Autonomic Management Framework for Cloud-Native Applications

Kosińska, Joanna; Zieliński, Krzysztof

doi:10.1007/s10723-020-09532-0

Cited by 27 publications

(26 citation statements)

References 26 publications

(28 reference statements)

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“…Intra-and inter-service execution paths are described as a behavioral model of the microservices under observation, serving as input to an application workload generator that produces realistic trace data. In [29], observability is considered as one of the main desirable features for supporting autonomic management of cloud-native applications. In [30,31], a service-mesh approach for supporting observability features is adopted to avoid custom instrumentation of each application based on the use of specific software libraries.…”

Section: Related Workmentioning

confidence: 99%

Data Fusion of Observability Signals for Assisting Orchestration of Distributed Applications

Tzanettis

Androna

Zafeiropoulos

et al. 2022

Sensors

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Nowadays, various frameworks are emerging for supporting distributed tracing techniques over microservices-based distributed applications. The objective is to improve observability and management of operational problems of distributed applications, considering bottlenecks in terms of high latencies in the interaction among the deployed microservices. However, such frameworks provide information that is disjoint from the management information that is usually collected by cloud computing orchestration platforms. There is a need to improve observability by combining such information to easily produce insights related to performance issues and to realize root cause analyses to tackle them. In this paper, we provide a modern observability approach and pilot implementation for tackling data fusion aspects in edge and cloud computing orchestration platforms. We consider the integration of signals made available by various open-source monitoring and observability frameworks, including metrics, logs and distributed tracing mechanisms. The approach is validated in an experimental orchestration environment based on the deployment and stress testing of a proof-of-concept microservices-based application. Helpful results are produced regarding the identification of the main causes of latencies in the various application parts and the better understanding of the behavior of the application under different stressing conditions.

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Section: Related Workmentioning

confidence: 99%

Data Fusion of Observability Signals for Assisting Orchestration of Distributed Applications

Tzanettis

Androna

Zafeiropoulos

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…In the state of the art, there are other research works related to autonomic computing [11,12,26]. All of these approaches have been interested in modeling and implementing autonomic environments in a centralized or decentralized manner.…”

Section: Related Workmentioning

confidence: 99%

Optimal Autonomic Management of Service-Based Business Processes in the Cloud

Hadded

Hamrouni

2022

Preprint

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Cloud computing is an emerging paradigm that provides hardware, platform and software resources as services over the internet in a pay-as-you-go model. It is being increasingly used for hosting and executing service-based business processes. These business processes are exposed to dynamic evolution during their life-cycle due to the highly dynamic evolution of cloud environments. The main adopted technique is to couple cloud computing with autonomic management in order to build autonomic computing systems. Almost all the existing approaches on autonomic computing have been focused on modeling and implementing autonomic mechanisms without paying any attention to the optimization of the autonomic management cost. Therefore, in this paper, we propose a novel approach based on binary linear program for determining the optimal allocation of cloud resources to manage a service-based business process which guarantees the specific requirements of customers and minimizes the management monetary cost. Then, to validate our approach under realistic conditions and inputs, we extend the CloudSim simulator to model and simulate the behaviour of business processes and their management in a cloud environment. Experiments conducted on two real datasets highlight the effectiveness of our approach.

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“…Embracing automation at every layer of performance management enables us to reduce costs while improving outcomes. Kosińska and Zielinski [14] lists the definition of autonomic management requirements of cloud-native applications, and the authors propose that the automation is achieved via high-level policies, while autonomy features are accomplished via the rule engine support. One such feature of online scheduling in a cloud-native context is migration.…”

Section: Latency-critical Cloud-native Applications and The Edge Cloudmentioning

confidence: 99%

Ultra-Reliable and Low-Latency Computing in the Edge with Kubernetes

Toka

2021

J Grid Computing

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Novel applications will require extending traditional cloud computing infrastructure with compute resources deployed close to the end user. Edge and fog computing tightly integrated with carrier networks can fulfill this demand. The emphasis is on integration: the rigorous delay constraints, ensuring reliability on the distributed, remote compute nodes, and the sheer scale of the system altogether call for a powerful resource provisioning platform that offers the applications the best of the underlying infrastructure. We therefore propose Kubernetes-edge-scheduler that provides high reliability for applications in the edge, while provisioning less than 10% of resources for this purpose, and at the same time, it guarantees compliance with the latency requirements that end users expect. We present a novel topology clustering method that considers application latency requirements, and enables scheduling applications even on a worldwide scale of edge clusters. We demonstrate that in a potential use case, a distributed stream analytics application, our orchestration system can reduce the job completion time to 40% of the baseline provided by the default Kubernetes scheduler.

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Autonomic Management Framework for Cloud-Native Applications

Cited by 27 publications

References 26 publications

Data Fusion of Observability Signals for Assisting Orchestration of Distributed Applications

Data Fusion of Observability Signals for Assisting Orchestration of Distributed Applications

Optimal Autonomic Management of Service-Based Business Processes in the Cloud

Ultra-Reliable and Low-Latency Computing in the Edge with Kubernetes

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