Network virtualization allows operators to host multiple client services over their base physical infrastructures. Today, this technique is being used to support a wide range of applications in cloud computing services, content distribution, large data backup, etc. Accordingly, many different algorithms have also been developed to achieve efficient mapping of client virtual network (VN) requests over physical topologies consisting of networking infrastructures and datacenter compute/storage resources. However as applications continue to expand, there is a growing need to implement scheduling capabilities for virtual network demands in order to improve network resource utilization and guarantee quality of service (QoS) support.
Now the topic of advance reservation (AR) has been studied for the case of scheduling point-to-point connection demands. Namely, many different algorithms have been developed to support various reservation models and objectives. Nevertheless, few studies have looked at scheduling more complex "topology-level'' demands, including virtual network services. Moreover, as cloud servers expand, many providers want to ensure user quality support at future instants in time, e.g., for special events, sporting venues, conference meetings, etc.
In the light of above, this dissertation presents one of the first studies on advance reservation of virtual network services. First, the fixed virtual overlay network scheduling problem is addressed as a special case of the more generalized virtual network scheduling problem and a related optimization presented. Next, the complete virtual network scheduling problem is studied and a range of heuristic and meta-heuristic solutions are proposed. Finally, some novel flexible advance reservation models are developed to improve service setup and network resource utilization. The performance of these various solutions is evaluated using various methodologies (discrete event simulation and optimization tools) and comparisons made with some existing strategies.
| Identifer | oai:union.ndltd.org:USF/oai:scholarcommons.usf.edu:etd-7658 |
| Date | 16 November 2016 |
| Creators | Bai, Hao |
| Publisher | Scholar Commons |
| Source Sets | University of South Flordia |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Graduate Theses and Dissertations |
| Rights | default |
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