Biblio

The router's buffer size imposes significant impli-cations on the performance of the network. Network operators nowadays configure the router's buffer size manually and stati-cally. They typically configure large buffers that fill up and never go empty, increasing the Round-trip Time (RTT) of packets significantly and decreasing the application performance. Few works in the literature dynamically adjust the buffer size, but are implemented only in simulators, and therefore cannot be tested and deployed in production networks with real traffic. Previous work suggested setting the buffer size to the Bandwidth-delay Product (BDP) divided by the square root of the number of long flows. Such formula is adequate when the RTT and the number of long flows are known in advance. This paper proposes a system that leverages programmable switches as passive instruments to measure the RTT and count the number of flows traversing a legacy router. Based on the measurements, the programmable switch dynamically adjusts the buffer size of the legacy router in order to mitigate the unnecessary large queuing delays. Results show that when the buffer is adjusted dynamically, the RTT, the loss rate, and the fairness among long flows are enhanced. Additionally, the Flow Completion Time (FCT) of short flows sharing the queue is greatly improved. The system can be adopted in campus, enterprise, and service provider networks, without the need to replace legacy routers.

In today's technological climate, users require fast automation and digitization of results for large amounts of data at record speeds. Especially in the field of medicine, where each patient is often asked to undergo many different examinations within one diagnosis or treatment. Each examination can help in the diagnosis or prediction of further disease progression. Furthermore, all produced data from these examinations must be stored somewhere and available to various medical practitioners for analysis who may be in geographically diverse locations. The current medical climate leans towards remote patient monitoring and AI-assisted diagnosis. To make this possible, medical data should ideally be secured and made accessible to many medical practitioners, which makes them prone to malicious entities. Medical information has inherent value to malicious entities due to its privacy-sensitive nature in a variety of ways. Furthermore, if access to data is distributively made available to AI algorithms (particularly neural networks) for further analysis/diagnosis, the danger to the data may increase (e.g., model poisoning with fake data introduction). In this paper, we propose a federated learning approach that uses decentralized learning with blockchain-based security and a proposition that accompanies that training intelligent systems using distributed and locally-stored data for the use of all patients. Our work in progress hopes to contribute to the latest trend of the Internet of Medical Things security and privacy.