Faculty of Engineering and Built Environment
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Item A resources allocation scheme for joint optical wireless transport networks(IEEE, 2023-08-03) Molefe, Mlungisi; Sibiya, Khulekani; Nleya, BakheAs the future of networking dives into a new era of connecting every single physical device into the internet termed Internet of Things (loT), this significantly means a rapid increase in the number of online connected devices, which leads to more bandwidth hungry and data consuming devices. The fifth generation (5G) of mobile communication has been deployed already in multiple countries, therefore researchers have migrated their focus to the sixth generation (6G) of mobile communication to cater for extensive coverage and massive number of loT devices. A promising architecture and technology to cope with massive number of online devices and extensive coverage is a joint optical wireless transport network which offers comparably ultra-high systems capacity and extremely low latency while maintaining an improved quality of service. Furthermore, an optical wireless transport network can accommodate high speed mobility for frequently moving end user devices which is essential for 6G. In this paper our focus is to explore and propose an ultimate optical wireless transport network architecture scheme that will cater for loT as well as networks beyond 5G. We thus propose an innovative Optical-Backhaul and Wireless Access (OBWA) network architecture as a favorable solution for future networks. We further present a joint channel and route allocation (JCRA) scheme for achieving optimal quality of experience. Performance evaluation of the proposed JCRA scheme for OBW A network architecture show a significant improvement in the network throughput as well as the network end-to-end delay despite varying load traffic or varying flow channels.Item Resources allocation for hybrid cloud-edge computing in 5G network slicing(TELKOM, 2023-09-01) Molefe, Mlungisi; Sibiya, Khulekani; Nleya, Bakhe; Smuts, Martin; Taute, AntonIn typical heterogeneous networks such as 5G and beyond, innovative technologies such as edge computing and network slicing can enhance overall network performance in terms of handling critical mission services as they often require extremely low latencies. Notably, network slicing facilitates the provisioning of virtual slices with different characteristics to serve different end-user requirements. The Network operator achieves this goal by utilizing the already existing physical wireless network resource. Current resource provisioning schemes suffer inadequacies in scalability and flexibility Thus to support both Cloud and Edge Computing in 5G and beyond networking, the work herein proposes a novel low latency scheme that affords dynamic and intelligent allocation of multi-dimensional resources. It bases on a Hybrid Cloud-edge Network Slicing (HCENS) architecture on leveraging both Cloud and Edge Computing The proposed scheme creates a flexible, scalable as well as energy efficient resource provisioning. Its architecture comprises both centralized units (CUs) and distributed units (DUs). These provide storage, that in turn enhances function partitioning for various network slices. Several agent-based simulations scenarios are carried out in evaluating the efficacy of the proposed scheme. Obtained analytical and simulation results indicate drastic reductions in network latencies for critical mission end user services. This couples with reductions in storage requirements.Item Energy-efficient resource management framework for cloud data centers(2023-05) Sibiya, Khulekani; Nleya, BakheThe continuing global surge in various cloud services, IoT, and Edge (Fog) computing has led to a sudden increase in the demand for Datacenters. By definition, a data center is a physical facility that corporations/organizations use to house their critical applications and data. A data canter‟s design is based on a network of computing and storage resources that enable the delivery of shared applications and data. Notable advantages of Data Centers include but are not limited, to their ability to provide services to end-users based on affordable rates in various plans as per contractual agreements. They also offer a robust hardware ecosystem as well as software. In operational terms, data centers offer reliable and enhanced system performance by way of carefully distributing the traffic loads uniformly across the cluster nodes. In that way, end users are excused from maintenance responsibilities. Data centers also afford instant scalability based on changing capacity demands by users. To enhance the fail-safe abilities of data canters, backup systems are incorporated. A notable drawback of Datacenters is the high power consumption which up both CAPEX and OPEX costs. E.g it is prohibitively costly to erect robust cooling systems for a large-scale data center. The same cooling system ought to be scalable to accommodate future expansions of the data centers in terms of new services that may require new hardware to be incorporated. Thus scalability of energy supply capacity is quite a challenge. Thus, how to maximize power utilization and optimizing the performance per power budget is critical for data centers to deliver enough computation ability. Overall the operational costs of Data centers directly link the resource management algorithms implemented to assign virtual machines (VMs) to actual hardware servers and degrees of flexibility to relocate them elsewhere in case of emergencies usually associated with power losses of excessive heating of system elements. The main contribution of this thesis is in proposing and analyzing a hierarchical SLA-based distributed hierarchical resource allocation and optimization scheme, that considers constraints such as energy consumption and cooling-related energy consumption in addition to the scalability issue. We also incorporate a load-balancing algorithm to minimize the operational costs of the proposed scheme. We utilize CloudSim, which is a customizable tool that supports the modeling, and creation of several VMs, (as well as mapping tasks to appropriate VMs) for the scheme‟s performance evaluation. Ultimately obtained results show that the scheme significantly reduces the operational costs of the overall cloud data center system and at the same time ensures energy efficiency.