Design of an algorithm for edge-node resource orchestration within an Operator Platform

Sammanfattning: The future of networking lies within the development of low-latency and reliable networks. This development poses increased demand on the presence of edge-nodes. For a network operator to provide a low-latency edge-node resource, the physical distance from antenna-to-user needs to be small. This in turn, requires the network operator to have wide coverage of their physical antennas. An alternative solution is for network operators to share their edge-nodes within a so-called Operator Platform (OP) to reduce the cost of expanding their physical presence. In this project Design Science Research (DSR) was used to design an artifact named Master Thesis Orchestrator (MTO), to address the issue of finding and delivering shared edge-node resources between operators. An abstracted model of a realistic scenario was adopted. This model was used in evaluating the performance of the design against a baseline solution. The MTO is a decentralised algorithm using a shared memory cache. The artifact also has a randomised component which is used to control the frequency of shared memory accesses. These design choices were chosen to improve the performance in terms of scalability. A simulation of the artifact and baseline was conducted using a testbed implemented with Kubernetes/minikube. By assessing the performance on different input sizes (number of edge-nodes), the following performance metrics was gathered: success-rate (accuracy), run-time, and amount of data transmitted. The results showed that the MTO produced an average accuracy of 36% (baseline=96.8%) in terms of successful/failed user requests. The performance regarding run-time and transmitted data, varied depending on the outcome of the request. The MTO’s worst-case performance occurs for failed matches, leading to performance akin to that of the baseline’s average performance. The best-case performance of the MTO showed improvements of run-time compared to the baseline solution. The data was validated through an Analysis of variance (ANOVA)-test and the distributions are significantly (α = 5%) different from each other. The designed artifact is however not better than the baseline solution on all analysed metrics. The designed algorithm is volatile in-terms of time-needed and accuracy, but resource efficient. The poor accuracy is a significant factor into the probability that the worst-case performance would occur resulting in a slow and unreliable solution. Nevertheless, in terms of scalability, the designed artifact is showing less severe growth-rate than that of the baseline.