Dataset Description This dataset contains the experimental results used to evaluate centralized and decentralized orchestration strategies in an EDGELESS cluster. The experiments compare a baseline configuration with a single orchestration domain against a target decentralized deployment with multiple orchestration domains. Experimental Scenarios Two system configurations were evaluated: Baseline (Single-Domain):An EDGELESS cluster with a single orchestration domain. All scheduling and resource management decisions are taken by a single ε-ORC instance. In this configuration, the ε-CON has no decision-making autonomy. Decentralized (Multi-Domain):An EDGELESS cluster composed of six orchestration domains. Resource management decisions are performed at two levels: The ε-ORC manages local resources within each domain using fine-grained measurements. The ε-CON coordinates function instances across domains using aggregated measurements and operates at a longer time scale. All other experimental conditions (benchmarking methodology, workload, applications, node resources, and runtime environments) were kept identical across scenarios. Application Workflow The evaluated application consists of a serverless workflow processing image streams: file-pusher (resource):Generates one base64-encoded image every 100 ms (10 images/s) from a local dataset of road images. flow-control (WebAssembly, stateless):Limits the number of in-flight images to 5, with a reset timeout after 50 input images. Excess images are dropped. image-scale (WebAssembly, stateless):Rescales images to a maximum resolution of 676×380 pixels while preserving the aspect ratio. obj-detect (resource):Performs object detection using YOLO (Ultralytics container optimized for NVIDIA AGX Orin). Detected object metadata (position, size, type) are added to each message. obj_track (WebAssembly, stateful):Tracks objects across the previous 5 frames, adding bounding boxes and trajectories. http-poster (resource):Decodes images and sends them to an external HTTP service. Viewer (external service):A Rust-based web application that logs received images with timestamps. Workload Configuration The workload was generated using the edgeless_benchmark utility with the following parameters: Experiment duration: 1800 seconds. Workflow duration: Poisson-distributed with mean 60 s, 120 s, or 240 s. Workflow interarrival time: Poisson-distributed with mean 2 s. This configuration results in approximately 30, 60, or 120 concurrently active applications. Testbed Experiments were conducted at the CNR-IIT facilities (Ubiquitous Internet research group). Control Plane: ε-CON deployed in a dedicated container within a Proxmox cluster (not restarted across experiments). ε-ORC services deployed in dedicated containers with Redis proxy and dataset dumping enabled. edgeless_benchmark executed in the same Proxmox environment. Compute Nodes: Raspberry Pi 5. NVIDIA AGX Orin 64 GB. Runtime: RUST_WASM. Resource providers: file-pusher and http-poster. obj-detect deployed only on Orin devices. Refresh interval to ε-ORC: 10 s. Performance sampling enabled. Networking: Cisco Layer-2 switches. 1 GbE links for Raspberry Pi 5 nodes. 10 GbE links for AGX Orin nodes. Power Monitoring: Active power consumption measured via Raritan PDUs with per-outlet monitoring. Dataset Contents The dataset includes: Raw experimental logs collected from orchestration services and nodes. Performance samples and resource utilization metrics. Power consumption measurements. Data required to reproduce the plots presented in the associated publication. Plots can be reproduced using the analyze.py script available in the plots/ directory of the corresponding GitHub repository. Acknowledgments The authors acknowledge the technical support of the Computer and Communication Networks Technology Unit at CNR-IIT for managing the Proxmox cluster infrastructure and for setting up the network connectivity of the Raspberry Pi and NVIDIA AGX Orin devices used in the experiments.