Geography Aware Virtual Machine Migrations and Replications for Distributed Cloud Data Centers

Cloud computing provides access to computing resources for a fee. Client applications and services can be hosted in clouds. Cloud computing typically uses a network of data centers that are geographically dispersed. The distance between clients and applications is impacted by geographical distance. The geographical distribution of client requests can be random and difficult to predict. This suggests a need to reconsider the placement of services at run-time through migration. This thesis describes a framework based on software-defined networking (SDN) principles. It demonstrates algorithms that are periodically executed and determine candidate services to migrate and replicate as well as target data centers to migrate to and replicate to and an evaluation. The evaluation shows that effectiveness of the algorithms

Online Virtual Network Provisioning in Distributed Cloud Computing Data Centers

Efficient virtualization methodologies constitute the core of cloud computing data center implementation. Clients are attracted to the cloud model by the ability to scale available resources dynamically and the flexibility in payment options. However, performance hiccups can push them to return to the buy-and-maintain model. Virtualization plays a key role in the synchronous management of the thousands of servers along with clients\u27 data residing on them. To achieve seamless virtualization, cloud providers require a system that performs the function of virtual network mapping. This includes receiving the cloud client requests and allocating computational and network resources in a way that guarantees the quality of service conditions for clients while maximizing the data center resource utilization and providers\u27 revenue. In this thesis, we introduce a comprehensive system to solve the problem of virtual network mapping for a set of connection requests sent by cloud clients. Connections are collected in time intervals called windows. Subsequently, node mapping and link mapping are performed. Different window size selection schemes are introduced and evaluated. Three schemes to prioritize connections are used and their effect is assessed. Moreover, a technique dealing with connections spanning over more than a window is introduced. Simulation results show that the dynamic window size algorithm achieves cloud service providers objectives in terms of generated revenue, served connections ratio, resource utilization and computational overhead. In addition, experimental results show that handling spanning connections independently improves the results for the performance metrics measured. Moreover, in a cloud infrastructure, handling all resources efficiently in their usage, management and energy consumption is challenging. We propose an energy efficient technique for embedding online virtual network requests in cloud data centers. The core focus of this study is to manage energy efficiently in cloud environment. A fixed windowing technique with spanning connections is used. Our algorithm, and a technique for randomly embedding nodes and links are also explained. The results clearly show that the algorithm used in this study generated better results in terms of energy consumption, served connections and revenue generation

Leveraging Machine Learning Techniques towards Intelligent Networking Automation

In this thesis, we address some of the challenges that the Intelligent Networking Automation (INA) paradigm poses. Our goal is to design schemes leveraging Machine Learning (ML) techniques to cope with situations that involve hard decision-making actions. The proposed solutions are data-driven and consist of an agent that operates at network elements such as routers, switches, or network servers. The data are gathered from realistic scenarios, either actual network deployments or emulated environments. To evaluate the enhancements that the designed schemes provide, we compare our solutions to non-intelligent ones. Additionally, we assess the trade-off between the obtained improvements and the computational costs of implementing the proposed mechanisms. Accordingly, this thesis tackles the challenges that four specific research problems present. The first topic addresses the problem of balancing traffic in dense Internet of Things (IoT) network scenarios where the end devices and the Base Stations (BSs) form complex networks. By applying ML techniques to discover patterns in the association between the end devices and the BSs, the proposed scheme can balance the traffic load in a IoT network to increase the packet delivery ratio and reduce the energy cost of data delivery. The second research topic proposes an intelligent congestion control for internet connections at edge network elements. The design includes a congestion predictor based on an Artificial Neural Network (ANN) and an Active Queue Management (AQM) parameter tuner. Similarly, the third research topic includes an intelligent solution to the inter-domain congestion. Different from second topic, this problem considers the preservation of the private network data by means of Federated Learning (FL), since network elements of several organizations participate in the intelligent process. Finally, the fourth research topic refers to a framework to efficiently gathering network telemetry (NT) data. The proposed solution considers a traffic-aware approach so that the NT is intelligently collected and transmitted by the network elements. All the proposed schemes are evaluated through use cases considering standardized networking mechanisms. Therefore, we envision that the solutions of these specific problems encompass a set of methods that can be utilized in real-world scenarios towards the realization of the INA paradigm