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AirDnD -- Asynchronous In-Range Dynamic and Distributed Network Orchestration Framework

Malsha Ashani Mahawatta Dona, Christian Berger, Yinan Yu

TL;DR

AirDnD addresses the challenge of underutilized resources across distributed edge devices and mobile data ponds by enabling spontaneous, asynchronous task offloading within a dynamic mesh. The approach introduces three interrelated models—Network Description, Task Description, and Data Description—for describing the evolving network, offloadable tasks, and data requirements. A pragmatic scenario, 'looking around the corner' for autonomous vehicles at intersections, guides evaluation with a focus on data locality and reduced cellular traffic. If successful, the framework supports AI-enhanced, serverless-like orchestration at the edge and advances distributed NFV concepts for edge ecosystems.

Abstract

The increasing usage of IoT devices has generated an extensive volume of data which resulted in the establishment of data centers with well-structured computing infrastructure. Reducing underutilized resources of such data centers can be achieved by monitoring the tasks and offloading them across various compute units. This approach can also be used in mini mobile data ponds generated by edge devices and smart vehicles. This research aims to improve and utilize the usage of computing resources in distributed edge devices by forming a dynamic mesh network. The nodes in the mesh network shall share their computing tasks with another node that possesses unused computing resources. This proposed method ensures the minimization of data transfer between entities. The proposed AirDnD vision will be applied to a practical scenario relevant to an autonomous vehicle that approaches an intersection commonly known as ``looking around the corner'' in related literature, collecting essential computational results from nearby vehicles to enhance its perception. The proposed solution consists of three models that transform growing amounts of geographically distributed edge devices into a living organism.

AirDnD -- Asynchronous In-Range Dynamic and Distributed Network Orchestration Framework

TL;DR

AirDnD addresses the challenge of underutilized resources across distributed edge devices and mobile data ponds by enabling spontaneous, asynchronous task offloading within a dynamic mesh. The approach introduces three interrelated models—Network Description, Task Description, and Data Description—for describing the evolving network, offloadable tasks, and data requirements. A pragmatic scenario, 'looking around the corner' for autonomous vehicles at intersections, guides evaluation with a focus on data locality and reduced cellular traffic. If successful, the framework supports AI-enhanced, serverless-like orchestration at the edge and advances distributed NFV concepts for edge ecosystems.

Abstract

The increasing usage of IoT devices has generated an extensive volume of data which resulted in the establishment of data centers with well-structured computing infrastructure. Reducing underutilized resources of such data centers can be achieved by monitoring the tasks and offloading them across various compute units. This approach can also be used in mini mobile data ponds generated by edge devices and smart vehicles. This research aims to improve and utilize the usage of computing resources in distributed edge devices by forming a dynamic mesh network. The nodes in the mesh network shall share their computing tasks with another node that possesses unused computing resources. This proposed method ensures the minimization of data transfer between entities. The proposed AirDnD vision will be applied to a practical scenario relevant to an autonomous vehicle that approaches an intersection commonly known as ``looking around the corner'' in related literature, collecting essential computational results from nearby vehicles to enhance its perception. The proposed solution consists of three models that transform growing amounts of geographically distributed edge devices into a living organism.
Paper Structure (6 sections, 1 figure)

This paper contains 6 sections, 1 figure.

Table of Contents

  1. Introduction
  2. Motivation
  3. Research Plan
  4. Research Methods
  5. Initial Results
  6. Challenges

Figures (1)

  • Figure 1: The proposed vision of AirDnD