2021
DOI: 10.1109/ms.2019.2955937
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Developing Self-Adaptive Microservice Systems: Challenges and Directions

Abstract: A self-adaptive system can dynamically monitor and adapt its behavior to preserve or enhance its quality attributes under uncertain operating conditions. This article identifies key challenges for the development of microservice applications as self-adaptive systems, using a cloud-based intelligent video surveillance application as a motivating example. It also suggests potential new directions for addressing most of the identified challenges by leveraging existing microservice practices and technologies.

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Cited by 49 publications
(18 citation 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%
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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%
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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%