Det(Com)2: Deterministic Communication and Computation Integration Toward AIGC Services

As an emerging intelligence paradigm, artificial intelligence generated content (AIGC) is envisioned to be a key technique for Internet of intelligence, which inevitably puts forward higher requirements for the network capability from both the forwarding and computing perspectives. This article proposes a novel deterministic communication and computation integration architecture, that is, Dot(Com) 2 . for future networks to effectively support large AI model services such as distributed training, rapid deployment, and collaborative inference. Deep reinforcement learning-based solutions are developed to achieve cross-domain computation resource orchestration and deterministic transmission scheduling. The proposed learning-based solutions can efficiently schedule computing tasks of large AI models among multiple geographically dispersed computing domains while guaranteeing bounded latency and near-zero packet loss, thus facilitating integrated resource management and supporting large AI model services across their life cycles. Finally, we present a case study on communication and computation integration and discuss open research issues.