Intelligent Self-Optimization for Edge and IoT Storage Platforms

Abstract

Internet of Things and Edge architectures have become increasingly popular during the last few years, leading to more complex architectures that cover multiple real-life use cases in various domains. These complex architectures often employ edge and IoT storage platforms that optimize the storage, processing, transfer, and general governance of the data produced and used by the applications hosted in these architectures. The present work proposes a novel approach for intelligent self-optimization in an edge cloud employing IoT storage nodes. Intelligent refers to the usage of constraint-aware optimization algorithms that adapt placement decisions to the evolving state of the network rather than relying on manual configuration. It aims to proactively place docker and virtual machine images in specific nodes to minimize the transfer delays, the bandwidth used, and the occupied memory in the edge nodes. The solution presented builds on our earlier work by including the state of the edge network at each point in time, creating a time series of graphs, and applying four distinct optimization methods to optimize the image placement at each timestep. The evaluation was performed in simulated scenarios, testing IoT edge networks of 64 nodes with mixed ethernet and Wi-Fi connections. The results indicate that integer linear programming solutions consistently achieve the minimum number of image replicas across all evaluated topologies, reducing the average hosts to clients ratio to 0.36–0.49 compared to 0.87–2.03 for the next best algorithm, while greedy and approximation algorithms deliver quick and cost effective placements in under 15 ms per timestep across all network configurations.