automatic anomaly detectors require a reliable and scalable platform for gathering, filtering, and transforming the logs, executing the detector models, and sending out the alerts to the DevOps staff. In this work, we report on implementing a prototype of such a platform based on the 7-layered architecture pattern, which leverages micro-service principles to distribute tasks among highly scalable, resources-efficient modules. The modules interact with each other via an instance of the Publish-Subscribe architectural pattern. The platform is deployed on the IBM Cloud service infrastructure and is used to detect anomalies in logs emitted by the IBM Cloud services, hence the dogfooding.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | The stability and performance of Cloud platforms are essential as they directly impact customers' satisfaction. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | Cloud service providers use Cloud monitoring tools to ensure that rendered services match the quality of service requirements indicated in established contracts such as service-level agreements. | background 1.00 | 0.04 | 0.03 | asserted 1.00 |
| s2 | Given the enormous number of resources that need to be monitored, highly scalable and capable monitoring tools are designed and implemented by Cloud service providers such as Amazon, Google, IBM, and Microsoft. | background 1.00 | 0.06 | 0.03 | asserted 1.00 |
| s3 | Cloud monitoring tools monitor millions of virtual and physical resources and continuously generate logs for each one of them. | background 1.00 | 0.07 | 0.02 | asserted 1.00 |
| s4 | Considering that logs magnify any technical issue, they can be used for disaster detection, prevention, and recovery. | background 0.99 | 0.06 | 0.03 | asserted 0.68 |
| s5 | However, logs are useless if they are not assessed and analyzed promptly. | background 0.80 | 0.07 | 0.05 | asserted 1.00 |
| s6 | Thus, we argue that the scale of Cloud-generated logs makes it impossible for DevOps teams to analyze them effectively. | background 0.48 | 0.23 | 0.22 | asserted 0.94 |
| s7 | This implies that one needs to automate the process of monitoring and analysis (e.g., using machine learning and artificial intelligence). | interpretation 0.42 | 0.18 | 0.32 | asserted 0.64 |
| s8 | If the automation will witness an anomaly in the logs --- it will alert DevOps staff. | method 0.65 | 0.09 | 0.07 | asserted 0.77 |
| s9 | The automatic anomaly detectors require a reliable and scalable platform for gathering, filtering, and transforming the logs, executing the detector models, and sending out the alerts to the DevOps staff. | background 0.56 | 0.10 | 0.09 | asserted 1.00 |
| s10 | In this work, we report on implementing a prototype of such a platform based on the 7-layered architecture pattern, which leverages micro-service principles to distribute tasks among highly scalable, resources-efficient modules. | contribution 0.68 | 0.94 | 0.16 | asserted 1.00 |
| s11 | The modules interact with each other via an instance of the Publish-Subscribe architectural pattern. | method 0.99 | 0.80 | 0.04 | asserted 1.00 |
| s12 | The platform is deployed on the IBM Cloud service infrastructure and is used to detect anomalies in logs emitted by the IBM Cloud services, hence the dogfooding. | contribution 0.61 | 0.94 | 0.25 | asserted 1.00 |
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The paper
Original abstract
The stability and performance of Cloud platforms are essential as they directly impact customers' satisfaction. Cloud service providers use Cloud monitoring tools to ensure that rendered services match the quality of service requirements indicated in established contracts such as service-level agreements. Given the enormous number of resources that need to be monitored, highly scalable and capable monitoring tools are designed and implemented by Cloud service providers such as Amazon, Google, IBM, and Microsoft. Cloud monitoring tools monitor millions of virtual and physical resources and continuously generate logs for each one of them. Considering that logs magnify any technical issue, they can be used for disaster detection, prevention, and recovery. However, logs are useless if they are not assessed and analyzed promptly. Thus, we argue that the scale of Cloud-generated logs makes it impossible for DevOps teams to analyze them effectively. This implies that one needs to automate the process of monitoring and analysis (e.g., using machine learning and artificial intelligence). If the automation will witness an anomaly in the logs --- it will alert DevOps staff. The automatic anomaly detectors require a reliable and scalable platform for gathering, filtering, and transforming the logs, executing the detector models, and sending out the alerts to the DevOps staff. In this work, we report on implementing a prototype of such a platform based on the 7-layered architecture pattern, which leverages micro-service principles to distribute tasks among highly scalable, resources-efficient modules. The modules interact with each other via an instance of the Publish-Subscribe architectural pattern. The platform is deployed on the IBM Cloud service infrastructure and is used to detect anomalies in logs emitted by the IBM Cloud services, hence the dogfooding.
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