Choice With No Regret – Mobility Induced Migration in Fog Computing

Fog computing is a highly virtualized platform that acts as an intermediary be- tween end-devices and cloud centers. Due to the close proximity between fog nodes and users, fog computing is ideal for latency-sensitive applications. To ensure Quality of Service (QoS) for mobile users, especially in applications like autonomous vehicles, fog computing employs a network slicing approach for efficient resource allocation. Moreover, services need to be kept close to users, necessitating the transfer of services from current fog nodes to closer nodes while users are mobile, a process known as ”mobility induced migration in fog computing”.

This thesis introduces three novel methods: (1) Fuzzy Migration Decision (FMD)), (2) Efficient Destination Choice (EDC) and (3) Robust Intra-Slice Migration in Fog Computing. Each of these methods plays a distinct role in the migration process, encompassing the decision-making phase of when to initiate migration, the selection of the optimal destination node within the network, and the execution of migration in a manner that prioritizes robustness and low latency.

The FMD algorithm consistently outperforms existing methods by determining the optimal time to start migration using fuzzy logic. It achieves approximately a 15% reduction in migration time and a 12% decrease in network usage compared to bench- mark algorithms.

Similarly, the EDC algorithm, which aims to find the optimal destination node us- ing fuzzy logic and avoids negative impacts on network performance caused by mi- gration, achieves a 10.15% reduction in network usage compared to benchmark algo- rithms.

Moreover, our proposed Robust Intra-Slice Migration in Fog Computing algorithm demonstrates a reduction of total migration time by around 10-26% and downtime by about 2-23% compared to non-robust post-copy migration techniques.

The evaluation results demonstrate that the proposed algorithms effectively find a trade off between preserving QoS for users and enhancing network efficiency dur- ing their operation. Furthermore, when we execute the entire migration process using the proposed algorithms, there is a notable reduction in both migration latency and the number of migrations. This migration process exhibits robust performance when compared to other studies. These findings underscore how addressing all three mi- gration challenges (When, Where, and How) can significantly influence the overall outcome of mobility-induced migration.