Towards self-adaptive peer-to-peer monitoring for fog environments

Colombo, Vera; Tundo, Alessandro; Ciavotta, Michele; Mariani, Leonardo

doi:10.1145/3524844.3528055

Cited by 10 publications

(4 citation statements)

References 24 publications

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“…Netdata offers internal and external plugins for gathering KPIs, and these plugins can be deployed according to the presented probe deployment patterns (e.g., Internal-T1P * or Shared-T * P * ). Other works focusing on fog monitoring [82], [83], [84], [85] rely on monitoring agents for collecting KPIs. FMone [83] employs agents executed in separate Docker containers similarly to the Shared-T * P1 and Shared-T1P1 patterns.…”

Section: Related Workmentioning

confidence: 99%

“…Souza et al's approach [84] involves the utilization of both internal and external agents, which can be deployed using the Internal-T1P * pattern or external unit patterns, such as Shared-T * P1 and Partially-shared-T * P * , depending on the execution environment or sharing policies. Additionally, both FogMon [82] and its self-adaptive extension [85] employ internal monitoring agents to gather multiple KPIs, following the Internal-T1P * pattern.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Monitoring Probe Deployment Patterns for Cloud-Native Applications: Definition and Empirical Assessment

Tundo,

Mobilio,

Riganelli

et al. 2024

IEEE Trans. Serv. Comput.

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Monitoring is a key feature to enhance systems with the capability to anticipate, detect, predict, and mitigate failures, while providing Quality of Service (QoS) monitoring and Service Level Agreements (SLAs) guarantee. Monitoring frameworks can serve these purposes by deploying probes according to many possible patterns that have different features, for instance in terms of efficiency and privacy. So far, these probe deployment patterns have not been systematically defined, analyzed and assessed. Thus, engineers who design and configure their monitoring systems have to take decisions only based on partial knowledge and personal experience.This paper addresses this knowledge gap, by presenting a systematic analysis of 11 probe deployment patterns, their known uses, and implementations. We assess these patterns qualitatively, and quantitatively using both VMs and containers. Results show the targets have negligible resource consumption (e.g., less than 1% CPU usage), while the probe holder consumption is mainly significant in relation to memory consumption, reaching up to 10 GiB in our experiments. Our findings suggest that reusing probes and holders among users can generally enhance efficiency and scalability when direct access to the monitored target is not an option. We generate a set of best practices that can assist engineers in configuring their monitoring systems. Finally, we showcase the application of certain patterns through three practical usage scenarios, which feature diverse technologies and requirements.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Monitoring Probe Deployment Patterns for Cloud-Native Applications: Definition and Empirical Assessment

Tundo,

Mobilio,

Riganelli

et al. 2024

IEEE Trans. Serv. Comput.

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show abstract

“…In [ 114 ], the authors present a self-adaptive fog monitoring software that uses a hierarchical P2P architecture that is capable of modifying its operation based on an “MAPE-K” feedback loop. This variation in its behavior is possible thanks to the data that the system collects from its environment.…”

Section: Literature Review and Analysismentioning

confidence: 99%

Self-* Capabilities of Cloud-Edge Nodes: A Research Review

S-Julián

Lacalle

Vaño

et al. 2023

Sensors

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Most recent edge and fog computing architectures aim at pushing cloud-native traits at the edge of the network, reducing latency, power consumption, and network overhead, allowing operations to be performed close to data sources. To manage these architectures in an autonomous way, systems that materialize in specific computing nodes must deploy self-* capabilities minimizing human intervention across the continuum of computing equipment. Nowadays, a systematic classification of such capabilities is missing, as well as an analysis on how those can be implemented. For a system owner in a continuum deployment, there is not a main reference publication to consult to determine what capabilities do exist and which are the sources to rely on. In this article, a literature review is conducted to analyze the self-* capabilities needed to achieve a self-* equipped nature in truly autonomous systems. The article aims to shed light on a potential uniting taxonomy in this heterogeneous field. In addition, the results provided include conclusions on why those aspects are too heterogeneously tackled, depend hugely on specific cases, and shed light on why there is not a clear reference architecture to guide on the matter of which traits to equip the nodes with.

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“…The closest proposed to our work is AdaptiveMon [37], which presents self-adaptive monitoring for fog environments. AdaptiveMon can adjust the frequency of reported metrics in a hierarchical peer-to-peer monitoring architecture.…”

Section: Motivationmentioning

confidence: 99%

Acala: Aggregate Monitoring for Geo-Distributed Cluster Federations

Huang

Pierre

2023

Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing

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Monitoring plays a vital role in geo-distributed cluster federation environments to accurately schedule applications across geographically dispersed computing resources. However, using a fixed frequency for collecting monitoring data from clusters may waste network bandwidth and is not necessary for ensuring accurate scheduling. In this paper, we propose Adaptive Prometheus Federation (AdapPF), an extension of the widelyused open-source monitoring tool, Prometheus, and its feature, Prometheus Federation. AdapPF aims to dynamically adjust the collection frequency of monitoring data for each cluster in geodistributed cluster federations. Based on actual deployment in the geo-distributed Grid'5000 testbed, our evaluations demonstrate that AdapPF can achieve comparable results to Prometheus Federation with 5-seconds scrape interval while reducing crosscluster network traffic by 36%.

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Towards self-adaptive peer-to-peer monitoring for fog environments

Cited by 10 publications

References 24 publications

Monitoring Probe Deployment Patterns for Cloud-Native Applications: Definition and Empirical Assessment

Monitoring Probe Deployment Patterns for Cloud-Native Applications: Definition and Empirical Assessment

Self-* Capabilities of Cloud-Edge Nodes: A Research Review

Acala: Aggregate Monitoring for Geo-Distributed Cluster Federations

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