English

High fusion computers: The IoTs, edges, data centers, and humans-in-the-loop as a computer

Distributed, Parallel, and Cluster Computing 2022-12-02 v1 Networking and Internet Architecture

Abstract

Emerging and future applications rely heavily upon systems consisting of Internet of Things (IoT), edges, data centers, and humans-in-the-loop. Significantly different from warehouse-scale computers that serve independent concurrent user requests, this new class of computer systems directly interacts with the physical world, considering humans an essential part and performing safety-critical and mission-critical operations; their computations have intertwined dependencies between not only adjacent execution loops but also actions or decisions triggered by IoTs, edge, datacenters, or humans-in-the-loop; the systems must first satisfy the accuracy metric in predicting, interpreting, or taking action before meeting the performance goal under different cases. This article argues we need a paradigm shift to reconstruct the IoTs, edges, data centers, and humans-in-the-loop as a computer rather than a distributed system. We coin a new term, high fusion computers (HFCs), to describe this class of systems. The fusion in the term has two implications: fusing IoTs, edges, data centers, and humans-in-the-loop as a computer, fusing the physical and digital worlds through HFC systems. HFC is a pivotal case of the open-source computer systems initiative. We laid out the challenges, plan, and call for uniting our community's wisdom and actions to address the HFC challenges. Everything, including the source code, will be publicly available from the project homepage: https://www.computercouncil.org/HFC/.

Keywords

Cite

@article{arxiv.2212.00721,
  title  = {High fusion computers: The IoTs, edges, data centers, and humans-in-the-loop as a computer},
  author = {Wanling Gao and Lei Wang and Mingyu Chen and Jin Xiong and Chunjie Luo and Wenli Zhang and Yunyou Huang and Weiping Li and Guoxin Kang and Chen Zheng and Biwei Xie and Shaopeng Dai and Qian He and Hainan Ye and Yungang Bao and Jianfeng Zhan},
  journal= {arXiv preprint arXiv:2212.00721},
  year   = {2022}
}

Comments

This paper has been published in BenchCouncil Transactions on Benchmarks, Standards and Evaluations (TBench). Link: https://www.sciencedirect.com/science/article/pii/S277248592200062X

R2 v1 2026-06-28T07:19:44.426Z