File Transfer
Dynamic Endpoint Transfer
Introducing Dynamic Endpoint Transfer, which applies the same Transfer Logic even as Endpoints keep changing.
File Transfer
Connect Files to the Endpoint You Need, Even as Destinations Keep Changing.
Traditional file transfer usually assumes fixed Sources and Targets such as Server A → Server B. But in Cloud, Kubernetes, AI Compute, Edge, and distributed Infrastructure, the Server, VM, Pod, GPU Compute, or Device that actually performs the work may be created, selected, and removed as needed.
- Data
- Transfer Logic
- AI Compute, Ready GPU
- Kubernetes, Selected Workload
- Edge Fleet, Available Devices
- Data Delivery
What matters in this environment is not rebuilding the Transfer every time a new Endpoint appears, but connecting the current Endpoint selected by the existing Infrastructure and Orchestrator to the same Data Movement. INNORIX Dynamic Endpoint Transfer connects Endpoint roles, Groups, status, and Lifecycle to Transfer Logic rather than relying on fixed IPs and Hostnames, preserving the same Transfer relationship even as Infrastructure changes.
File Transfer
Connect the Endpoint Selected at Runtime to the Transfer Instead of Using a Fixed Target.
Dynamic Endpoint Transfer is not simply a feature for environments with many Servers. The key is an environment where the actual Target that should receive the file is determined at runtime or changes continuously. In AI Training, the GPU Compute used may differ each time. A Kubernetes Workload may run on a new Pod or Node, Cloud Compute may be created on demand, and Edge Devices may connect at different times.
Where a traditional Transfer fixes a specific IP or Hostname as its Target, Dynamic Endpoint Transfer connects business-required roles and conditions, such as "the GPU Compute that will run this Training" or "an Edge Device currently in Ready status," to an actual Endpoint. Once the Infrastructure determines the actual Endpoint, INNORIX applies that decision to File Transfer, so the business Transfer Logic does not need to be rebuilt every time the Endpoint changes.
Real-World Scenario, Infrastructure Change, Transfer
Static
- Server A
- Server B — 192.168.10.24
Dynamic
- Dataset
- Target Condition — GPU Compute, Training, Ready
- Selected Endpoint — GPU-07
File Transfer
Configure Changing Infrastructure Using Endpoint Roles, Conditions, and Groups.
A Dynamic Endpoint is not simply a list of IP addresses. By connecting information managed by the actual Infrastructure, such as Role, Environment, Region, Workload, Project, and Status, to the Transfer relationship, Data Movement can be configured around the Endpoint conditions required by the business rather than around specific device names.
When multiple Compute Resources and Devices perform the same role, they can be organized into an Endpoint Group. Even as the actual Infrastructure inventory changes across GPU Pools, Compute Pools, Kubernetes Workloads, Edge Fleets, and Temporary Compute, the Transfer Logic remains centered on Groups and Conditions. This makes it possible to separate the actual Endpoint configuration from the business Data Movement relationship while connecting them as a single Transfer at runtime.
Endpoint Information, Example
Endpoint Group, Typical Configuration
- Dataset
- Role, Region, Workload, Status — GPU Compute, Seoul, Training, Ready
- GPU Compute Pool
- GPU-01, Ready
- GPU-02, Busy
- GPU-03, Ready
- GPU-04, Offline
- GPU-05, Ready
- Selected Endpoint
File Transfer
Existing Infrastructure Determines Endpoint Status, and INNORIX Executes That Decision as a Transfer.
In Dynamic Infrastructure, the existence of an Endpoint does not always mean that it is ready to perform a Transfer. Whether an Endpoint is Online, Ready to receive a Workload, or Busy with another task is determined by existing Infrastructure such as Kubernetes, GPU Schedulers, Cloud Orchestrators, and Device Management systems.
INNORIX does not Provision Compute or determine Workload Placement. Once the existing system decides where the work will run and which Endpoint is currently available, INNORIX connects the selected Endpoint and its status to the actual File Transfer and handles Data Delivery, Recovery, and Results. Its role is to preserve the decision-making authority of the Infrastructure while executing that decision as Data Movement.
