Performance modelling and verification of cloud-based auto-scaling policies

Evangelidis, Alexandros; Parker, David; Bahsoon, Rami

doi:10.1016/j.future.2017.12.047

Cited by 48 publications

(25 citation statements)

References 22 publications

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“…In a real-time scenario that involves enormous networking data, the credibility of traditional machine learning frameworks is highly questionable. On the other hand, if emphasis is laid on the application of MLaaS environments like BigML, Algorithmia, DataRobot [48] and Azure Studio (used in the proposed work), it can help immensely to achieve auto-scaling or load balancing [49]- [50]. Table I presents an overview of some recent approaches and also highlights the significance of our proposed approach.…”

Section: Automated Machine Learningmentioning

confidence: 99%

Towards Effective Network Intrusion Detection: From Concept to Creation on Azure Cloud

2021

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Network Intrusion Detection is one of the most researched topics in the field of computer security. Hacktivists use sophisticated tools to launch numerous attacks that hamper the confidentiality, integrity and availability of computer resources. There is an incessant need to safeguard these resources to avoid further damage. In the proposed study, we have presented a meta-classification approach using decision jungle to perform both binary and multiclass classification. We have established the robustness of our approach by configuring an optimal set of hyper-parameters coupled with relevant feature subsets using a production-ready environment namely Azure machine learning. We have validated the efficiency of the proposed design using three contemporary datasets namely UNSW NB-15, CICIDS 2017, and CICDDOS 2019. We could achieve an accuracy of 99.8% pertaining to UNSW NB-15 whereas the accuracy in the case of CICIDS 2017 and CICDDOS 2019 datasets has been 98% and 97% respectively. A distinctive ability of the proposed model lies in its finesse to detect thirty-three modern attack types considerably well. Unlike conventional stacking ensembles, the proposed solution relies on a train-test ratio of 40:60 to establish the legitimacy of predictions. We also conducted statistical significance tests to compare the performance of classifiers involved in the study. To extend the functionalities further, we have automated the proposed model that can be a reliable candidate for real-time network intrusion detection.

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Section: Automated Machine Learningmentioning

confidence: 99%

Towards Effective Network Intrusion Detection: From Concept to Creation on Azure Cloud

2021

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“…Therefore, allocating the appropriate amount of resources to the VNF instance requires detailed knowledge of the VNF. Because this complicates manual scaling, a few studies 45,46 led to the proposal of performance models to predict the resource usage of VNFs. This prediction was used to dynamically resize the number of instances.…”

Section: Related Workmentioning

confidence: 99%

Deep Q‐network‐based auto scaling for service in a multi‐access edge computing environment

Lee

Jeong

et al. 2021

Int J Network Mgmt

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“…The presented user-side autoscaling performance evaluation approach and metrics should be considered complimentary to the existing approaches and metrics (Ilyushkin et al, 2017;Evangelidis et al, 2017).…”

Section: Comparison With the Existing Evaluation Schemesmentioning

confidence: 99%

“…The performance estimation approach presented by Evangelidis et al (2017) is based on probabilistic discrete-time Markov chain model checking. The checking is conducted using the PRISM tool, which is a probabilistic model checker for the formal modeling and analysis of systems with random or probabilistic behavior.…”

Section: Related Workmentioning

confidence: 99%

Multilayered Autoscaling Performance Evaluation: Can Virtual Machines and Containers Co–Scale?

Podolskiy

Jindal

Gerndt

2019

International Journal of Applied Mathematics and Computer Science

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The wide adoption of cloud computing by businesses is due to several reasons, among which the elasticity of the cloud virtual infrastructure is the definite leader. Container technology allows increasing the flexibility of an application by adding another layer of virtualization. The containers can be dynamically created and terminated, and also moved from one host to another. A company can achieve a significant cost reduction and increase the manageability of its applications by allowing the running of containerized microservice applications in the cloud. Scaling for such solutions is conducted on both the virtual infrastructure layer and the container layer. Scaling on both layers needs to be synchronized so that, for example, the virtual machine is not terminated with containers still running on it. The synchronization between layers is enabled by multilayered cooperative scaling, implying that the autoscaling solution of the virtual infrastructure layers is aware of the decisions of the autoscaling solution on the container layer and vice versa. In this paper, we introduce the notion of cooperative multilayered scaling and the performance of multilayered autoscaling solutions evaluated using the approach implemented in ScaleX (previously known as Autoscaling Performance Measurement Tool, APMT). We provide the results of the experimental evaluation of multilayered autoscaling performance for the combination of virtual infrastructure autoscaling of AWS, Microsoft Azure and Google Compute Engine with pods horizontal autoscaling of Kubernetes by using ScaleX with four distinct load patterns. We also discuss the effect of the Docker container image size and its pulling policy on the scaling performance.

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Performance modelling and verification of cloud-based auto-scaling policies

Cited by 48 publications

References 22 publications

Towards Effective Network Intrusion Detection: From Concept to Creation on Azure Cloud

Towards Effective Network Intrusion Detection: From Concept to Creation on Azure Cloud

Deep Q‐network‐based auto scaling for service in a multi‐access edge computing environment

Multilayered Autoscaling Performance Evaluation: Can Virtual Machines and Containers Co–Scale?

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