## 3 Overview of OMF Architecture
### OMF Experiment Workflow  
The workflow of a typical OMF experiment is illustrated in Figure 1, which captures the sequence of interactions between the EC, AM, and RC. This process begins with the researcher specifying the experiment parameters in an EDL file. The EDL file serves as a blueprint for the experiment, detailing the configuration of each node, the specificmeasurement tools required, and the actions that each node will undertake. Once this file is passed to the EC, it interprets the commands within the file, translating high-level experiment descriptions into executable instructions. Following interpretation, the EC communicates with the AM to verify the availability of all necessary resources. The AM checks the current status and configuration of each node designated for the experiment, ensuring that each node is loaded with the correct images and prepared for deployment. If a node requires initialization or reconfiguration, the AM initiates the setup process by sending specific commands to the RC on that node. This setup phase includes tasks such as loading operating system images, configuring network interfaces, and ensuring that each node meets the experiment's prerequisites. The AM then informs the EC once all nodes have been configured properly. Once the nodes are ready, the EC proceeds to start the experiment by sending execution commands to the RCs on each node, initiating the measurement applications or tools specified in the EDL file. At this stage, each RC controls the local operations of its node, executing the tasks assigned by the EC independently. This localized execution allows for parallel operations across multiple nodes, which is particularly valuable in experiments that involve distributed network measurements or simulations. By enabling each node to operate independently under the EC's coordination, the OMF architecture minimizes delays and optimizes the experiment's overall efficiency. During the experiment, data collected by measurement applications on each node is transmitted to the OMF measurement library (OML). The OML functions as an intermediary data handler, responsible for capturing, storing, and, if necessary, preprocessing measurement data. Preprocessing may involve performing calculations, such as sums, averages, or products, directly on the data before it is stored in the measurement collection service managed by the AM. This pre-processing capability reduces the need for additional data handling after the experiment, allowing researchers to access processed results almost immediately after the experiment concludes. The measurement collection service, managed by the AM, is central to data retrieval and analysis. By centralizing the collection of experiment data, the AM provides a streamlined system where results from each node are readily accessible in a unified format. The  
Figure 1: Overview of OMF architecture. AM's measurement collection service also simplifies post-experiment analysis by organizing the data based on each measurement's origin and parameters, enabling researchers to interpret results efficiently. This structure is particularly beneficial for experiments involving large amounts of data or multiple data types, as it provides an organized framework for managing complex datasets. In large-scale experiments, scalability is further supported by the modular nature of the EC-AM-RC structure, which allows for the addition of multiple ECs and AMs when required. This flexibility ensures that even extensive experiments involving hundreds of resources can maintain optimal performance and stability. By connecting additional ECs and AMs, the architecture adapts to increased demands without sacrificing efficiency or control, making it ideal for conducting high-impact network measurement experiments that simulate real-world conditions. Overall, the OMF experiment workflow, as demonstrated in Figure 1, exemplifies a structured yet adaptable process for conducting network experiments.