HANSEL: Adaptive horizontal scaling of microservices using Bi-LSTM

Yan, Ming; Liang, Xiaomeng; Lu, Zhihui; Wu, Jie; Zhang, Wei

doi:10.1016/j.asoc.2021.107216

Cited by 42 publications

(38 citation statements)

References 11 publications

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“…Furthermore, the BT model is responsible for the prediction of long-term QoS violations. To further improve the speed and eiciency of RL-based scaling approaches for microservices under hybrid cloud environments, Yan et al [78] developed a multi-agent parallel training module based on SARSA and improved the horizontal scaling policy of Kubernetes, supported by the microservice workload prediction results generated by Bi-LSTM.…”

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

confidence: 99%

“…Bi-LSTM models can overcome this limitation by processing the data sequence from both forward and backward directions. Therefore, Bi-LSTM models are proposed in References [71,78] to capture more key metrics and improve the prediction accuracy.…”

Section: Workload Characterizationmentioning

confidence: 99%

“…For example, given one research work aims to predict resource demand with low error rates, it can utilize GRU-based approach in [58] and it can improve the existing work by using more comprehensive application models. If another work plans to forecast requests arrival rate with high accuracy, the Bi-LSTM approaches in [71] and [78] can be the candidates. However, the researchers need to overcome the inaccuracy in long-term prediction.…”

Section: Workload Characterizationmentioning

confidence: 99%

“…The Actor-Critic model produces a horizontal scaling decision to minimize the SLO violations through evaluating three crucial features, including SLO maintenance ratio, workload changes, and request composition. To speed up the model training process of RL methods, Yan et al [78] design a multi-agent parallel training model based on SARSA for horizontal scaling of microservices under hybrid clouds. Assisted with the workload prediction results generated by Bi-LSTM , their elastic scaling approach could make a more accurate scaling decision of when, where, and how many microservice instances should be scaled up/down.…”

Section: Scalingmentioning

confidence: 99%

See 3 more Smart Citations

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

confidence: 99%

Section: Workload Characterizationmentioning

confidence: 99%

Section: Workload Characterizationmentioning

confidence: 99%

Section: Scalingmentioning

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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“…Ming Yan et al [23] presented a fusion elastic scaling strategy for Kubernetes (k8's) that combined reactive and proactive approaches. The proactive technique uses the Bi-LSTM model to learn the physical host and pod resource consumption history in order to anticipate future workload (Memory usage, CPU utilization).…”

Section: Literature Reviewmentioning

confidence: 99%

Predictive Scaling for Elastic Compute Resources on Public Cloud Utilizing Deep Learning based Long Short-term Memory

Bharanidharan¹,

Jayalakshmi²

2021

IJACSA

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A review of microservices autoscaling with formal verification perspective

2022

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The process of scaling microservices is a challenging task, especially in maintaining optimum resource provisioning while respecting QoS constraints and SLA.Many research works have proposed autoscaling approaches for microservices, however, less likely concerned with the correctness guarantee of the proposed algorithms. Hence, it is significant to gather and summarize these approaches to foster future innovation. Meanwhile, a few reviews have been published concerning microservices from different aspects. Therefore, our review complements the existing by focusing on autoscaling with verification perspectives. This study highlights the recent contributions in three inter-related main topics that were published within the year 2017 to 2022, namely, microservice, verification, and autoscaling. Due to limited resources on verification for microservice autoscaling, we widen the perspective by considering the verification for autoscaling in cloud-based systems. Based on our findings, we found that the formal method is not a new thing in verifying the autoscaling policies in cloud-based systems, and one recent study that implements the formal method in the microservices area has been identified. Apart from the autoscaling techniques, we have also determined several factors that have been a concern in scaling the microservices as well as the relatable metrics. Meanwhile, from a verification perspective, we identified that probabilistic model checking is the common formal verification technique used to verify microservices and cloud autoscaling. Finally, we recommend open challenges from two perspectives which highlight the verification for existing microservice autoscaling and verification for ML-based microservice autoscaling.

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HANSEL: Adaptive horizontal scaling of microservices using Bi-LSTM

Cited by 42 publications

References 11 publications

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

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

Predictive Scaling for Elastic Compute Resources on Public Cloud Utilizing Deep Learning based Long Short-term Memory

A review of microservices autoscaling with formal verification perspective

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