Predictive auto-scaling with OpenStack Monasca

Lanciano, Giacomo; Galli, Filippo; Cucinotta, Tommaso; Bacciu, Davide; Passarella, Andrea

doi:10.1145/3468737.3494104

Cited by 6 publications

(10 citation statements)

References 22 publications

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“…Second, an open-source implementation of the forecasting component within OpenStack, leveraging on Monasca [8] , that automatically computes forecasts and makes them available as additional metrics. Our implementation also includes a few reference implementations of metric predictors, i.e., linear regression (LR) [9] , autoregressive integrated moving average (ARIMA) [10] , multi-layer perceptron (MLP), and recurrent neural network (RNN), showing that the proposed architecture is flexible, as it allows for easy customization. Third, an extensive experimental validation of our architecture, using both synthetic and real content delivery network (CDN) workload traces, where we set up a synthetic elastic application, exploit the native capabilities of OpenStack, and compare the performance of several predictive elasticity controllers based on the aforementioned reference predictors.…”

Section: Contributionmentioning

confidence: 99%

“…The autoregressive moving average (ARMA) model is an effective tool for time-series forecasting (see Ref. [10]). Given the observations up to time , the forecast is given by…”

Section: Arimamentioning

confidence: 99%

“…The ARIMA model is an extension of ARMA that can deal with non-stationary time-series (see Ref. [10]). Indeed, given a , ARIMA is obtained by applying ARMA to estimate , which is the d-order differenced signal obtained from as for , and for .…”

Section: Arimamentioning

confidence: 99%

See 2 more Smart Citations

Extending OpenStack Monasca for Predictive Elasticity Control

Lanciano,

Galli,

Cucinotta

et al. 2024

Big Data Min. Anal.

Self Cite

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Traditional auto-scaling approaches are conceived as reactive automations, typically triggered when predefined thresholds are breached by resource consumption metrics. Managing such rules at scale is cumbersome, especially when resources require non-negligible time to be instantiated. This paper introduces an architecture for predictive cloud operations, which enables orchestrators to apply time-series forecasting techniques to estimate the evolution of relevant metrics and take decisions based on the predicted state of the system. In this way, they can anticipate load peaks and trigger appropriate scaling actions in advance, such that new resources are available when needed. The proposed architecture is implemented in OpenStack, extending the monitoring capabilities of Monasca by injecting short-term forecasts of standard metrics. We use our architecture to implement predictive scaling policies leveraging on linear regression, autoregressive integrated moving average, feed-forward, and recurrent neural networks (RNN). Then, we evaluate their performance on a synthetic workload, comparing them to those of a traditional policy. To assess the ability of the different models to generalize to unseen patterns, we also evaluate them on traces from a real content delivery network (CDN) workload. In particular, the RNN model exhibites the best overall performance in terms of prediction error, observed client-side response latency, and forecasting overhead. The implementation of our architecture is open-source.

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Section: Contributionmentioning

confidence: 99%

“…The autoregressive moving average (ARMA) model is an effective tool for time-series forecasting (see Ref. [10]). Given the observations up to time , the forecast is given by…”

Section: Arimamentioning

confidence: 99%

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Extending OpenStack Monasca for Predictive Elasticity Control

Lanciano,

Galli,

Cucinotta

et al. 2024

Big Data Min. Anal.

Self Cite

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“…Regarding the monitoring part of orchestration, I collaborated in the creation of an ML-based detection system for cloud operations in OpenStack, extending the work in [14]. The idea is to overcome the limitations of traditional, threshold-based detection systems by automatically applying corrective actions in response to observed anomalies.…”

Section: Future Workmentioning

confidence: 99%

Towards a Holistic Cloud System with End-to-End Performance Guarantees

Andreoli,

Cucinotta

2023

2023 IEEE International Conference on Cloud Engineering (IC2E)

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Computing technologies are undergoing a relentless evolution from both the hardware and software sides, incorporating new mechanisms for low-latency networking, virtualization, operating systems, hardware acceleration, smart services orchestration, serverless computing, hybrid private-public Cloud solutions and others. Therefore, Cloud infrastructures are becoming increasingly attractive for deploying a wider and wider range of applications, including those with more and more stringent timing constraints, like the emerging use case of deploying time-critical applications. However, despite the availability of a number of public Cloud offerings, and of products (or open-source suites) for deploying in-house private Cloud infrastructures, still there are no solutions readily available for managing time-critical software components with predictable end-to-end timing requirements in the range of hundreds or even tens of milliseconds. The goal of this discussion is to present the multi-domain challenges associated with orchestrating a holistic Cloud system with endto-end guarantees, which is the subject of my current PhD investigations.

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“…This auto-scaling is explained in detail in [ 27 ]. Recently in the year 2021, Lanciano et al, presented predictive auto-scaling with Monasca and proposed architecture for auto-scaling cloud services based on the status in which the system is expected to evolve in the near future [ 28 ]. In addition to SDN-based auto-scaling [ 29 ], recently some autonomous VNF auto-scaling methods have been proposed based on artificial intelligence including machine learning and deep learning [ 30 , 31 , 32 , 33 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Framework for Efficient Auto-Scaling of Virtual Network Functions in a Cloud Environment

Zafar

Ayub

Alkhammash

et al. 2022

Sensors

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Network Function Virtualization (NFV) offers an alternate method to design, deploy and manage network services. The NFV decouples network functions from the dedicated hardware and moves them to the virtual servers so that they can run in the software. One of the major strengths of the NFV is its ability to dynamically extend or reduce resources allocated to Virtual Network Functions (VNF) as needed and at run-time. There is a need for a comprehensive metering component in the cloud to store and process the metrics/samples for efficient auto-scaling or load-management of the VNF. In this paper, we propose an integrating framework for efficient auto-scaling of VNF using Gnocchi; a time-series database that is integrated within the framework to store, handle and index the time-series data. The objective of this study is to validate the efficacy of employing Gnocchi for auto-scaling of VNF, in terms of aggregated data points, database size, data recovery speed, and memory consumption. The employed methodology is to perform a detailed empirical analysis of the proposed framework by deploying a fully functional cloud to implement NFV architecture using several OpenStack components including Gnocchi. Our results show a significant improvement over the legacy Ceilometer configuration in terms of lower metering storage size, less memory utilization in processing and management of metrics, and reduced time delay in retrieving the monitoring data to evaluate alarms for the auto-scaling of VNF.

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Predictive auto-scaling with OpenStack Monasca

Cited by 6 publications

References 22 publications

Extending OpenStack Monasca for Predictive Elasticity Control

Extending OpenStack Monasca for Predictive Elasticity Control

Towards a Holistic Cloud System with End-to-End Performance Guarantees

Framework for Efficient Auto-Scaling of Virtual Network Functions in a Cloud Environment

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