If you need to transfer files between multiple devices, INNORIX Platform should be the first option that comes to mind.#
Hundreds of devices simultaneously generate thousands of transfer events, dramatically increasing complexity.#
| Event classification | Event details | Example scenario | Purpose of processing |
|---|---|---|---|
| Device connection status | Online/Offline Detection | 15 out of 800 devices disconnected | Route reselection and transfer stability |
| Device connection delay | Delay Increase Detection | Response time for a specific device increased by 500ms | transfer detour and backoff handling |
| Storage space monitoring | Remaining Capacity Check | Remaining capacity below 3% | transfer interruption and replacement with other devices |
| Storage space spikes/decrease | Abnormal Capacity Changes | Sudden drop of 60GB per day | Data loss suspicion and notification |
| Transfer speed tracking | Average/Instantaneous Speed Monitoring | Specific section: 30MB/s → 3MB/s | Bottleneck detection |
| Transfer rate/progress | Transfer Rate and ETA Calculation | 220GB out of 500GB completed | Monitoring of large-scale transfer progress |
| Transfer failure detection | Failure/Timeout Detection | 2 out of 20 failed | Automatic retransfer |
| File integrity check | Hash Verification (MD5/SHA) | SHA256 mismatch | Recovery of damaged files |
| Chunk-based transfer errors | Chunk Retransfer Events | 80 out of 100,000 chunks missing | Partial retransfer |
| Network congestion detection | Packet Loss/Congestion | Loss rate spiked by 10% | QoS adjustment and path change |
| I/O performance monitoring | Disk Read/Write Speed | I/O wait increased by 40% | Scheduling adjustment |
| CPU/memory load | Resource Overload Detection | CPU usage continued at 98% | Work distribution |
| Policy-based control | Priority/Bandwidth Limits | Priority given to bulk backups | Resource optimization |
| Hub/waypoint selection | Automatic Relay Node Changes | Changed from Ubuntu → AWS S3 → CentOS | Securing optimal paths |
| Device group status | Group Status Summary | 8 out of 100 Hub2 devices failed | Group transfer control |
| Scheduled events | Scheduled Task Execution/Stop | Automatic collection at 3:00 AM | Non-disruptive operation |
| Security events | Authentication Failure/Token Expiration | 1 S3 authentication error | Automatic reauthentication |
| Device metadata changes | Device IP/Name Change Detection | Automatic change of device IP | Connection reconfiguration |
| Failure notifications/follow-up actions | Slack/Webhook transfer | Immediate notification of failure | Increased administrator response speed |
| Operation log recording | Automatic transfer/Error/Performance Log Storage | 3 million logs generated daily | Problem analysis and audit trail |
INNORIX Platform processes thousands of these events in parallel in real time, ensuring congestion-free transfer.#
INNORIX Platform automatically analyzes device performance and available bandwidth to instantly configure the optimal path and number of simultaneous transfers.#
| Server device types | Performance | Bandwidth | Target devices | INNORIX Platform |
|---|---|---|---|---|
| AWS S3 | Managed | 10Gbps | 300 devices | 160-200 simultaneous transfers |
| MS Azure Blob | Managed | 10Gbps | 300 devices | 150-200 simultaneous transfers |
| Dell Object Storage | Managed | 10Gbps | 300 devices | 130-180 simultaneous transfers |
| Windows Server | 64 core | 1Gbps | 300 devices | 40-100 simultaneous transfers |
| RHEL Linux | 32 core | 10Gbps | 300 devices | 20-35 simultaneous transfers |
| Ubuntu Linux | 16 core | 1Gbps | 300 devices | 10-25 simultaneous transfers |
| CentOS Linux | 8 core | 1Gbps | 300 devices | 8-15 simultaneous transfers |
| Rocky Linux | 16 core | 1Gbps | 300 devices | 10-20 simultaneous transfers |
| Debian Linux | 8 core | 1Gbps | 300 devices | 8-15 simultaneous transfers |
| RHEL Linux Enterprise | 32 core | 1Gbps | 300 devices | 20-35 simultaneous transfers |
| macOS | 8 core | 1Gbps | 300 devices | 5-12 simultaneous transfers |
As a result, file distribution and collection to large-scale devices are completed with the fastest and most accuracy.#
Even if conditions change during a transfer, INNORIX Platform immediately readjusts priorities and transfer paths to maintain flow.#
