Comprehensive Overview of Computing Power Networks: Definitions, Features, Architecture, and Future Directions

Data‑driven economies generate massive data that require coordinated cloud, edge, and terminal computing resources, giving rise to the concept of a computing‑power network—a new information infrastructure that dynamically allocates and schedules compute, storage, and network resources across distributed locations.

What is computing power? It is the raw processing capability of devices, from smartphones to supercomputers, measured in units such as MIPS, FLOPS, and Hash/s. High‑performance computing (HPC) and general‑purpose computing represent two major categories, while AI‑driven workloads increasingly dominate demand.

Key network features for computing‑power networks include:

Elasticity – adjustable bandwidth and duration to match bursty workloads.

Agility – seamless, on‑demand access to compute resources without user concern for underlying placement.

Lossless transmission – ultra‑low packet loss to avoid severe compute degradation.

Security – end‑to‑end data protection, isolation, and threat mitigation.

Perception – differentiated SLA guarantees and performance monitoring for diverse applications.

Visualization – a digital network map enabling real‑time topology and path tracing.

These capabilities are enabled by technologies such as IPv6‑based SRv6 for ubiquitous, agile provisioning, network slicing for deterministic, lossless, and secure isolation, and IFIT‑based flow‑monitoring for real‑time diagnostics.

Architectural layers consist of cloud, edge, and terminal compute nodes, coordinated by a “brain” that performs global perception, cross‑domain scheduling, and intelligent decision‑making, delivering functions like computing‑aware routing, elastic scheduling, and blockchain‑backed compute trading.

Why a network is essential – without a unified fabric, distributed compute resources cannot be shared, scheduled, or utilized efficiently. The network must provide professional, elastic, and collaborative services to support diverse workloads.

Future directions span:

Ubiquitous AI‑enabled compute for smart societies.

Data‑centric heterogeneous architectures (GPU, FPGA, AI‑chips, DPU/IPU) for efficient processing.

All‑optical transport networks for high‑capacity, low‑latency links.

Deterministic low‑latency networks for industrial digitalization.

Deep integration of compute and network (NFV/SDN) to enable in‑network processing.

Brain‑driven multi‑factor orchestration across cloud, edge, data, AI, security, and blockchain.

Trusted, shared compute marketplaces powered by blockchain.

Green, low‑carbon designs for sustainable digital infrastructure.

Numerous white‑papers, reports, and downloadable resources are referenced for deeper study.