Probabilistic Partitioning for Edge Server Assignment with Time-Varying Workload
Quynh Vo, Duc A. Tran · 2019
As mobile devices have become ubiquitous, traffic at the edge of the network is growing faster than ever. To improve user experience, commodity servers are deployed in the edge to form a distributed network of mini datacenters. A consequential task is to partition the user cells into groups, each to be served by an edge server, to maximize the offloading to the edge. Most often this task can be cast as a graph partitioning problem, where a graph is used to represent the workload and the goal of the partition is to satisfy a given objective, for example, minimum cut. This approach is effective for a fixed workload. In contrast, we address the case where the workload changes over the time. We propose a probabilistic partitioning technique that is more robust to workload dynamics. The novelty of this technique is in the representation of the time-varying workload as a random variable of a probability distribution. We evaluate our theory with an evaluation using real-world datasets.