| Collection path | Example scenario | INNORIX Platform | Result |
|---|---|---|---|
| 500 Ubuntu servers → Hub (20 Rocky servers) → AWS S3 | Five Hub Rocky hubs temporarily down | All transfers are automatically queued, with healthy nodes prioritized. | No dropouts, minimized delay |
| 300 Windows servers → 10 RHEL servers → 100 CentOS servers → Final S3 | RHEL hub CPU load at 100% | Transfer tasks are automatically reordered (from low capacity to high capacity). | Full file sequential resumption |
| 200 macOS servers → 30 Ubuntu servers → Debian central server | Network latency increased to 800ms | Transfers to devices with high latency are automatically suspended, with fast devices restarted first. | 30% reduction in total transfer time |
| 1,000 CentOS servers → 50 Fedora servers → AWS S3 | Three out of 15 Fedora hubs failed | Work on failed nodes is automatically distributed and queues are reordered. | 0 failed files |
| 400 Debian servers → 5 Rocky servers → RHEL servers | Hub storage exceeded 95% | Transfers to the affected devices are immediately paused, with spare devices reordered. | Maintained transfer stability |
| 800 Windows servers → 100 Ubuntu servers (Hub1) → 20 Ubuntu servers (Hub2) → AWS S3 | Hub1 network disconnected | All flows are automatically queued and rerouted to the Hub2 direct path. | Continued transfers without interruption |
| 120 macOS servers → AWS S3 → 2,000 CentOS servers | S3 API throttling occurred during large transfers | Queues are sorted until the API is restored, with each chunk retransferred. | 100% integrity |
| 900 RHEL servers → 40 Debian servers → Ubuntu final server | Seven out of 40 Debian hubs experienced I/O bottlenecks | Tasks on the affected node are moved to a lower priority, with the fastest node being resumed first. | Automatically optimized throughput |
| 300 Rocky servers → 10 Windows servers → AWS S3 | Three Windows hubs experienced memory shortages | Memory thresholds are automatically detected and queues are reordered. | Continued stable transfers |
| 600 Fedora servers → 20 CentOS servers → RHEL servers | RHEL server failed (rebooted) | All tasks are suspended until the server is restored, with automatic restart upon reconnection. | Retransfers without interruption |
All files are transferred completely, without any errors, and any file corruption or loss is automatically verified.#
INNORIX Platform's high-performance file transfer engine dramatically reduces the time and costs wasted by traditional methods.#
| Industries | Cases | INNORIX Platform |
|---|---|---|
| Government/Public Institutions | Automatically collect files from 1,000 regional offices to a central server. | Shortened collection time, 0% dropout rate, and reduced operating costs |
| Public Transportation | Automatically transfer status files from city bus stops to a central server. | Increased maintenance efficiency, real-time data reflection |
| Smart Factories | Automatically collect process data from 300 production devices. | Increased quality analysis speed, eliminating process delays |
| Medical Institutions (PACS) | Automatically collect CT/MRI/X-ray images from 1,200 hospitals. | Transfer time reduced from 24 hours to 1 hour, 0% video dropout rate, and improved treatment efficiency |
| Manufacturing (Process Control) | Automatically distribute process control files/patches to 500 devices. | Eliminated manpower, distribution time reduced from 6 hours to 30 minutes, and reduced process errors |
| Finance/ATMs | Security and settlement logs from 4,000 ATMs/unmanned counters. | Shortened security incident response time, reduced branch operating costs |
| Energy (Solar/Wind/ESS) | Automatically collect data from 2,500 power generation facilities. | Stable collection even on unstable networks, reducing maintenance costs |
| Broadcasting/Media | Automatically upload files to H.Q. from nationwide relay stations and branches. | Continuous transfer of 10TB video, improving transfer stability |
| Logistics/Distribution | Automatically collect RFID/scan data from 5,000 delivery points. | Improved delivery tracking accuracy, parallel processing of millions of files |
| Construction/Heavy Industry | Collect vibration and status logs from 2,000 remote heavy devices. | Stable transfer even on LTE and satellite networks, reducing maintenance costs |