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

Banijamali, Ahmad; Kuvaja, Pasi; Oivo, Markku; Jamshidi, Pooyan

doi:10.1007/978-3-030-64148-1_23

Cited by 7 publications

(5 citation statements)

References 27 publications

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“…Furthermore, an architecturebased self-adaptation framework with a MAPE-K feedback loop for a microservice as a managed system shows a reduced cost of ownership and faster self-adaptation [10]. On the other hand, the introduction of microservices architecture, as a managing system, can improve the self-adaptation capabilities of systems for various measures such as run-time data analysis [11]. Some challenges in developing a self-adaptive system based on microservices include developing monitoring and adaptation mechanisms for ensuring quality attributes; determining the level of distribution, observability, and granularity for deploying control components; and determining mechanisms for evaluation of the given quality attributes [12].…”

Section: A Self-adaptive System For Microservices Architecturementioning

confidence: 99%

CHESS: A Framework for Evaluation of Self-adaptive Systems based on Chaos Engineering

Malik,

Moonen

2023

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There is an increasing need to assess the correct behavior of self-adaptive and self-healing systems due to their adoption in critical and highly dynamic environments. However, there is a lack of systematic evaluation methods for self-adaptive and self-healing systems. We proposed CHESS, a novel approach to address this gap by evaluating self-adaptive and self-healing systems through fault injection based on chaos engineering (CE).The artifact presented in this paper provides an extensive overview of the use of CHESS through two microservice-based case studies: a smart office case study and an existing demo application called Yelb. It comes with a managing system service, a self-monitoring service, as well as five fault injection scenarios covering infrastructure faults and functional faults. Each of these components can be easily extended or replaced to adopt the CHESS approach to a new case study, help explore its promises and limitations, and identify directions for future research.

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Section: A Self-adaptive System For Microservices Architecturementioning

confidence: 99%

CHESS: A Framework for Evaluation of Self-adaptive Systems based on Chaos Engineering

Malik,

Moonen

2023

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“…Finally, we describe the environmental set up used for performing the experiments. The artefacts used in our research are available online 4 .…”

Section: Exploratory Studymentioning

confidence: 99%

“…(2) Flexible controllers and meta-controllers: Banijamali et al [4] emphasise the increasing interest in microservices in self-adaptive systems, in particular, in the automotive domain. In their approach, which is similar to ours, they consider a controller consisting of microservices.…”

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 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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“…• orchestration of complex services (the complexity of microservices applications is pushed from the components to the integration level [15], [23]); • maintaining data consistency and transaction management across microservices [15], [19], [32], [33]; • the difficulty in understanding the system holistically [7], [27]; • increased consumption of computing resources [7], [11], [17]. For small scale systems, these challenges may outweigh the benefits [27].…”

Section: A Motivation and Problem Statementmentioning

confidence: 99%

“…• Microservices are autonomous -they are self-contained and independently deployable services fully responsible for executing a given business [33], [35]- [37]. Because of their autonomy, they contain all dependencies such as: libraries, the execution environments -web servers and containers or virtual machines.…”

Section: B Microservice Architecturementioning

confidence: 99%

Monolithic vs. Microservice Architecture: A Performance and Scalability Evaluation

2022

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Context. Since its proclamation in 2012, microservices-based architecture has gained widespread popularity due to its advantages, such as improved availability, fault tolerance, and horizontal scalability, as well as greater software development agility. Motivation. Yet, refactoring a monolith to microservices by smaller businesses and expecting that the migration will bring benefits similar to those reported by top global companies, such as Netflix, Amazon, eBay, and Uber, might be an illusion. Indeed, for systems that do not have thousands of concurrent users and can be scaled vertically, the benefits of such migration have not been sufficiently investigated, while the existing evidence is inconsistent. Objective. The purpose of this paper is to compare the performance and scalability of monolithic and microservice architectures on a reference web application. Method. The application was implemented in four different versions, covering not only two different architectural styles (monolith vs. microservices) but also two different implementation technologies (Java vs. C# .NET). Next, we conducted a series of controlled experiments in three different deployment environments (local, Azure Spring Cloud, and Azure App Service). Findings. The key lessons learned are as follows: (1) on a single machine, a monolith performs better than its microservice-based counterpart; (2) The Java platform makes better use of powerful machines in case of computation-intensive services when compared to .NET; the technology platform effect is reversed when non-computationally intensive services are run on machines with low computational capacity; (3) vertical scaling is more cost-effective than horizontal scaling in the Azure cloud; (4) scaling out beyond a certain number of instances degrades the application performance; (5) implementation technology (either Java or C# .NET) does not have a noticeable impact on the scalability performance.

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

Cited by 7 publications

References 27 publications

CHESS: A Framework for Evaluation of Self-adaptive Systems based on Chaos Engineering

CHESS: A Framework for Evaluation of Self-adaptive Systems based on Chaos Engineering

Design and Evaluation of Controllers based on Microservices

Monolithic vs. Microservice Architecture: A Performance and Scalability Evaluation

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