Abstract

In-Network Computing (INC) refers to the process that enables the distribution of computing tasks across the network instead of computing on servers outside the network. Advances in programmable hardware allow computations directly within network devices, such as switches and Smart Network Interface Cards (smart NICs), as data passes through them, and thereby reduces network congestion, minimizes reliance on distant cloud servers, and improves latency.

The growing demand for ultra-low delays, high bandwidth, and the ability to dynamically synchronize data streams in emerging applications, particularly Holographic-Type Communication application, is pushing the limits of current network infrastructures, with INC emerging as a promising solution as it aims to meet these stringent requirements. In addition, efficient provisioning of INC requires a comprehensive understanding of INC components, including their specifications, configuration information, and requirements. Nevertheless, an architecture with a description and discovery model offers a structured and standardized framework to represent INC components, defining their functionality and characteristics, and retrieving the most pertinent INC components according to the user requirements.

This thesis proposes a novel architecture for describing and discovering the most relevant INC components based on user preferences, specifically tailored for holographic-type application. The contribution of this work is threefold. First, we introduce the In-Network Computing Ontology (INCO), a domain-independent, ontology-based description model that provides a semantic representation of INC components, facilitating their discovery from a centralized repository. The description model covers both the functional and non-functional specifications of INC components and consists of two parts: a generic description for INC components and an extension detailing four specific INC components (i.e. encoder, decoder, transcoder, and renderer)- essential for our holographic application use case. Second, we present a semantic matchmaking algorithm that leverages the INCO model to automatically identify and select the most appropriate INC components based on user requests and preferences. Lastly, we validate the proposed approach through experimental simulations, demonstrating the algorithm’s effectiveness in terms of response time and consistency. Response time was measured based on two criteria: query complexity and the number of retrieved instances. The simulation results indicate that response time fluctuates with increasing query complexity, while it remains comparatively stable as the number of retrieved instances grows.