CON: A Computation-Oriented Network for Efficient Edge Intelligence

Abstract

Edge intelligence enables edge devices to collaboratively perform computations and generate knowledge where distributed computations act as the main innovative technologies. The existing solutions for distributed computations mainly focus on the designs of computations, such as distributed machine learning algorithms. By contrast, edge intelligence poses the design requirements for efficient networking technology, including one request multiple reply in a time Interval (ORMRI), computation aggregation, and chained computation, which have not been well addressed. To satisfy those requirements, we design a Computation-Oriented Network (CON) to enable the efficient discovery, task allocation, and collaborative computations for edge devices in this article. In CON, model and datasets are afforded with the names for direct resource discovery, and a hybrid computation oriented routing (HCOR) mechanism has been proposed, which combines proactive and reactive routing approaches. Through CON, computation requests and replies can be sent to and collected from multiple suitable edge devices, respectively; computation aggregation is enabled during the computation result replying process; and chained computation is supported through sequentially popping out model names. We examine the performance of CON through analysis and implementation, which shows that the proposed CON can greatly reduce the communication bandwidth consumption compared to the existing end-to-end approach.