QUIC is a UDP-based multiplexed and secure transport protocol that was standardized in 2021 by the IETF. It seeks to replace the traditional TCP/TLS stack by combining functionality from different layers of the ISO/OSI model. Therefore, it reduces overhead and introduces new functionality to better support application protocols, e.g., streams and datagrams. QUIC is the foundation for HTTP/3 and new proxy technologies (MASQUE). It is used for video streaming and considered for other media services.

This talk will introduce the protocol and motivate its relevance. In the second part, I will provide insights into existing implementations and their performance. Our research shows that QUIC performance varies widely between client and server implementations from 90 Mbit/s to over 6000 Mbit/s. In the second part, I provide an overview about QUIC deployments on the Internet. At least one deployment for 18 different libraries can actually be found on the Internet.

The complexity of the protocol, the diversity of libraries and their usage on the Internet makes QUIC an important research subject.

The impact of algorithmic decision-making systems on individuals has raised significant interest in addressing fairness concerns within such systems. Designing fair systems entails several critical components, which have garnered considerable attention from the research community. However, notable gaps persist in three key components. Specifically, in this thesis, we address gaps in following components: i) evaluating existing approaches and systems for (un)fairness, ii) updating deployed algorithmic systems fairly, and iii) designing new decision-making systems from scratch. Firstly, we evaluate fairness concerns within foundation models. The primary challenge is that fairness definitions are task-specific while foundation models can be used for diverse tasks. To address this problem, we introduce a broad taxonomy to evaluate the fairness of popular foundation models and their popular bias mitigation approaches. Secondly, we tackle the issue of fairly updating already deployed algorithmic decision-making systems. To this end, we propose a novel notion of update-fairness and present measures and efficient mechanisms to incorporate this notion in binary classification.  However, in cases where there is no deployed system or updating an existing system is prohibitively complex, we must design new fair decision-making systems from scratch. Lastly, we develop new fair decision-making systems for three key application scenarios. Major challenges in designing these systems include computational complexity, lack of existing approaches to tackle fairness issues and designing human-subject based studies. We develop a computationally efficient mechanism for fair influence maximization to make the spread of information in social graphs fair. Additionally, we address fairness concerns under model uncertainty, i.e., uncertainty arising due lack of data or the knowledge about the best model. We propose a novel approach for training nondiscriminatory systems that differentiate errors based on their uncertainty origin and provide efficient methods to identify and equalize errors occurring due to model uncertainty in binary classification. Furthermore, we investigate whether algorithmic decision-aids can mitigate inconsistency among human decision-makers through a large-scale study testing novel ways to provide machine advice.

Reward functions are central in reinforcement learning (RL), guiding agents towards optimal decision-making. The complexity of RL tasks requires meticulously designed reward functions that effectively drive learning while avoiding unintended consequences. Effective reward design aims to provide signals that accelerate the agent’s convergence to optimal behavior. Crafting rewards that align with task objectives, foster desired behaviors, and prevent undesirable actions is inherently challenging. This thesis delves into the critical role of reward signals in RL, highlighting their impact on the agent’s behavior and learning dynamics and addressing challenges such as delayed, ambiguous, or intricate rewards. In this thesis work, we tackle different aspects of reward shaping. First, we address the problem of designing informative and interpretable reward signals from a teacher’s/expert’s perspective (teacher-driven). Here, the expert, equipped with the optimal policy and the corresponding value function, designs reward signals that expedite the agent’s convergence to optimal behavior. Second, we build on this teacher-driven approach by introducing a novel method for adaptive interpretable reward design. In this scenario, the expert tailors the rewards based on the learner’s current policy, ensuring alignment and optimal progression. Third, we propose a meta-learning approach, enabling the agent to self-design its reward signals online without expert input (agent-driven). This self-driven method considers the agent’s learning and exploration to establish a self-improving feedback loop

Cloud systems are composed of multiple inter-connected independent systems. These systems are complex in nature as they are made of heterogeneous components rife with complicated interactions, operate in dynamic conditions, and exhibit unpredictable behaviors. Despite all the complexity, developers of these systems are tasked to efficiently design, implement, optimize, operate, and improve these systems in a continuous fashion. A proposed way of managing the complexity for designing, implementing, and optimizing these systems is to automate these tasks. There are three major roadblocks preventing this automation from becoming reality - (i) lack of abstractions for design and implementation and design exploration of cloud systems; (ii) lack of abstractions and tooling converting user's high level design intent into actual implementations;  (iii) lack of abstractions for leveraging runtime information for optimizing cloud systems. I propose new abstractions for cloud systems, with a special focus on microservice systems, for automating developer tasks. The work I will present takes us one step closer to the vision of automating the design, implementation, and optimization of cloud systems whilst managing the inherent complexity of these systems.