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

Zhiheng Zhong, Minxian Xu, Maria Alejandra Rodriguez, Chengzhong Xu, Rajkumar Buyya

arXiv:2106.12739v1cs.LGcs.DC

TL;DR

Container orchestration must manage complex, dynamic workloads across increasingly heterogeneous cloud environments, while existing approaches do not yet provide a systematic full-process solution. This paper conducts a comprehensive review and taxonomy of machine learning-based orchestration methods, examining their evolution, applications, and open challenges. The reviewed literature spans workload modelling, resource provisioning, scaling, and diverse application architectures, but a complete machine learning-based optimization framework remains absent.

  • Problem

    Dynamic and heterogeneous cloud workloads make orchestration complex, while existing solutions do not systematically address the complete orchestration process.

  • Method

    The paper conducts a comprehensive literature review and taxonomy of machine learning-based container orchestration approaches across application architectures and cloud infrastructures.

  • Results

    Reviewed approaches apply diverse machine learning methods to workload characterization, resource provisioning, scaling, and management of microservice and serverless applications.

  • Takeaways & Limitations

    The field is moving toward hybrid machine learning pipelines that combine behavior modelling with resource provisioning and decision-making methods.

  • Takeaways & Limitations

    No systematic approach yet builds a complete machine learning-based optimization framework for the whole orchestration process.

Abstract

from arXiv · show

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.

1 INTRODUCTION

Container orchestration automates the management of increasingly diverse containerized workloads, but static heuristics struggle with dynamic, large-scale, heterogeneous environments and application-level QoS requirements. This paper reviews and classifies machine learning-based orchestration approaches to characterize the field, identify limitations, and outline future directions.

  • 1 INTRODUCTION: Container orchestration automates resource allocation, deployment, autoscaling, monitoring, migration, load balancing, security, and networking for containerized applications.Its effectiveness affects resource utilization, energy efficiency, and application performance in large cloud infrastructures.
  • 1 INTRODUCTION: Static heuristic policies suit predefined workloads but cannot adapt effectively to highly dynamic runtime scaling demands.Threshold-based autoscaling is given as an example of a policy configured for limited workload scenarios.
  • 1 INTRODUCTION: Heuristic performance can degrade at scale, with bin-packing methods causing high task-scheduling delays in large compute clusters.Best-fit and least-fit algorithms are cited as common examples for scheduling and resource allocation.
  • 1 INTRODUCTION: Existing methods often overlook contention, application dependencies, and application-level QoS metrics such as deployment, completion, and communication delays.These omissions can contribute to performance degradation, maintenance costs, SLA violations, and SLO violations in containerized applications.
  • 1 INTRODUCTION: The paper reviews machine learning-based container orchestration, proposes a taxonomy, classifies approaches by key characteristics, and identifies future research directions.It extends prior surveys by focusing on machine learning for complex orchestration problems, including multidimensional workload characterization and autoscaling in hybrid clouds.

2 BACKGROUND

This section introduces machine learning-based container orchestration as a framework for modelling workloads and predicting system behavior to support automated resource decisions. It also outlines orchestration functions, common ML model categories, and the cloud environments and workloads they must manage.

  • Machine Learning: Regression, classification, decision-making, and time-series models organize ML approaches by their roles in performance analysis, anomaly detection, provisioning decisions, and forecasting.Examples include support vector regression, K-means, reinforcement learning, and ARIMA or recurrent neural network models.
  • Container Orchestration: Container orchestration automates resource allocation, deployment, autoscaling, health monitoring, migration, load balancing, security, and network configuration.These functions support management of containerized applications across cloud environments.
  • Orchestration Functions: Orchestration must assign, schedule, scale, migrate, monitor, balance, and replicate containers while accounting for multidimensional workload requirements.The reviewed functions include resource and application monitoring, load distribution, and fault tolerance.
  • Deployment Environments: Container orchestration spans private, public, fog, and edge infrastructures, where moving computation toward users can reduce delay, communication costs, bandwidth use, and energy consumption.Containers can be deployed across multiple infrastructure types because of their portability and lightweight nature.
  • Machine Learning-based Orchestration: Machine learning-based orchestration uses monitoring data and system logs to model workloads and predict future resource provisioning decisions.Its optimization engine may be integrated with or independent from the Container Orchestrator.

