How Baidu Cloud Storage Solves the Four Big Challenges of the ABC Era

In the so‑called ABC era—where AI (A), big data (B), and ubiquitous cloud adoption (C) dominate—enterprise storage faces four major challenges: massive data volumes, cost‑effectiveness, stability, and diverse usage scenarios.

Four Core Challenges

Massive data: Enterprises now store video, audio, and other large media, causing explosive growth that demands efficient cloud migration and scalable physical capacity.

Cost‑effectiveness: Data is a valuable asset, yet customers expect lower storage costs even as volumes increase from tens to hundreds of petabytes.

Stability: Distributed systems must guarantee high reliability and provide disaster‑recovery capabilities for millions of customers.

Diversity: Modern workloads span big‑data analytics, AI training, hybrid‑cloud platforms, and more, requiring a flexible mix of storage products.

Baidu Canghai Storage Portfolio Overview

Baidu Canghai provides a matrix of storage services: Object Storage (BOS), Block Storage (CDS), File Storage (CFS), Parallel File Storage (PFS), and specialized solutions such as RapidFS for data‑lake acceleration, CloudFlow for data migration, and BSG storage gateway for hybrid‑cloud integration.

Data Migration and Multi‑Cloud Transfer (Section 3.1)

Three migration methods are offered:

Disk‑array hybrid cloud, moving data via physical devices ("Moonlight Box").

Dedicated line service between customer IDC and Baidu Cloud for high‑speed transfer.

Cross‑cloud migration using CloudFlow, which visualizes and automates one‑click data sync between source and destination clouds.

For incremental cross‑cloud sync, CloudFlow supports event‑driven mirroring that pulls new data from the source when accessed.

Intelligent Lifecycle Management (Section 3.2)

BOS now stores exabytes of data across tens of thousands of servers. To reduce costs, tiered storage classes—Standard (multi‑AZ), Infrequent Access, Cold, and Archive—are available. Users can define lifecycle rules, e.g., move objects from Standard to Infrequent Access after 30 days and to Archive after 60 days, cutting storage fees to as low as 18 % of the standard rate.

Rules also support upward migration: if cold‑storage objects become hot, they can be automatically promoted back to higher‑performance tiers.

Multi‑Level Disaster Recovery and Reliability (Section 3.3)

BOS guarantees 12 9’s (99.9999999999 %) durability using erasure‑coding across multiple AZs, and 99.95 %–99.99 % availability per AZ (99.9995 % in practice). The architecture includes four‑layer load balancing, cluster‑wide redundancy, and physical‑machine‑level failover.

Additional disaster‑recovery options include:

Physical‑machine redundancy with automatic failover.

Multi‑AZ storage types that replicate data across data centers.

Cross‑region backup across Beijing, Suzhou, Guangzhou, Baoding, etc.

Data‑mirroring back‑source for automatic retrieval from backup when primary data is missing.

Integrated Multi‑Product Data Flow (Section 3.4)

Complex workloads require a combination of products:

Data‑lake acceleration: RapidFS sits atop BOS, providing a high‑speed cache for big‑data and AI workloads.

Hybrid‑cloud storage: The BSG storage gateway bridges on‑premise IDC storage with BOS, enabling seamless read/write across local and cloud environments.

AI HPC storage: Parallel File System (PFS) with POSIX‑compatible acceleration layers supports massive read‑heavy AI training workloads.

Typical use cases include video streaming platforms (e.g., iQIYI) that store raw media in Standard storage, then automatically tier down to Cold or Archive as access frequency drops, saving significant costs while still supporting CDN distribution.

Conclusion

By addressing the four ABC‑era challenges through scalable architecture, tiered lifecycle policies, robust multi‑AZ reliability, and a suite of integrated products, Baidu Canghai storage enables enterprises to migrate massive datasets to the cloud, control costs, ensure data safety, and support diverse workloads ranging from big‑data analytics to AI training.