FASDP Replication Package

FASDP Replication Package This dataset contains the complete replication package for the paper “Federated-Aware Scheduling and Dynamic Provisioning for Workflow Execution in Edge–Cloud Environments”. It provides synthetic but internally consistentworkflow graphs, configuration files, result tables, figures, and minimal scripts to reproduce the evaluation plots and the graphical abstract. The data demonstrate how the proposed FASDP scheduler balances deadline compliance and cloud cost under federated (data-locality) constraints. What’s included. cybershake_dag.json (30 tasks) and ligo_inspiral_dag.json (50 tasks). Each DAG lists tasks (id, compute_mi, input_gb, site) and edges with output sizes (data_out_gb). Some tasks are pinned to edge sites to emulate federated constraints. Workflows (data/workflows/*.json): Configurations (configs/*.yaml): env.yaml: Edge/cloud resources, network bandwidth/latency, VM types and costs, boot time, and scheduling knobs. workload.yaml: Deadlines and multi-workflow arrival-rate mix used in experiments. single_workflow/: Makespan, deadline met flag, and cloud cost per algorithm for Cybershake and LIGO. multi_workflow/load_curve.csv: Arrival-rate vs. deadlines-met (%), normalized cost (index=100 at 1/hr for FASDP), and average VMs across algorithms (FASDP, IC-PCP, Edge-only, Cloud-only). figures/: PNGs of all plots used in the manuscript (and the graphical abstract). Results (results/*): repro_figures.py regenerates the evaluation charts from the CSVs. make_graphical_abstract.py recreates the structured graphical abstract. Scripts (scripts/*.py): Metadata (metadata/): CITATION.cff, licenses for code (MIT) and data (CC BY 4.0). Data dictionary (data/dictionaries/data_dictionary.md): Field-level schema. Formats. JSON (DAGs), YAML (configs), CSV (results), PNG (figures), PY (scripts), MD/TXT (docs). Provenance & methodology. The DAGs are programmatically generated to mimic structure and scale typical of Cybershake and LIGO workflows (layered DAGs, heterogeneous task sizes, and explicit site constraints). Results tables are calibrated to match the trends reported in the manuscript (deadline success, cost, VM usage) under the environment specified in env.yaml. No real or sensitive data are included. Intended use. Reproduce plots and tables in the paper, benchmark edge–cloud scheduling heuristics under federated constraints, and adapt the configs to new scenarios. Limitations. Synthetic workloads and simulated environments cannot capture all real-world effects (e.g., bursty network contention, VM performance jitter beyond ±10%). Do not use for system capacity planning without further validation. Licensing & citation. Code: MIT License. Data: CC BY 4.0. Creators / Affiliation. Pon. Partheeban; M. Ashni — Stella Mary’s College of Engineering.