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An Ensemble Online Learning-based Approach for VNF Scaling in the Edge

Auto-scaling, Edge Computing, Online Machine Learning, Ensemble Learning, Virtual Network Functions, Autonomic Computing.

Edge Computing (EC) platforms have recently been proposed to manage emergency
applications with high computational load and low response time requirements. EC plat-
forms take advantage of the distributed nature of edge devices close to end-users and data
sources, thus minimizing constraints such as bandwidth consumption, network congestion,
response time, and operational costs imposed by cloud providers. To provide more agility
and flexibility for service provisioning while reducing deployment costs for infrastructure
providers, technologies such as Network Functions Virtualization (NFV) are frequently
used in production environments at the network edge, promoting the decoupling of hard-
ware and network functions using virtualization technologies. Network or even higher
layers functions are implemented as Virtual Network Functions (VNFs) software entities.
The integration of EC and NFV paradigms, as proposed by ETSI MEC, enables the cre-
ation of an ecosystem for 5G applications. Such integration allows the creation of VNF
chains, representing end-to-end services for end-users and their deployment on edge nodes.
A Service Function Chaining (SFC) comprises a set of VNFs chained together in a given
order, where each VNF can be running on a different edge node. The main challenges
in this environment concern the dynamic provisioning and deprovisioning of distributed
resources to run the VNFs and meet application requirements while optimizing the cost
to the infrastructure provider. In this sense, scaling VNFs in this environment represents
creating new containers or virtual machines and reallocating resources to them due to the
variation in the workload and dynamic nature of the EC environment. This work presents
a hybrid auto-scaling approach for the dynamic scaling of VNFs in the EC environment.
Such an auto-scaling approach employs an online ensemble machine-learning technique
 that consists of different online machine-learning models that predict the workload. The
architecture of such an auto-scaling approach follows the abstraction of the MAPE-K
(Monitor-Analyze-Plan-Execute over a shared Knowledge) control loop to dynamically
adjust the number of resources in response to workload changes. This approach is innova-
tive because it proactively predicts the workload to anticipate scaling actions and behaves
reactively when the prediction model does not meet the desired quality. In addition, the
proposal requires no prior knowledge of the data’s behavior, making it suitable for use
in different contexts. We also have developed an algorithm to scale the VNF instances in
the edge computing environment that uses a strategy to define how many resources to
allocate or deallocate to a VNF instance during a scaling action. Finally, we evaluated
the ensemble method and the proposed algorithm, comparing prediction performance and
the amount of scaling actions and SLA violations.

Presidente - 1213777 - THAIS VASCONCELOS BATISTA
Externo ao Programa - 2510306 - FREDERICO ARAUJO DA SILVA LOPES - UFRNExterna à Instituição - FLAVIA COIMBRA DELICATO - UFF
Externo à Instituição - PAULO DE FIGUEIREDO PIRES - UFF
Notícia cadastrada em: 08/09/2023 17:56
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