Developing Self-Adaptive Microservice Systems: Challenges and Directions

Mendonça, Nabor C.; Jamshidi, Pooyan; Garlan, David; Pahl, Claus

doi:10.1109/ms.2019.2955937

Cited by 42 publications

(16 citation statements)

References 12 publications

(31 reference statements)

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“…However, we aim to explore this in more concrete application settings. We consider here traffic management and coordinated cars as a concrete IoT and edge setting, where traffic and car movement is captured and processed, maybe combined by infotainment information with image and video data [6,16,20,25]. This mobile setting would need to be supported by local clusters that act autonomously, but require some higher-level coordination [7,14,27].…”

Section: Discussionmentioning

confidence: 99%

“…Full details of the second scenario are given in Table 4. Amount of time each user requests Randomized in range [1,20] Amount of CPU per user request Random in the interval [2,8] For this experiment, 100 input requests are considered. In this scenario, the number of user requests is considered to be fixed, but the number of edge layer nodes is considered to be 5, 10, 15, 20, 30 and 50.…”

Section: Scenario 2: Edge Node Variationmentioning

confidence: 99%

“…Table 5 shows the full configuration details of the third scenario. Amount of time each user requests Randomized in range [1,20] Amount of CPU per user request Random in the interval [2,8] According to Fig. 6, in different iterations we can see different results despite not changing the input values at each iteration.…”

Section: Scenario 3: Iteration Variationmentioning

confidence: 99%

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Performance Management in Clustered Edge Architectures Using Particle Swarm Optimization

Azimi

Pahl

Shirvani

2021

Communications in Computer and Information Science

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Recently distributed computing capacities are brought to the edge of the Internet, permitting Internet-of-Things applications to process calculation all the more locally and subsequently more productively and this has brought a totally different scope of apparatuses and usefulness. This instrument can The most significant characterizing highlights of edge processing are low latency, location awareness, wide geographic distribution, versatility, support for countless nodes, etc. We want to likely limit the latency and delay in edge-based structures. We center around a progressed compositional setting that considers communication and processing delays and the management effort notwithstanding a real request execution time in an operational efficiency situation. Our design is based on multi-cluster edge layer with nearby autonomous edge node clusters. We will contend that particle swarm optimization as a bio-motivated optimization approach is a perfect candidate for distributed IoT load handling in self-managed edge clusters. By designing a controller and utilizing a particle swarm optimization algorithm, we show that delay and end-to-end latency can be reduced.

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Section: Discussionmentioning

confidence: 99%

Section: Scenario 2: Edge Node Variationmentioning

confidence: 99%

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Performance Management in Clustered Edge Architectures Using Particle Swarm Optimization

Azimi

Pahl

Shirvani

2021

Communications in Computer and Information Science

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“…Orchestration platforms like Kubernetes introduce functionalities that contribute to the autoscaling and self-healing of microservices, thus contributing to the reliability of services. 27 Despite the easing of microservices operations with Kubernetes, the setup can be hard, time consuming and be highly dependent of the cloud infrastructure. In this regard, platforms like KubeNow 28 aim to enable easy and fast deployment of microservices using Docker and Kubernetes in a platform agnostic fashion (e.g., Amazon Web Services and Google Cloud Platform).…”

Section: Microservices and Orchestration Platformsmentioning

confidence: 99%

“…Orchestration platforms like Kubernetes introduce functionalities that contribute to the autoscaling and self‐healing of microservices, thus contributing to the reliability of services 27 . Despite the easing of microservices operations with Kubernetes, the setup can be hard, time consuming and be highly dependent of the cloud infrastructure.…”

Section: Related Workmentioning

confidence: 99%

Next generation of microservices for the 5G Service‐Based Architecture

et al. 2020

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The architecture for 5G core includes a Service-Based Architecture for the diverse network functions (NFs), which relies on HTTP/2 for the SBI and TCP as the underlying transport protocol. The specifications of the HTTP family is moving towards more efficient and secure protocols, which are based on UDP to assure enhanced transport but using TLS to secure the communication channel. The next generation of microservices needs to be more secure, performant and easily manageable, where HTTP/3 and containers orchestration platforms (like Kubernetes) can provide significative contributions towards such goals. Different deployment approaches can be followed for services implemented in compliance to the 5G SBA. This paper contributes with the assessment of deployment models for services in 5G networks, where NFs are implemented following traditional architectures (all the functions in a VM) or as serverless architectures (with functions distributed in containers). The performance of microservices in Kubernetes is also evaluated. The evaluation conducted also considers the employment of different versions of HTTPs to empower the service-based interfaces of 5G services. Results demonstrate performance benefits of employing HTTP/3, based on QUIC protocol, in scenarios with networks characterised by losses or delay conditions. Despite such gain, deployment in 5G networks needs to carefully consider aspects related with connection tracking mechanisms to support high volumes of requests.

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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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Developing Self-Adaptive Microservice Systems: Challenges and Directions

Cited by 42 publications

References 12 publications

Performance Management in Clustered Edge Architectures Using Particle Swarm Optimization

Performance Management in Clustered Edge Architectures Using Particle Swarm Optimization

Next generation of microservices for the 5G Service‐Based Architecture

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

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