QoS-aware Adaptive Resource Management in OFDMA Networks

PhDOne important feature of the future communication network is that users in the network are required to experience a guaranteed high quality of service (QoS) due to the popularity of multimedia applications. This thesis studies QoS-aware radio resource management schemes in different OFDMA network scenarios. Motivated by the fact that in current 4G networks, the QoS provisioning is severely constrained by the availability of radio resources, especially the scarce spectrum as well as the unbalanced traffic distribution from cell to cell, a joint antenna and subcarrier management scheme is proposed to maximise user satisfaction with load balancing. Antenna pattern update mechanism is further investigated with moving users. Combining network densi fication with cloud computing technologies, cloud radio access network (C-RAN) has been proposed as the emerging 5G network architecture consisting of baseband unit (BBU) pool, remote radio heads (RRHs) and fronthaul links. With cloud based information sharing through the BBU pool, a joint resource block and power allocation scheme is proposed to maximise the number of satisfi ed users whose required QoS is achieved. In this scenario, users are served by high power nodes only. With spatial reuse of system bandwidth by network densi fication, users' QoS provisioning can be ensured but it introduces energy and operating effciency issue. Therefore two network energy optimisation schemes with QoS guarantee are further studied for C-RANs: an energy-effective network deployment scheme is designed for C-RAN based small cells; a joint RRH selection and user association scheme is investigated in heterogeneous C-RAN. Thorough theoretical analysis is conducted in the development of all proposed algorithms, and the effectiveness of all proposed algorithms is validated via comprehensive simulations.China Scholarship Counci

Resource Management in Softwarized Networks

Communication networks are undergoing a major transformation through softwarization, which is changing the way networks are designed, operated, and managed. Network Softwarization is an emerging paradigm where software controls the treatment of network flows, adds value to these flows by software processing, and orchestrates the on-demand creation of customized networks to meet the needs of customer applications. Software-Defined Networking (SDN), Network Function Virtualization (NFV), and Network Virtualization are three cornerstones of the overall transformation trend toward network softwarization. Together, they are empowering network operators to accelerate time-to-market for new services, diversify the supply chain for networking hardware and software, bringing the benefits of agility, economies of scale, and flexibility of cloud computing to networks. The enhanced programmability enabled by softwarization creates unique opportunities for adapting network resources in support of applications and users with diverse requirements. To effectively leverage the flexibility provided by softwarization and realize its full potential, it is of paramount importance to devise proper mechanisms for allocating resources to different applications and users and for monitoring their usage over time. The overarching goal of this dissertation is to advance state-of-the-art in how resources are allocated and monitored and build the foundation for effective resource management in softwarized networks. Specifically, we address four resource management challenges in three key enablers of network softwarization, namely SDN, NFV, and network virtualization. First, we challenge the current practice of realizing network services with monolithic software network functions and propose a microservice-based disaggregated architecture enabling finer-grained resource allocation and scaling. Then, we devise optimal solutions and scalable heuristics for establishing virtual networks with guaranteed bandwidth and guaranteed survivability against failure on multi-layer IP-over-Optical and single-layer IP substrate network, respectively. Finally, we propose adaptive sampling mechanisms for balancing the overhead of softwarized network monitoring and the accuracy of the network view constructed from monitoring data

Traffic and Resource Management in Robust Cloud Data Center Networks

Cloud Computing is becoming the mainstream paradigm, as organizations, both large and small, begin to harness its benefits. Cloud computing gained its success for giving IT exactly what it needed: The ability to grow and shrink computing resources, on the go, in a cost-effective manner, without the anguish of infrastructure design and setup. The ability to adapt computing demands to market fluctuations is just one of the many benefits that cloud computing has to offer, this is why this new paradigm is rising rapidly. According to a Gartner report, the total sales of the various cloud services will be worth 204 billion dollars worldwide in 2016. With this massive growth, the performance of the underlying infrastructure is crucial to its success and sustainability. Currently, cloud computing heavily depends on data centers for its daily business needs. In fact, it is through the virtualization of data centers that the concept of "computing as a utility" emerged. However, data center virtualization is still in its infancy; and there exists a plethora of open research issues and challenges related to data center virtualization, including but not limited to, optimized topologies and protocols, embedding design methods and online algorithms, resource provisioning and allocation, data center energy efficiency, fault tolerance issues and fault tolerant design, improving service availability under failure conditions, enabling network programmability, etc. This dissertation will attempt to elaborate and address key research challenges and problems related to the design and operation of efficient virtualized data centers and data center infrastructure for cloud services. In particular, we investigate the problem of scalable traffic management and traffic engineering methods in data center networks and present a decomposition method to exactly solve the problem with considerable runtime improvement over mathematical-based formulations. To maximize the network's admissibility and increase its revenue, cloud providers must make efficient use of their's network resources. This goal is highly correlated with the employed resource allocation/placement schemes; formally known as the virtual network embedding problem. This thesis looks at multi-facets of this latter problem; in particular, we study the embedding problem for services with one-to-many communication mode; or what we denote as the multicast virtual network embedding problem. Then, we tackle the survivable virtual network embedding problem by proposing a fault-tolerance design that provides guaranteed service continuity in the event of server failure. Furthermore, we consider the embedding problem for elastic services in the event of heterogeneous node failures. Finally, in the effort to enable and support data center network programmability, we study the placement problem of softwarized network functions (e.g., load balancers, firewalls, etc.), formally known as the virtual network function assignment problem. Owing to its combinatorial complexity, we propose a novel decomposition method, and we numerically show that it is hundred times faster than mathematical formulations from recent existing literature