Role, Responsible System, INNORIX
- Existing Infrastructure
- Select Endpoint
- Request Data Delivery
- INNORIX
- Move Files
- Transfer Result
File Transfer
Connect Data at the Points Where It Is Needed Across Workload and Endpoint Lifecycles.
Even when Compute is created automatically and a Workload is placed, the Dataset and Files required for the actual job still need to reach that Endpoint. In Cloud and AI Infrastructure, Compute may be a GPU, VM, or Pod created temporarily for a job rather than a permanent Server, making it important to connect the Timing of the Endpoint Lifecycle with the Transfer Lifecycle.
Once the existing Infrastructure creates the Endpoint and determines that it is Ready, INNORIX delivers the required Data. After the existing Workload performs Processing or Training, INNORIX can collect Results and Checkpoints back to Persistent Storage. INNORIX does not create or terminate Endpoints; it executes Transfers when Data is needed within the Lifecycle and returns completion Results.
- Endpoint Created
- Endpoint Ready
- INNORIX Data Delivery
- Existing Workload — Training / Processing
- INNORIX Result Collection
- Persistent Storage
- Endpoint Released
File Transfer
Invoke Dynamic Transfers from Existing Orchestrators and APIs.
Dynamic Endpoint Transfer does not add a new Orchestrator. Instead, it allows the Data Delivery required within an existing Workflow to be invoked as an external Transfer Operation. After the Orchestrator selects an Endpoint and requests a Transfer, INNORIX executes the actual Data Movement and returns Status and Results, allowing the existing Workflow to proceed to the next stage based on that result.
With this structure, Workflow Logic and Scheduling remain in the existing system while only File Transfer is connected as an independent Data Movement Layer. Even when the Endpoint changes every time, the invocation and result-handling methods remain consistent, allowing organizations to add Transfer Capability to Dynamic Infrastructure without redefining existing Automation.
Interface, Role
- Existing Orchestrator
- Selected Endpoint
- Request Transfer
- INNORIX
- Data Movement
- Transfer Result
- Continue Workload
File Transfer
Keep Transfer Logic Centered on Roles and Conditions as the Number of Endpoints Grows.
With fixed Targets, adding a new Server or Device can also increase the number of Transfer Configurations such as Source → Server A and Source → Server B. The Dynamic Endpoint Model separates the actual Endpoint inventory from the business Transfer Logic and defines the relationship around what roles and conditions an Endpoint must satisfy to receive Data from the Source.
As a result, the same Transfer Logic can be reused when new Compute Resources or Devices are added to an Endpoint Group or existing Endpoints are replaced. Transfers begin with ready Endpoints, unavailable Endpoints wait according to their status, and Resume and Recovery are applied when problems occur during Transfer, preserving the same relationship through final Data Delivery even as Infrastructure scale and status change.
Fixed
- Source
- Server A
- Server B
- Server C
- Server D
- Server E
Dynamic
- Source
- Target Condition
- Endpoint Group
- A, Ready
- B, Busy
- C, Ready
- D, Offline
- E, Ready
- Available Endpoint
File Transfer
Extend Dynamic Endpoints to Distribution and Collection Using the Same Model.
Dynamic Endpoints are not limited to a single Source and a single Target. The same Endpoint Model can be applied not only to 1:1 Transfers that deliver a Dataset to one selected GPU, but also to Distribution across a GPU Pool or Available Edge Fleet, and Collection of Results from multiple GPUs or data from Factory Devices into Persistent Storage and Data Centers.
Even when the Source or Target changes at runtime, the basic completion criteria for Distribution and Collection remain the same. By combining Dynamic Endpoints with Large File, High-Volume, Resume, and Recovery Capabilities, INNORIX connects Delivery and Result Collection through one Data Movement Model even as Source and Target relationships change.
Pattern, Dynamic Transfer
Dynamic Distribution
- Dataset
- GPU Pool
- A
- B
- C
Dynamic Collection
- A
- B
- C
- Results
- Object Storage
File Transfer
Connect Cloud, Kubernetes, AI, and Edge Through One Endpoint Model.
Dynamic Endpoint is not a concept limited to a single type of Infrastructure. In Cloud environments, VM and Compute Instances can be Transfer Endpoints; in Kubernetes, Pods, Nodes, and Workload Endpoints; in AI, GPUs and Training or Inference Compute; and in Edge environments, Gateways, Factory Devices, and Branch Devices.
