Clustering Algorithms for Scale-free Networks and Applications to Cloud Resource Management

In this paper we introduce algorithms for the construction of scale-free networks and for clustering around the nerve centers, nodes with a high connectivity in a scale-free networks. We argue that such overlay networks could support self-organization in a complex system like a cloud computing infrastructure and allow the implementation of optimal resource management policies.Comment: 14 pages, 8 Figurs, Journa

Enabling Scalable and Sustainable Softwarized 5G Environments

The fifth generation of telecommunication systems (5G) is foreseen to play a fundamental role in our socio-economic growth by supporting various and radically new vertical applications (such as Industry 4.0, eHealth, Smart Cities/Electrical Grids, to name a few), as a one-fits-all technology that is enabled by emerging softwarization solutions \u2013 specifically, the Fog, Multi-access Edge Computing (MEC), Network Functions Virtualization (NFV) and Software-Defined Networking (SDN) paradigms. Notwithstanding the notable potential of the aforementioned technologies, a number of open issues still need to be addressed to ensure their complete rollout. This thesis is particularly developed towards addressing the scalability and sustainability issues in softwarized 5G environments through contributions in three research axes: a) Infrastructure Modeling and Analytics, b) Network Slicing and Mobility Management, and c) Network/Services Management and Control. The main contributions include a model-based analytics approach for real-time workload profiling and estimation of network key performance indicators (KPIs) in NFV infrastructures (NFVIs), as well as a SDN-based multi-clustering approach to scale geo-distributed virtual tenant networks (VTNs) and to support seamless user/service mobility; building on these, solutions to the problems of resource consolidation, service migration, and load balancing are also developed in the context of 5G. All in all, this generally entails the adoption of Stochastic Models, Mathematical Programming, Queueing Theory, Graph Theory and Team Theory principles, in the context of Green Networking, NFV and SDN

Resource Management in Large-scale Systems

The focus of this thesis is resource management in large-scale systems. Our primary concerns are energy management and practical principles for self-organization and self-management. The main contributions of our work are: 1. Models. We proposed several models for different aspects of resource management, e.g., energy-aware load balancing and application scaling for the cloud ecosystem, hierarchical architecture model for self-organizing and self-manageable systems and a new cloud delivery model based on auction-driven self-organization approach. 2. Algorithms. We also proposed several different algorithms for the models described above. Algorithms such as coalition formation, combinatorial auctions and clustering algorithm for scale-free organizations of scale-free networks. 3. Evaluation. Eventually we conducted different evaluations for the proposed models and algorithms in order to verify them. All the simulations reported in this thesis had been carried out on different instances and services of Amazon Web Services (AWS). All of these modules will be discussed in detail in the following chapters respectively