Predictive auto-scaling with OpenStack Monasca

@article{Lanciano2021PredictiveAW,
  title={Predictive auto-scaling with OpenStack Monasca},
  author={Giacomo Lanciano and Filippo Galli and Tommaso Cucinotta and Davide Bacciu and Andrea Passarella},
  journal={Proceedings of the 14th IEEE/ACM International Conference on Utility and Cloud Computing},
  year={2021}
}
Cloud auto-scaling mechanisms are typically based on reactive automation rules that scale a cluster whenever some metric, e.g., the average CPU usage among instances, exceeds a predefined threshold. Tuning these rules becomes particularly cumbersome when scaling-up a cluster involves non-negligible times to bootstrap new instances, as it happens frequently in production cloud services. To deal with this problem, we propose an architecture for auto-scaling cloud services based on the status in… 

Figures and Tables from this paper

References

SHOWING 1-10 OF 42 REFERENCES
Efficient Autoscaling in the Cloud Using Predictive Models for Workload Forecasting
TLDR
A model-predictive algorithm for workload forecasting that is used for resource auto scaling is developed and empirical results are provided that demonstrate that resources can be allocated and deal located by the algorithm in a way that satisfies both the application QoS while keeping operational costs low.
Robust Resource Scaling of Containerized Microservices with Probabilistic Machine learning
  • Peng Kang, P. Lama
  • Computer Science
    2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC)
  • 2020
TLDR
RScale is presented, a robust resource scaling system that provides end-to-end performance guarantee for containerized microservices deployed in the cloud and meets the performance SLO targets for various microservice workflows even in the presence of multi-tenant performance interference and changing system dynamics.
Auto-Scaling VNFs Using Machine Learning to Improve QoS and Reduce Cost
TLDR
This study proposes a proactive Machine Learning (ML) based approach to perform auto-scaling of VNFs in response to dynamic traffic changes and demonstrates using realistic traffic load traces and optical backbone network that the ML method improves QoS and saves significant cost for network owners as well as leasers.
Autonomic scaling of Cloud Computing resources using BN-based prediction models
  • A. Bashar
  • Computer Science
    2013 IEEE 2nd International Conference on Cloud Networking (CloudNet)
  • 2013
TLDR
A Bayesian Networks based predictive modeling framework is proposed to provide for an autonomic scaling of utility computing resources in the Cloud Computing scenario and initial simulated experiments involving random demand scenarios provide insights into the feasibility and applicability.
Efficient Auto-Scaling Approach in the Telco Cloud Using Self-Learning Algorithm
TLDR
A novel SLA-aware and Resource-efficient Self-learning Approach (SRSA) for auto-scaling policy decision and shows that the solution outperforms threshold based policy and voting policy adopted by RightScale in oscillation suppression, QoS guarantee, and energy saving.
An adaptive scaling mechanism for managing performance variations in network functions virtualization: A case study in an NFV-based EPC
TLDR
This paper proposes an adaptive scaling mechanism based on Q-Learning and Gaussian Processes that is utilized by an agent to carry out an improvement strategy of a scaling policy, and therefore, to make better decisions for managing performance variations in NFV.
An extensible Autoscaling Engine (AE) for Software-based Network Functions
TLDR
An Autoscaling Engine (AE) capable of dynamically adapting a NS based on policies provided by the Operator and integrated in the ETSI NFV information model is presented.
Empirical prediction models for adaptive resource provisioning in the cloud
Time-series Extreme Event Forecasting with Neural Networks at Uber
TLDR
A novel endto-end recurrent neural network architecture is proposed that outperforms the current state of the art event forecasting methods on Uber data and generalizes well to a public M3 dataset used for time-series forecasting competitions.
Adaptive VNF Scaling and Flow Routing with Proactive Demand Prediction
TLDR
This work forms the VNF provisioning problem in order that the cost incurred by inaccurate prediction and VNF deployment is minimized, and employs an efficient online learning method which aims at minimizing the error in predicting the service chain demands.
...
...