Leveraging Edge Computing through Collaborative Machine Learning

The Internet of Things (IoT) offers the ability to analyze and predict our surroundings through sensor networks at the network edge. To facilitate this predictive functionality, Edge Computing (EC) applications are developed by considering: power consumption, network lifetime and quality of context inference. Humongous contextual data from sensors provide data scientists better knowledge extraction, albeit coming at the expense of holistic data transfer that threatens the network feasibility and lifetime. To cope with this, collaborative machine learning is applied to EC devices to (i) extract the statistical relationships and (ii) construct regression (predictive) models to maximize communication efficiency. In this paper, we propose a learning methodology that improves the prediction accuracy by quantizing the input space and leveraging the local knowledge of the EC devices

Using Genetic Programming to Build Self-Adaptivity into Software-Defined Networks

Self-adaptation solutions need to periodically monitor, reason about, and adapt a running system. The adaptation step involves generating an adaptation strategy and applying it to the running system whenever an anomaly arises. In this article, we argue that, rather than generating individual adaptation strategies, the goal should be to adapt the control logic of the running system in such a way that the system itself would learn how to steer clear of future anomalies, without triggering self-adaptation too frequently. While the need for adaptation is never eliminated, especially noting the uncertain and evolving environment of complex systems, reducing the frequency of adaptation interventions is advantageous for various reasons, e.g., to increase performance and to make a running system more robust. We instantiate and empirically examine the above idea for software-defined networking -- a key enabling technology for modern data centres and Internet of Things applications. Using genetic programming,(GP), we propose a self-adaptation solution that continuously learns and updates the control constructs in the data-forwarding logic of a software-defined network. Our evaluation, performed using open-source synthetic and industrial data, indicates that, compared to a baseline adaptation technique that attempts to generate individual adaptations, our GP-based approach is more effective in resolving network congestion, and further, reduces the frequency of adaptation interventions over time. In addition, we show that, for networks with the same topology, reusing over larger networks the knowledge that is learned on smaller networks leads to significant improvements in the performance of our GP-based adaptation approach. Finally, we compare our approach against a standard data-forwarding algorithm from the network literature, demonstrating that our approach significantly reduces packet loss.Comment: arXiv admin note: text overlap with arXiv:2205.0435