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T. Rausch:
"A Distributed Compute Fabric for Edge Intelligence";
Supervisor, Reviewer: S. Dustdar, W. Shi, M. Zhao; Institute of Information Systems Engineering, Distributed Systems Group, 2021; oral examination: 2021-05-21.

Edge intelligence is a post-cloud computing paradigm, and a consequence of the past decade of developments in Artificial Intelligence (AI), Internet of Things (IoT), and human augmentation. At the intersection of these domains, new applications have emerged that require real-time access to sensor data from the environment, low-latency AI model inferencing, or access to data isolated in edge networks for training AI models, all while operating in highly dynamic and heterogeneous computing environments. These requirements have profound implications on the scale and design of supporting computing
platforms that are clearly at odds with the centralized nature of cloud computing. Instead, edge intelligence necessitates a new operational layer that is designed for the characteristics of AI and edge computing systems. This layer weaves both cloud and federated
edge resources together using appropriate platform abstractions to form a distributed compute fabric. The main goals of this thesis are to examine the associated challenges, and to provide evidence for the efficacy of the idea. To further develop the concept of Edge Intelligence, we first discuss emerging technology trends at the intersection of edge computing and AI. We then examine scenarios where distributed computing resources can be federated and served as a utility, and argue that multi-purpose edge computer clusters will be a fundamental infrastructural component. Through experiments on prototypes we have built, we highlight the challenges faced by operational mechanisms such as load balancers in these environments. We extend the body of evaluation methods for edge computing, which is, compared to cloud computing research, still underdeveloped. Most
notably, we present a toolkit to generate synthetic infrastructure configurations and network topologies that are grounded in our examination of existing edge systems, and serve as input for a trace-driven simulator we have built. To create the distributed computing
fabric, we develop two orthogonal systems: an elastic message-oriented middleware, and a serverless edge computing platform. From a static centralized deployment in the cloud, we bootstrap a network of brokers that diffuse to the edge based on resource availability, and the number of clients and their proximity to edge resources. The system continuously optimizes communication latencies by monitoring client-broker proximity, and reconfiguring connections as necessary. Our serverless platform builds on existing container orchestration systems. The core is a custom scheduler that can make tradeoffs between data and computation movement, and is aware of workload heterogeneity and device capabilities such as GPUs. Furthermore, we demonstrate a method to automatically fine-tune scheduler parameters and optimize high-level operational goals.

Edge Computing / Cloud Computing / Artificial Intelligence / Serverless Computing / Middleware / Internet of Things

http://dx.doi.org/10.34726/hss.2021.93560