3 TAXONOMY OF MACHINE LEARNING-BASED CONTAINER ORCHESTRATION TECHNOLOGIES

The paper’s taxonomy classifies machine learning-based container orchestration research by application architecture, infrastructure, objectives, and behavior modelling or prediction. These dimensions organize how reviewed approaches characterize systems and forecast future behavior.

  • Taxonomy Dimensions: The taxonomy classifies reviewed approaches by application architecture, infrastructure, optimization objectives, and behavior modelling and prediction.Application architecture concerns component structure, infrastructure concerns operating environments, and objectives concern intended improvements.

3.1 Application Architecture

The taxonomy distinguishes monolithic, microservice, and serverless application architectures. Each architecture creates different orchestration requirements for deployment, resource management, dependencies, execution, and service-level assurance.

  • Monolithic: Monolithic applications package all functional modules into one deployment unit, which can be easy to develop and maintain at small scale.The passage describes the unit as one container.
  • Microservice: Microservices split large applications into loosely coupled, independently deployable components that collaborate through lightweight communication.Their growing component count and dynamic interdependencies increase infrastructure-level load-distribution and resource-management challenges.
  • Serverless: Serverless applications use event-driven, stateless functions with limited execution times, while platforms manage provisioning, energy, SLA assurance, and security.Function chains connect individual functions through centralized messaging services.
  • Serverless: Serverless orchestration manages function-chain initialization, execution, transitions, and data-flow control.The passage identifies function initialization overhead as a performance concern.

3.2 Infrastructure

The infrastructure taxonomy covers single-cloud, multi-cloud, and hybrid-cloud environments. These settings differ in provider composition, resource choices, and proximity of computation to end users.

  • Single Cloud: Single-cloud environments host applications using resources from one private or public cloud service provider.All applications are served from that provider’s resources.
  • Multi-Cloud: Multi-cloud environments combine services from multiple providers, creating choices across resource configurations, prices, network latency, and geographic locations.The participating services may be private clouds, public clouds, or a mixture of both.
  • Hybrid Cloud: Hybrid-cloud environments combine private or public clouds with fog or edge devices to process applications and data closer to end users.This arrangement addresses transmission time and network latency between users and cloud servers.

3.3 Objectives

The reviewed orchestration approaches optimize four major objective categories: resource efficiency, energy efficiency, cost efficiency, and SLA assurance. Because orchestration commonly pursues multiple objectives, solutions must balance their trade-offs.

  • Orchestration solutions typically optimize multiple objectives simultaneously, requiring trade-offs among the selected metrics.
  • Resource Efficiency: Resource efficiency uses infrastructure-level metrics to model resource demand and relate resource usage to application performance.These insights support provisioning decisions intended to improve resource efficiency and application performance.
  • Energy Efficiency: Energy efficiency targets lower electricity consumption by adjusting physical-machine resource utilization.
  • Cost Efficiency: Cost efficiency selects cloud services by pricing and computing capability while satisfying users’ QoS requirements.
  • SLA Assurance: SLA assurance targets application constraints such as response time, initialization time, completion time, and throughput.Autoscaling is commonly used to maintain SLA assurance under changing workloads.

3.4 Behavior Modelling and Prediction

Behavior modelling and prediction analyzes multi-layer workload, application, and infrastructure metrics to support orchestration optimization. The taxonomy covers workload characterization, performance analysis, anomaly detection, and dependency analysis.

  • Behavior modelling analyzes application- and infrastructure-level metrics to produce predictions useful for orchestration objectives.
  • Workload characterization: Workload characterization captures differing task structures, resource demands, arrival rates, and distributed locations in containerized applications.Workload knowledge supports more precise resource provisioning decisions.
  • Performance analysis: Performance analysis relates infrastructure metrics such as resource utilization and energy consumption to application metrics such as response time, execution time, and throughput.These relations help manage trade-offs among optimization objectives.
  • Anomaly detection: Anomaly detection identifies security threats, instance failures, workload spikes, performance degradation, and resource overloading.Fast root-cause localization can prevent SLA violations or system crashes.
  • Dependency analysis: Dependency analysis models graph-based relationships among application components to configure resources for dynamically changing dependencies.Incremental models are needed to address chain reactions from individual components to overall application performance.

