Decision Guidance Models for Microservice Monitoring

Haselböck, Stefan; Weinreich, Rainer

doi:10.1109/icsaw.2017.31

Cited by 36 publications

(17 citation statements)

References 9 publications

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“…Monitoring data can also be used to generate models for online performance evaluation . Haselböck and Weinreich performed a comparison study of the various monitoring tools . These tools can be used to collect metrics at service level and infrastructure level.…”

Section: Taxonomy Based On Different Aspects Of Msasmentioning

confidence: 99%

Straddling the crevasse: A review of microservice software architecture foundations and recent advancements

Joseph

Chandrasekaran

2019

Softw Pract Exp

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Microservice architecture style has been gaining wide impetus in the software engineering industry. Researchers and practitioners have adopted the microservices concepts into several application domains such as the internet of things, cloud computing, service computing, and healthcare. Applications developed in alignment with the microservices principles require an underlying platform with management capabilities to coordinate the different microservice units and ensure that the application functionalities are delivered to the user. A multitude of approaches has been proposed for the various tasks in microservices-based systems. However, since the field is relatively young, there is a need to organize the different research works. In this study, we present a comprehensive review of the research approaches directed toward microservice architectures and propose a multilevel taxonomy to categorize the existing research. The study also discusses the different distributed computing paradigms employing microservices and identifies the open research challenges in the domain.

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Section: Taxonomy Based On Different Aspects Of Msasmentioning

confidence: 99%

Straddling the crevasse: A review of microservice software architecture foundations and recent advancements

Joseph

Chandrasekaran

2019

Softw Pract Exp

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“…In an application split into several microservices, the authors pointed out that it is difficult to detect the cause of a failure at run time. In [16], the authors provide a model for guiding decisions with a view to selecting the best monitoring system for a specific microservice architecture. Dealing with the development of applications requiring the interaction with different providers increases the complexity of managing the application itself.…”

Section: Related Workmentioning

confidence: 99%

Monitoring-Aware Optimal Deployment for Applications Based on Microservices

Fadda

Plebani

Vitali

2021

IEEE Trans. Serv. Comput.

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Modern cloud applications are required to be distributed, scalable, and reliable. The microservice architectural style enables developers to achieve this goal with reduced effort. Nonetheless, microservices deployment is not trivial due to the heterogeneity of the microservices in terms of both functional and non-functional requirements. This is also true when considering the monitoring requirements that are specific to each microservice and must be satisfied in order to enable the verification of the application objectives satisfaction. However, not all providers offer the same set of metrics with the same quality. The goal of this paper is to provide an approach for supporting the deployment of microservices in multi-cloud environments focusing on the Quality of Monitoring. Adopting a multi-objective mixed integer linear optimisation problem, our approach supports the application owner in finding the optimal deployment for satisfying all the constraints and maximising the quality of monitored data, while minimising the costs. To this end, a knowledge base is introduced to mediate between the perspectives of the cloud provider and the application owner, while a Bayesian Network is adopted to enhance the provider's monitoring capabilities by estimating metrics requested by the application owners that the cloud provider is not able to monitor.

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“…The monitoring metrics divided into platform/host (CPU, RAM, threads and database connections) and application metrics (service availability, service and API endpoint latency, success of API endpoints, API endpoint response times, API request clients, errors and exceptions) should be collected at each stage of the deployment pipeline. [57] identifies four areas for microservice monitoring based on monitoring activities of information generation, processing, dissemination and presentation: Generation and collection of monitoring data, storage, hosting and distribution of monitoring data, processing of the data to obtain platform and application metrics and presentation of needto-know information via a dashboard to the relevant stakeholders. In addition, a real-time monitoring component of a production-ready microservices application to detect current and imminent failures due to changes in key metrics.…”

Section: : Platform Monitoring and Managementmentioning

confidence: 99%

Crane Cloud: A Resilient Multi-Cloud Service Layer for Resource Constrained Settings

Mwotil¹

2021

Preprint

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Whereas the main cloud providers have set up cloud services on stable infrastructure, developers and users situated in low-resource settings access cloud services and platforms using low-end computing devices that often connect to the Internet via slow mobile connections. These settings require custom software abstraction layers that consider such bandwidth constraints and intermittent connections as a rule rather than the exception. In this paper, we identify key challenges for developing for and accessing cloud services in resource constrained settings, namely, (1) Frequent Internet partitions and bandwidth constraints, (2) Data jurisdiction restrictions, (3) Vendor lock-in, and (4) Poor quality of service. To address these challenges, we propose a set of design considerations and properties for a resilient multi-cloud service layer, that includes: (1) Containerisation and orchestration of applications, (2) Service scheduling and replication, (3) Portability and multi-cloud migration, (4) Resilience to network partitions and bandwidth constraints, (5) Automated service discovery and load balancing, (6) Localised image registry, and (7) Support for platform monitoring and management. We present a prototype validation case study, Crane Cloud, an open source multi-cloud service abstraction layer built on-top of Kubernetes that is designed with inherent support for resilience to network partitions, microservice orchestration (deployment, scaling and management of containerized applications)a localized image registry, support for migration of services between private and public clouds to avoid vendor lock-in issues and platform monitoring. We evaluate the performance and user experience of Crane Cloud by implementing and deploying a computational and bandwidth intensive machine learning system shows lower response time compared when hosted on other public clouds.

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Decision Guidance Models for Microservice Monitoring

Cited by 36 publications

References 9 publications

Straddling the crevasse: A review of microservice software architecture foundations and recent advancements

Straddling the crevasse: A review of microservice software architecture foundations and recent advancements

Monitoring-Aware Optimal Deployment for Applications Based on Microservices

Crane Cloud: A Resilient Multi-Cloud Service Layer for Resource Constrained Settings

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