A platform to enable self-adaptive cloud applications using trustworthiness properties

Pereira, Jose D'Abruzzo; Silva, Rui; Antunes, Nuno; Silva, Jorge L. M.; França, Breno Bernard Nicolau de; Moraes, Regina; Vieira, Marco

doi:10.1145/3387939.3391608

Cited by 12 publications

(4 citation statements)

References 10 publications

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“…The key idea being promoted is to use an ensemble of micro-controllers instead of a monolithic controller, like an implementation of a MAPE-K loop [14,20]. These micro-controllers would not be restricted to the implementation of services provided by the distinct stages of a MAPE-K loop [29]. Instead, the micro-controllers would be associated with specific services that make up the individual stages of a controller [12].…”

Section: A Micro-controllersmentioning

confidence: 99%

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Design and Evaluation of Controllers based on Microservices

Siqueira

Ferrari

Lemos

2023

2023 IEEE/ACM 18th Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)

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In self-adaptive software systems, generic controllers can be configured parametrically according to system needs, even though their reuse is restricted because of the wide range of services that can be provided by each of the stages of a feedback control loop, like MAPE-K. Rainbow is a typical example of such a generic, monolithic controller. This paper advocates controllers that are structurally flexible, and which are composed from micro-controllers, each providing specific services (e.g., based on microservices). To provide evidence on the feasibility of our approach, we compare three different architectural configurations for the controller: monolithic, decentralised, and decentralised with a meta-controller. Results from our experiments indicate that even though the decentralised configuration with a meta-controller demanded more computational resources, it performed comparatively well when compared to the other configurations. We conclude that a multi-layered controller design, based on micro-controllers, provides the basis for defining structurally flexible controllers at operational-time, and may promote reuse at development-time.

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Section: A Micro-controllersmentioning

confidence: 99%

“…Pereira et al [29] proposed the development of flexible controllers that are based on MAPE-K (each sate as a single microservice), whereas our approach is not limited to the MAPE-K components. We also include the meta-controller as an additional layer, which is able to reconfigure the controller.…”

Section: Related Workmentioning

confidence: 99%

Design and Evaluation of Controllers based on Microservices

Siqueira

Ferrari

Lemos

2023

2023 IEEE/ACM 18th Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)

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“…In this regard, inducing microservices with the primitives of selfadaptivity is a strong candidate for addressing autonomic trade-offs [11]. Pereira et al [22] presented an adaptive system that designed the control loop components in the form of microservices that are easily deployed in a container-based system, such as Kubernetes. Another study [1] proposed an architecture-based self-adaptation service for cloud-native applications based on the Rainbow selfadaptation framework with support for Docker containers and Kubernetes.…”

Section: Related Workmentioning

confidence: 99%

Kuksa$$^{*}$$: Self-adaptive Microservices in Automotive Systems

Banijamali

Kuvaja

Oivo

et al. 2020

Product-Focused Software Process Improvement

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In pervasive dynamic environments, vehicles connect to other objects to send operational data and receive updates so that vehicular applications can provide services to users on demand. Automotive systems should be self-adaptive, thereby they can make real-time decisions based on changing operating conditions. Emerging modern solutions, such as microservices could improve self-adaptation capabilities and ensure higher levels of quality performance in many domains. We employed a real-world automotive platform called Eclipse Kuksa to propose a framework based on microservices architecture to enhance the selfadaptation capabilities of automotive systems for runtime data analysis. To evaluate the designed solution, we conducted an experiment in an automotive laboratory setting where our solution was implemented as a microservice-based adaptation engine and integrated with other Eclipse Kuksa components. The results of our study indicate the importance of design trade-offs for quality requirements' satisfaction levels of each microservices and the whole system for the optimal performance of an adaptive system at runtime.

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“…Cloud computing is the de facto standard platform for the global connectivity of actors (e.g., humans, robots, devices and sensors) and services, across many heterogeneous domains (e.g., mobile [1], health care [2], IoT [3] and telecommunication [4]). In this scenario, actors and services may have to frequently interact in ways that cannot be fully anticipated, demanding for strong configurability, adaptability and programmability requirements [5], [6], [7], [8], [9], [10].…”

Section: Introductionmentioning

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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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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A platform to enable self-adaptive cloud applications using trustworthiness properties

Cited by 12 publications

References 10 publications

Design and Evaluation of Controllers based on Microservices

Design and Evaluation of Controllers based on Microservices

Kuksa$$^{*}$$: Self-adaptive Microservices in Automotive Systems

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

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