3.5 Resource Provisioning

Resource provisioning for containerized applications must account for workload diversity, heterogeneous resources, and internal application structures. The taxonomy organizes provisioning around scheduling, scaling, migration, and computation offloading.

  • Resource provisioning strategies use behavior modelling and prediction to address diverse workloads, heterogeneous resources, and complex application structures.The stated goals include higher accuracy and shorter computation delays.
  • Scheduling: Scheduling places task units, and its decision quality directly affects overall application performance and resource efficiency.Task units may be single tasks or independent or dependent task groups.
  • Scaling: Scaling adjusts application or node size to handle workload fluctuations and reduce SLA violations.Horizontal scaling changes replica or node counts, vertical scaling changes assigned resources, and hybrid scaling combines both.
  • Migration: Migration relocates tasks for load balancing or moves computation-intensive tasks across hybrid clouds when resource bottlenecks occur.The latter is described as computation offloading.

3.6 Evolution of Machine Learning-based Container Orchestration technologies

From 2016 to 2021, machine-learning methods in container orchestration expanded from simple resource prediction toward integrated modelling and reinforcement-learning-based decisions. Recent work increasingly combines existing models into hybrid orchestration pipelines.

  • 2016 approaches used ARIMA and nearest-neighbor models for resource-utilization prediction with simple infrastructure-level time-series inputs.
  • 2017–2018 studies expanded to microservice dependency analysis, anomaly detection, workload prediction, performance analysis, and graph-based task scheduling.Reported models included LSTM, GBR, Bi-LSTM, regression methods, KNN, SVM, Naive Bayes, random forest, and SARSA.
  • Reinforcement learning and deep reinforcement learning were applied to vertical scaling, task scheduling, and computation offloading, targeting resource, time, energy, or cost objectives.
  • 2019 work used GRU to reduce computational costs and error rates in resource-usage prediction relative to more complex LSTM models.
  • 2020–2021 hybrid approaches combined dependency or performance models with reinforcement learning, boosted trees, or convolutional neural networks for scaling and QoS prediction.
  • Hybrid ML-based solutions combine multiple existing methods into orchestration pipelines spanning multidimensional behavior modelling and resource provisioning.The review associates this trend with improving prediction accuracy and computational efficiency.

4 STATE-OF-THE-ART IN MACHINE LEARNING FOR ORCHESTRATION OF CONTAINERS

The reviewed approaches use machine learning to characterize workloads, analyze performance, detect anomalies, and support container scaling. Most workload and performance studies predict request rates, resource usage, time constraints, or related metrics, while scaling research increasingly addresses dynamic microservice workloads.

  • 4.1.1 Workload Characterization: Most workload-characterization studies predict request arrival rates and resource-usage patterns using time-series analysis or regression models.Approaches include ARIMA, LSTM, Bi-LSTM, GRU, clustering, nearest-neighbor regression, and hybrid models.
  • 4.1.2 Performance Analysis: Most performance-analysis studies predict time constraints or resource-usage patterns through regression and time-series techniques.Targets include serverless cost, end-to-end latency, cold-start latency, CPU utilization, response time, and microservice tail latency.
  • 4.1.3 Anomaly Detection: Reviewed anomaly-detection approaches identify abnormal system, application, or security behavior using clustering, ensemble learning, and incremental SVM models.Applications include vulnerability detection, host-load classification, and identifying microservices at risk of SLO violations.
  • 4.2.2 Scaling: Scaling research covers diverse application architectures, with model-free reinforcement learning increasingly used for dynamically changing workloads and microservice dependencies.Serverless scaling approaches predict invocation behavior or use Q-Learning to provision function containers and reduce cold-start effects.

