Heterogeneous Task Co-location in Containerized Cloud Computing Environments

Zhong, Zhiheng; He, Jiabo; Rodríguez, María Alejandra; Erfani, Sarah M.; Ramamohanarao, Kotagiri; Buyya, Rajkumar

doi:10.1109/isorc49007.2020.00021

Cited by 18 publications

(13 citation statements)

References 34 publications

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“…Considering the increasingly diverse and dynamic cloud workloads processed by existing cloud service providers, it is still unclear how to automate the orchestration process for complex heterogeneous workloads under largescale cloud computing systems [8,9]. Note that containers bear a resemblance to VMs from the perspective of orchestration.…”

Section: Needs For Machine Learning-based Container Orchestrationmentioning

confidence: 99%

“…Co-located applications could compete for shared resources, which may cause application performance downgrade, extra maintenance costs, and service-level agreement (SLA) violations [3,4,8] (details and solutions are discussed in Section 4.2.3). (4) The dependency structures between containerized application components are not considered during resource provisioning.…”

Section: Needs For Machine Learning-based Container Orchestrationmentioning

confidence: 99%

“…Another direction in ML-based workload characterization is resource demand modelling and prediction. Zhong et al [8] leverage the K-means++ algorithm for task classiication and identiication based on the resource usage (e.g., CPU and memory) patterns of diferent workloads. This advantage of K-means based approach is scalable for multiple types of tasks, however, the accuracy is undermined under loads with high variance.…”

Section: Workload Characterizationmentioning

confidence: 99%

“…Most of the reviewed approaches follow a design pattern of combining ML-based Workload Modellers or Performance Analyzers with a heuristic scheduling Decision Maker, as discussed in Section 2.2.4. As the system complexity has been navigated by prediction models, bin packing and approximation algorithms such as best it or least it are commonly adopted to make scheduling decisions with improved resource utilization and energy eiciency [8,16,18,58,83,84]. For example, Venkateswaran and Sarkar [16] manage to signiicantly reduce the complexity of selecting the best-it container system and cluster compositions under multi-cloud environments, standing on their prediction model described in Section 4.2.2.…”

Section: Schedulingmentioning

confidence: 99%

“…Some reviewed approaches manage to bind ML-based behavior models with heuristic migration algorithms. Zhong et al [8] develop a least-it rescheduling algorithm to evict and relocate a set of lower-priority containers with the least QoS impact when unexpected resource contention occurs between co-located containers. The rescheduling algorithm readjusts the container coniguration based on runtime performance metrics and selects the node with the most available resources evaluated by K-means for relocation.…”

Section: Migrationmentioning

confidence: 99%

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

Zhong

Rodríguez

et al. 2022

ACM Comput. Surv.

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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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Section: Needs For Machine Learning-based Container Orchestrationmentioning

confidence: 99%