Even in Multi-Region environments where Workloads run in different locations depending on Region and Environment, Transfer Logic can connect the Endpoint and Data Path selected by the existing Infrastructure instead of being fixed to one Server in a specific Region. Regardless of the actual Infrastructure type, the same Endpoint → Condition → Transfer → Result Data Movement Model applies.
Environment, Endpoint Example
- Data
- Cloud Compute
- Kubernetes Workloads
- GPU Compute
- Data Center
- Edge Fleet
- Remote Devices
- Selected Region
- Transfer Result
File Transfer
Operate Dynamic Transfers by Separating Endpoint, Policy, Flow, and Run.
As Dynamic Infrastructure becomes more complex, it becomes increasingly important to separate where something runs from what is being moved. An Endpoint represents the actual Server, Compute, or Device. Endpoint Groups and Conditions express which destinations are appropriate for the business, while a Transfer defines the Files and Datasets to move.
Policy defines the operational rules applied to a Transfer, Flow represents the business relationships before and after the Transfer, and Run represents the actual execution result. Separating these roles allows the purpose and operating rules of a Transfer to remain consistent even as the Endpoint inventory continues to change, while the selected Endpoint and File Movement are connected as a single Run at execution time.
Component, Role
- Endpoint / Group
- Condition
- Transfer + Policy
- Flow
- RUN — Selected Endpoint, Files, Result
File Transfer
AI Data Delivery and Dynamic Endpoint Transfer Handle Changes in Data and Infrastructure Respectively.
The two products are closely connected, but they answer different core questions. AI Data Delivery focuses on what needs to be delivered, such as Datasets, Models, Checkpoints, and Results, while Dynamic Endpoint Transfer connects where it needs to be delivered now, such as a GPU, Compute Resource, Server, or Device.
For example, in AI Training, AI Data Delivery handles the actual File Movement of Datasets and Model Weights, while Dynamic Endpoint Transfer connects the GPU or Temporary Compute selected by the Scheduler as the current Transfer Target. After the Workload completes, Checkpoints and Results can be collected back into Persistent Storage, allowing Data Lifecycle and Endpoint Lifecycle changes to be connected through one Data Movement without overlapping the two roles.
AI Data Delivery, Dynamic Endpoint Transfer
What → Where
- What — Dataset, Model, Checkpoint, Result
- Where — Selected GPU, Available Compute, Temporary Endpoint
- Data Movement
File Transfer
Operate Transfer Relationships and Results on One Platform Even as Endpoints Change.
The Endpoint used for execution may change every time, but Transfer Operations and Results do not need to become scattered across multiple systems. On the INNORIX Platform, actual Endpoints can be viewed in Devices, Transfer Logic configured in Flows, the progress of executed Dynamic Transfers checked in Runs and Monitoring, and final Results verified through File Status and Receipt.
Even as Infrastructure expands from a small Compute Pool to multi-Region Cloud, Kubernetes, and Edge Fleets, the fundamental structure remains the same: connect the selected and ready Endpoint to the actual File Transfer, recover the Transfer when required, and return completion Results through the same Platform Model.
Operation, What You Verify
INNORIX Platform
- Control, Flow, Monitoring
- GPU Pool
- Cloud Compute
- Edge Fleet
- Data Movement
- Runs, Receipt
File Transfer
Data Movement Follows as Infrastructure Changes.
Even when GPU Compute changes every time, Kubernetes Workloads run on new Endpoints, and Edge Devices connect at different times, Transfer Logic does not need to be fixed to a single Server.
Once the existing Infrastructure and Orchestrator determine where the work will run, INNORIX delivers the required files to that Endpoint and connects Results generated after the Workload back to the required Storage and Systems.
Let Infrastructure Be Infrastructure. Let INNORIX Handle Data Movement.
Considering transfer to changing Endpoints?
We'll help you review the Dynamic Transfer setup you need across Cloud, Kubernetes, and distributed environments.
- Review of your Dynamic Infrastructure environment
- Proposal for an Endpoint and Group connection method
- Guidance on integrating with your existing Orchestrator