4.3 Summary

The literature is concentrated on microservice applications and single-cloud deployments, while serverless studies remain less numerous but are growing. Hybrid machine-learning models also become more prevalent as containerized applications grow more complex.

  • 4.3.1 Application Architecture: 68% of reviewed papers address modelling and management of microservice-based applications.These studies combine diverse ML algorithms with heuristic or model-free reinforcement-learning methods for resource utilization, cost reduction, and SLA assurance.
  • 4.3.1 Application Architecture: Single-component applications account for 22% of studies and mainly use workload prediction with heuristic or reinforcement-learning methods for resource allocation.The stated optimization targets include cost, energy, and resource efficiency.
  • 4.3.1 Application Architecture: Serverless applications account for 10% of studies, with existing work primarily addressing function cold starts through invocation-time prediction and container scaling.LSTM, GBR, heuristic methods, and Q-Learning are reported across these approaches.
  • 4.3.2 Cloud Infrastructure: 71% of included studies consider deployment under single-cloud environments, whereas 5% investigate geographic-distributed multi-cloud behavior modelling and scaling.Multi-cloud interactions are described as time-consuming and computation-intensive.
  • 4.3.3 Hybrid Machine Learning Model: The distribution of hybrid ML models rises between 2016 and 2021 as containerized applications become more complex and dynamic.By 2018, only single ML models had been adopted in the field.

5 FUTURE DIRECTIONS

The paper identifies open challenges in modelling microservice workloads and dependencies, scheduling complex jobs, managing serverless and hybrid-cloud applications, and optimizing multiple objectives. It proposes more integrated, distributed, and energy-aware ML orchestration frameworks.

  • (1) Workload Distribution in Microservices: Workload distribution across microservice units remains insufficiently studied, despite its potential chain reactions for overall application performance.The paper calls for standardized workload-distribution models supporting load balancing and performance optimization.
  • (2) Microservice Dependency Analysis: Existing dependency analysis does not explicitly relate individual microservice status to overall application performance metrics.Such models are identified as necessary for scheduling and scaling with performance optimization and SLA assurance.
  • (3) Systematic Anomaly Detection and Recovery: The paper calls for systematic anomaly detection, root-cause categorization, and timely recovery across application architectures and cloud infrastructures.Current methods mainly use resource, performance, or security-threat signals.
  • (4) Graph-based Task Scheduling: Scheduling methods for heterogeneous DAG jobs remain limited because existing work often assumes homogeneous task structures.The paper identifies graph-based scheduling as an open direction for more realistic dependent-task workloads.
  • (5) Management of Function Chains in Serverless Architectures: Serverless research should extend beyond cold-start mitigation toward workload classification, resource-demand estimation, autoscaling, and SLO- and cost-aware function-chain optimization.The paper highlights hybrid-cloud deployment as an especially important setting for function chains.
  • (6) Microservices in Hybrid Clouds: Hybrid-cloud microservice orchestration must account for heterogeneous, geographically distributed resources and the long-term effects of placement and adjustment decisions.The paper also highlights energy-aware management, multi-dimensional performance benchmarks, fully integrated ML engines, and decentralized edge intelligence.

6 CONCLUSION

The paper reviews machine-learning approaches for container orchestration across application architectures and cloud infrastructures. It concludes that ML-based models can improve orchestration decisions in complex environments, while a complete end-to-end ML optimization framework remains absent.

  • 6 CONCLUSION: The review organizes machine-learning-based container orchestration research by application architectures and cloud infrastructures.It covers ML use for behavior modelling and resource provisioning in decentralized containerized applications.
  • 6 CONCLUSION: Compared with traditional heuristic methods, ML-based models could produce more accurate orchestration decisions with shorter computation delays in complex, heterogeneous, and geographically distributed environments.This conclusion is stated within the scope of the reviewed literature.
  • 6 CONCLUSION: No systematic approach yet builds a complete ML-based optimization framework responsible for the whole orchestration process.The paper identifies such frameworks as necessary for growing microservice and serverless complexity in hybrid clouds and for balancing multiple objectives.
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