Load balancing method for KDN-based data center using neural network

The growth of cloud application services delivered through data centers with varying traffic demands unveils limitations of traditional load balancing methods. Aiming to attend evolving scenarios and improve the overall network performance, this paper proposes a load balancing method based on an Artificial Neural Network (ANN) in the context of Knowledge-Defined Networking (KDN). KDN seeks to leverage Artificial Intelligence (AI) techniques for the control and operation of computer networks. KDN extends Software-Defined Networking (SDN) with advanced telemetry and network analytics introducing a so-called Knowledge Plane. The ANN is capable of predicting the network performance according to traffic parameters paths. The method includes training the ANN model to choose the path with least load. The experimental results show that the performance of the KDN-based data center has been greatly improved

Estudos de aplicabilidade de redes neurais para balanceamento de carga em redes de data centers baseados em OpenFlow

Orientador: Christian Rodolfo Esteve RothenbergDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: O crescimento dos serviços de aplicativos em nuvem fornecidos por os data centers com demandas de tráfego variáveis revela limitações dos métodos tradicionais de balanceamento de carga. Visando em atender aos cenários em evolução e melhorar o desempenho geral da rede. Esta pesquisa propõe um estudo de balanceamento de carga baseado em uma Rede Neural Artificial (ANN) no contexto da Rede Definido por Conhecimento (KDN). A KDN busca alavancar as técnicas de Inteligência Artificial (AI) para o controle e operação de redes de computadores. O KDN amplia o Redes Definidas por Software (SDN) com telemetria avançada e análise rede, introduzindo o chamado Plano de Conhecimento. A proposta da ANN é capaz de prever o desempenho da rede de acordo com os parâmetros de tráfego, criando um modelo de comportamento de tráfego baseado em medições de largura de banda e latência sobre diferentes caminhos. O estudo inclui o treinamento do modelo ANN para escolher o roteamento de caminho menos carregado. Realizamos uma série de experimentos em um ambiente emulado para validar o estudo proposto. Os resultados experimentais mostram que o desempenho do data center baseado em KDN foi bastante aprimoradoAbstract: The growth of cloud application services delivered through data centers with varying traffic demands unveils the limitations of traditional load balancing study. Aiming at attending the evolving scenarios and improving the overall network performance. This research proposes a load-balancing study based on an Artificial Neural Network (ANN) in the context of Knowledge-Defined Networking (KDN). KDN seeks to leverage Artificial Intelligence (AI) techniques for the control and operation of computer networks. KDN extends Software Defined Networking (SDN) with advanced telemetry and network analytics introducing a so-called Knowledge Plane. The ANN is capable of predicting the network performance according to traffic parameters by creating a model of traffic behavior using the available bandwidth and latency measurements over different paths. The study includes training the ANN model to choose the least loaded path routing. We conduct a series of experiments to verify the proposed study. The experimental results show that the performance of the KDN-based data center has been greatly improvedMestradoEngenharia de ComputaçãoMestre em Engenharia Elétrica134031/2015-6CNP

Internet of Things and Neural Network Based Energy Optimization and Predictive Maintenance Techniques in Heterogeneous Data Centers

Rapid growth of cloud-based systems is accelerating growth of data centers. Private and public cloud service providers are increasingly deploying data centers all around the world. The need for edge locations by cloud computing providers has created large demand for leasing space and power from midsize data centers in smaller cities. Midsize data centers are typically modular and heterogeneous demanding 100% availability along with high service level agreements. Data centers are recognized as an increasingly troublesome percentage of electricity consumption. Growing energy costs and environmental responsibility have placed the data center industry, particularly midsize data centers under increasing pressure to improve its operational efficiency. The power consumption is mainly due to servers and networking devices on computing side and cooling systems on the facility side. The facility side systems have complex interactions with each other. The static control logic and high number of configuration and nonlinear interdependency create challenges in understanding and optimizing energy efficiency. Doing analytical or experimental approach to determine optimum configuration is very challenging however, a learning based approach has proven to be effective for optimizing complex operations. Machine learning methodologies have proven to be effective for optimizing complex systems. In this thesis, we utilize a learning engine that learns from operationally collected data to accurately predict Power Usage Effectiveness (PUE) and creation of intelligent method to validate and test results. We explore new techniques on how to design and implement Internet of Things (IoT) platform to collect, store and analyze data. First, we study using machine learning framework to predictively detect issues in facility side systems in a modular midsize data center. We propose ways to recognize gaps between optimal values and operational values to identify potential issues. Second, we study using machine learning techniques to optimize power usage in facility side systems in a modular midsize data center. We have experimented with neural network controllers to further optimize the data suite cooling system energy consumption in real time. We designed, implemented, and deployed an Internet of Things framework to collect relevant information from facility side infrastructure. We designed flexible configuration controllers to connect all facility side infrastructure within data center ecosystem. We addressed resiliency by creating reductant controls network and mission critical alerting via edge device. The data collected was also used to enhance service processes that improved operational service level metrics. We observed high impact on service metrics with faster response time (increased 77%) and first time resolution went up by 32%. Further, our experimental results show that we can predictively identify issues in the cooling systems. And, the anomalies in the systems can be identified 30 days to 60 days ahead. We also see the potential to optimize power usage efficiency in the range of 3% to 6%. In the future, more samples of issues and corrective actions can be analyzed to create practical implementation of neural network based controller for real-time optimization.Ph.D.Information Systems Engineering, College of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/136074/1/Final Dissertation Vishal Singh.pdfDescription of Final Dissertation Vishal Singh.pdf : Dissertatio