Machine Learning-based Orchestration of Containers: A Taxonomy and Future Directions
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.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningManagementMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration
Future AI applications require performance, reliability and privacy that the existing, cloud-dependant system architectures cannot provide. In this article, we study orchestration in the device-edge-cloud continuum, and …
Cloud ComputingFrom Standalone LLMs to Integrated Intelligence: A Survey of Compound Al Systems
Compound Al Systems (CAIS) is an emerging paradigm that integrates large language models (LLMs) with external components, such as retrievers, agents, tools, and orchestrators, to overcome the limitations of standalone mo…
BenchmarkingRAGRetrieval-augmented GenerationSurveyLarge Language Models Meet Text-Attributed Graphs: A Survey of Integration Frameworks and Applications
Large Language Models (LLMs) have achieved remarkable success in natural language processing through strong semantic understanding and generation. However, their black-box nature limits structured and multi-hop reasoning…
parameter-efficient fine-tuningRepresentation LearningRecommendation SystemsQuestion AnsweringLLM-Based Data Science Agents: A Survey of Capabilities, Challenges, and Future Directions
Recent advances in large language models (LLMs) have enabled a new class of AI agents that automate multiple stages of the data science workflow by integrating planning, tool use, and multimodal reasoning across text, co…
Multimodal ReasoningFeature EngineeringOSI Stack Redesign for Quantum Networks: Requirements, Technologies, Challenges, and Future Directions
Quantum communication is poised to become a foundational element of next-generation networking, offering transformative capabilities in security, entanglement-based connectivity, and computational offloading. However, th…