The Taub Faculty of Computer Science Events and Talks
Tuesday, 16.08.2022, 14:00
In recent years network operators are experiencing changes in clients needs. Self-driving cars, augmented reality games and large scale data streaming are simple examples of new applications that require faster service with higher bandwidth availability to the clients. These changes force the network operators shift their business model and new network paradigms arise.
The ongoing transition into 5G networks (and 6G networks that will soon arrive) is enabled in part by the combination of NFV (Network Function Virtualization) and MEC (Multi-access Edge Computing), two promising paradigms that allow executing ultra-low-latency network where services are located on edge nodes, physically closer to the clients.
These two paradigms complete each other, the main idea behind NFV is decoupling functionality from hardware, by moving function from hardware based server to being software-based and deployed on off-the-shelf commodity server, it allows networks to be more agile where service location may change swiftly. MEC, on the other hand, allows to move services from centralized data centers within network's core to the network's edge, which allows the network to provide service with lower latency due to short distance to the clients. Therefore, orchestrating this complex distributed environment and especially provisioning services in a timely manner, in order to address the dynamic workload, created a big challenge.
In order to utilize these paradigms, networks operators needs to deploy them efficiently by using new resource allocation algorithms. In this thesis we provide several algorithmic solutions that can help better utilize these emerging networks. We define a rigorous model and present an algorithmic solution for the specific problems. We provide analytically proven performance bounds for these algorithms that are compared to the relevant lower bound. For some of the problems we also present a thorough performance evaluation via extensive simulation, indicating their advantage over other solutions in realistic scenarios.