Banca de DEFESA: SANDINO BARROS JARDIM

Uma banca de DEFESA de DOUTORADO foi cadastrada pelo programa.
STUDENT : SANDINO BARROS JARDIM
DATE: 23/10/2020
TIME: 10:00
LOCAL: http://meet.google.com/znv-eksf-wsv
TITLE:

Proactive Autoscaling Towards Assertive Elasticity of Virtual Network Functions in Service Chains

KEY WORDS:

Network Function Virtualization; Design of virtual functions; Machine learning; Demand Prediction

PAGES: 100
BIG AREA: Ciências Exatas e da Terra
AREA: Ciência da Computação
SUBÁREA: Sistemas de Computação
SPECIALTY: Teleinformática
SUMMARY:

The virtualization of network functions is a technology that proposes to decouple network functions, traditionally allocated on specialized hardware, so that making them available as software elements executing at general-purpose servers premises. Such flexibility allows offering network services running over cloud infrastructures and facilitates enforcing network policies based on the chaining of different functions, through which a targeting traffic must be subjected. The variation in services demand will require the resource management attribute of elasticity to tackle performance goals, adjusting the computational resources of the functions to suit both the new projected demand  and operating costs so as to avoid provisioning beyond the need. Traditionally, reactive threshold-based approaches afford elasticity function, to the cost of exponentially increasing response times as resources run out. Recent work suggest proactive elasticity approaches harnessing the combination of machine learning methods that allow anticipating decisions and adapting resources to the projected demand, as much as possible. Such adequacy is crucial for the success of a proactive elasticity solution, in the perspective to enable assertive scaling decisions to respond with agility and precision to variations in demand, as well as to contribute with the balance of cost and performance objectives. This doctoral thesis presents ARENA, a proactive elasticity mechanism for autoscaling virtualized network functions driven by demand prediction based on machine learning to maximize the assertiveness of horizontal and vertical dimensioning decisions.

BANKING MEMBERS:
Presidente - 1699087 - AUGUSTO JOSE VENANCIO NETO
Interna - 1350250 - ANNE MAGALY DE PAULA CANUTO
Interno - 2212166 - BENJAMIN RENE CALLEJAS BEDREGAL
Externo à Instituição - HAROLD IVAN ANGULO BUSTOS - UERN
Externa à Instituição - MARÍLIA PASCOAL CURADO - UC