High Resource Utilization Auto-Scaling Algorithms for Heterogeneous Container Configurations

Cheng, Yuxiang; Lin, Ching-Chi; Liu, Pangfeng; Wu, Jan‐Jan

doi:10.1109/icpads.2017.00030

“…Furthermore, anomaly detection was built on top of the LSTM model for the identiication of abnormal behaviors in resource utilization or application performance. Besides, Cheng et al [70] used Gradient Boosting Regression (GBR) that can ensemble multiple weak prediction models (e.g. regression trees) to form a more powerful model, and applied GBR for resource demand prediction in workload characterization.…”

Section: Evolution Of Machine Learning-based Container Orchestration ...mentioning

confidence: 99%

“…Plenty of previous studies [66,67,70,74,81] have tried to leverage heuristic methods for microservice scaling, assisted by ML-based workload modelling and performance analysis. However, such approaches underestimate the inter-dependencies between microservices that are updated dynamically.…”

Section: Scalingmentioning

confidence: 99%

“…7a, most of the reviewed papers (68%) are concerned with modelling and management of microservice-based applications. In light of the dynamic and decentralized nature of microservices, diverse ML algorithms have been investigated for capturing the key characteristic of microservice workloads, performance, and inter-dependencies, such as BGR, LSTM, GRU, SVM, GP, ANN, CNN, and DT [23,69,70,81,82,85,86]. As the inter-dependencies of microservice units could dynamically update at runtime, the key concern of microservice resource provisioning is the chain reactions caused by microservice unit scaling operations.…”

Section: Application Architecturementioning

confidence: 99%

“…As the inter-dependencies of microservice units could dynamically update at runtime, the key concern of microservice resource provisioning is the chain reactions caused by microservice unit scaling operations. By combining MLbased behavior models with heuristic or model-free RL methods for scaling, the most recent studies manage to achieve high resource utilization optimization, cost reduction, and SLA assurance [24,66,67,70,74,78,81].…”

Section: Application Architecturementioning

confidence: 99%

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“…Furthermore, anomaly detection was built on top of the LSTM model for the identification of abnormal behaviors in resource utilization or application performance. Besides, Cheng et al [66] used Gradient Boosting Regression (GBR) for resource demand prediction in workload characterization.…”

Section: Evolution Of Machine Learning-based Container Orchestration ...mentioning

confidence: 99%

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Containerization is a lightweight application virtualization technology, providing high environmental consistency, operating system distribution portability, and resource isolation. Existing mainstream cloud service providers have prevalently adopted container technologies in their distributed system infrastructures for automated application management. To handle the automation of deployment, maintenance, autoscaling, and networking of containerized applications, container orchestration is proposed as an essential research problem. However, the highly dynamic and diverse feature of cloud workloads and environments considerably raises the complexity of orchestration mechanisms. Machine learning algorithms are accordingly employed by container orchestration systems for behavior modelling and prediction of multi-dimensional performance metrics. Such insights could further improve the quality of resource provisioning decisions in response to the changing workloads under complex environments. In this paper, we present a comprehensive literature review of existing machine learning-based container orchestration approaches. Detailed taxonomies are proposed to classify the current researches by their common features. Moreover, the evolution of machine learning-based container orchestration technologies from the year 2016 to 2021 has been designed based on objectives and metrics. A comparative analysis of the reviewed techniques is conducted according to the proposed taxonomies, with emphasis on their key characteristics. Finally, various open research challenges and potential future directions are highlighted.

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High Resource Utilization Auto-Scaling Algorithms for Heterogeneous Container Configurations

Cited by 10 publications

References 7 publications

Machine Learning-based Orchestration of Containers: A Taxonomy and Future Directions

Machine Learning-based Orchestration of Containers: A Taxonomy and Future Directions

Machine Learning-based Orchestration of Containers: A Taxonomy and Future Directions

Reinforcement Learning Based Vertical Scaling for Hybrid Deployment in Cloud Computing

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