A Containerized Big Data Streaming Architecture for Edge Cloud Computing on Clustered Single-board Devices

Scolati, Remo; Fronza, Ilenia; Ioini, Nabil El; Samir, Areeg; Pahl, Claus

doi:10.5220/0007695000680080

Cited by 33 publications

(26 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In other works [35][36][37][38], we have implemented and experimentally evaluated 8-node cluster architectures, with 3 types of containers (orchestration, service provider, service consumer) in different architectural settings. Our observation from these experiments was that taking into account specific architecture configuration will not result in sufficiently generic results.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Detecting and Localizing Anomalies in Container Clusters Using Markov Models

Samir

Pahl

2020

Electronics

Self Cite

View full text Add to dashboard Cite

Detecting the location of performance anomalies in complex distributed systems is critical to ensuring the effective operation of a system, in particular, if short-lived container deployments are considered, adding challenges to anomaly detection and localization. In this paper, we present a framework for monitoring, detecting and localizing performance anomalies for container-based clusters using the hierarchical hidden Markov model (HHMM). The model aims at detecting and localizing the root cause of anomalies at runtime in order to maximize the system availability and performance. The model detects response time variations in containers and their hosting cluster nodes based on their resource utilization and tracks the root causes of variations. To evaluate the proposed framework, experiments were conducted for container orchestration, with different performance metrics being used. The results show that HHMMs are able to accurately detect and localize performance anomalies in a timely fashion.

show abstract

Section: Discussionmentioning

confidence: 99%

“…So far, the framework only focuses on some common performance and load-related anomalies. In the future, we will include more anomaly scenarios for different scenarios and architectures [2,36], and we will handle more metrics to capture more cases, but still maintaining accuracy.…”

Section: Discussionmentioning

confidence: 99%

Detecting and Localizing Anomalies in Container Clusters Using Markov Models

Samir

Pahl

2020

Electronics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Containerization is a virtualization technology that, instead of virtualizing hardware, separates processes from each other by utilizing certain features of the Linux kernel. It has been successfully used on Raspberry Pis [65,66]. Docker containers bundle an application along with all of its dependencies.…”

Section: Raspberry Pimentioning

confidence: 99%

Fuzzy Container Orchestration for Self-adaptive Edge Architectures

Gand

Fronza

Ioini

et al. 2021

Communications in Computer and Information Science

Self Cite

View full text Add to dashboard Cite

The edge or edge computing refers to the computational infrastructure between sensors and Internet-of-Things world on the one side and centralised cloud data centres on the other. Edge clusters could consist of small single-board devices, which are widely used in different applications. This includes microcontrollers regulating an industrial process or controllers monitoring and managing traffic roadside. Although generally hardware capabilities of devices are growing, resources at the edge are often still limited, requiring an intelligent resource management. This can done through self-adaptive scaling mechanisms in these clusters allowing to scale components of the application in the cluster. We introduce here an auto-scalable cluster architecture for lightweight container-based edge devices. A serverless architecture forms the core of this architecture. The auto-scaling component is based on fuzzy logic in order to better manage uncertainty problems arising in such contexts. Our evaluations show that our platform architecture and the auto-scaling functionality satisfy the need of lightweightness in edge architecture.

show abstract

“…Sistemi za obradu velikih skupova podataka na ivici zasnovanom na klasteru ugrađenih sistema. Rad [5] predstavlja ideju obrade velikih skupova podataka na ivici u približno realnom vremenu. Predloženo rešenje implementira sistem koji se sastoji od klastera Raspberry Pi [5] uređaja.…”

Section: Prethodna Rešenjaunclassified

“…Rad [5] predstavlja ideju obrade velikih skupova podataka na ivici u približno realnom vremenu. Predloženo rešenje implementira sistem koji se sastoji od klastera Raspberry Pi [5] uređaja. Prednost predloženog rešenja u odnosu na tradicionalne tehnike obrade velikih skupova podataka je to što se podaci obrađuju na Raspberry Pi uređajima bliže mestu prikupljanja čime se obrada ubrzava.…”

Section: Prethodna Rešenjaunclassified

Mašinsko Učenje Na Ivici Upotrebom Nvidia Jetson Tx2 Uređaja

Bućan¹

2021

Zbornik radova FTN

View full text Add to dashboard Cite

show abstract

A Containerized Big Data Streaming Architecture for Edge Cloud Computing on Clustered Single-board Devices

Cited by 33 publications

References 0 publications

Detecting and Localizing Anomalies in Container Clusters Using Markov Models

Detecting and Localizing Anomalies in Container Clusters Using Markov Models

Fuzzy Container Orchestration for Self-adaptive Edge Architectures

Mašinsko Učenje Na Ivici Upotrebom Nvidia Jetson Tx2 Uređaja

Contact Info

Product

Resources

About