Overview
AI-powered Managed File Transfer for Google Cloud (GCS, Filestore, Storage Transfer Service)
gcp-mft-ai is an open-source, production-grade Python library that transforms traditional file transfers on Google Cloud Platform (GCP) into intelligent, ML-optimized, secure operations.
It automates, predicts, protects, and optimizes file movement across:
- Google Cloud Storage (GCS)
- Cloud Filestore (NFS-based File System)
- Storage Transfer Service (STS API)
- Designed for large-scale enterprises, DevOps engineers, and AI/ML pipelines, gcp-mft-ai brings the future of AI-enhanced Managed File Transfer (MFT) into your cloud workflows.
Features
- Upload/download large files intelligently
- AES-256 encryption support
- Predict transfer time with ML
- Optimize best transfer windows
- Detect anomalies in transfer logs
Core Capabilities
Multi-Service MFT: GCS bucket transfers, Filestore filesystem moves, GCP Storage Transfer Service orchestration
Encryption at Source: AES-256-GCM authenticated encryption (optional per transfer)
ML-Based Transfer Time Prediction: Predict upload/download times using Linear Regression or Random Forest models
Anomaly Detection: Identify unusual slowdowns, spikes, or transfer errors automatically using Isolation Forest
Transfer Window Optimization: Find the best network window (hour of day) to minimize congestion and maximize throughput
Resilient Transfers: Automatic retries, resumable uploads for large objects, GCP API throttling handling
Config-Driven Automation: Manage all settings via simple YAML or JSON configuration files
Internal Architecture
GCS Transfers: Built atop the google-cloud-storage SDK for resumable, secure, and reliable object transfers.
Filestore Transfers: Abstracted over NFS filesystem mounts, allowing simple shutil-based secure moves between instances or buckets.
Storage Transfer Service API: Dynamically creates and monitors cloud-to-cloud transfer jobs via authenticated REST API calls (fully IAM compliant).
Prediction Engine:
i) Trained on historical transfer data (file_size_mb, transfer_time_sec)supports
ii) Linear Regression (lightweight, fast)
iii) Random Forest (higher-accuracy, non-linear patterns)
Anomaly Detection: Isolation Forest model isolates unusual file size vs time behavior — flagging spikes, failures, and risks early.
Encryption Layer: AES-256 encryption with GCM mode ensures data integrity and confidentiality before movement.
Optimization Layer: Hour-by-hour analysis of historical transfer speeds to recommend the best operational windows.
Security-First Design
Encryption: Native AES-256-GCM encryption/decryption for any file before or after cloud storage.
Token Management: Secure OAuth2 token usage for Storage Transfer Service API access.
No plaintext secrets: Designed for service account usage via environment or config.
Usage Overview
Upload to GCS: upload_to_gcs(source_path, bucket, destination_path)
Download from GCS: download_from_gcs(blob_name, bucket, destination_path)
Upload to Filestore: upload_to_filestore(source_path, mount_point, relative_path)
Launch Storage Transfer Job: launch_storage_transfer_job(source_bucket, destination_bucket, project_id)
Predict Transfer Time: predict_transfer_time(file_size_mb)
Detect Anomalies: detect_transfer_anomalies(csv_log_path)
Find Best Transfer Window: find_best_transfer_window(csv_log_path)
Real-World Use Cases
Media & Entertainment: Migrate large UHD videos to GCS for editing pipelines
AI/ML Model Training: Transfer terabyte datasets securely and predictably to TPU training zones
Backup & Disaster Recovery: Automate and encrypt cross-region backup uploads with anomaly alerting
Healthcare & Finance: Securely move critical records across cloud environments with end-to-end encryption
Retail Analytics: Optimize massive log file ingestion pipelines to GCP data lakes
Technology Stack
- Python 3.7+
- Google Cloud SDKs (google-cloud-storage, requests)
- Cryptography (AES-256-GCM secure cipher)
- scikit-learn (ML Models: Linear Regression, Random Forest, Isolation Forest)
- pandas (Data preparation for ML)
- pyyaml (Config loading)
- joblib (Model persistence)
MIT License
Author: Raghava Chellu
MIT License is freely usable for academic, personal, and commercial projects.
Installation
pip install gcp-mft-ai
Deployment Readiness
PyPI-ready (setup.py, pyproject.toml)
Full unit testing (unittest framework)
Full documentation (README.md, examples/)
Cloud deployment friendly (Docker/CI/CD pipelines)
Conclusion
Traditional file transfers are simple. Modern file transfers must be intelligent, secure, and predictive. gcp-mft-ai brings cutting-edge AI and cloud-native automation to Managed File Transfer on Google Cloud securing your data, optimizing your operations, and helping you move smarter, stronger, and faster.
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