Overview
This repository contains a dataset for vehicle requests in an edge computing environment. Each request is randomly assigned values for:
- CPU Utilization
- Request Length
- Request Size
Additionally, a probability distribution figure is provided to illustrate the distribution of these generated values.
Research Context
This dataset is associated with the research paper "Energy-SLA-aware genetic algorithm for edge–cloud integrated computation offloading in vehicular networks" (https://doi.org/10.1016/j.future.2022.04.009). The study focuses on optimizing energy consumption in edge-cloud computing platforms while maintaining applications' Service Level Agreements (SLAs) in vehicular networks.
The main contributions of the research are:
- A novel algorithm is proposed to solve the problem of offloading in vehicular networks that minimizes the total energy consumption of vehicles’ requests while adhering to latency and processing time constraints.
- This optimization NP-hard problem is solved by applying an adaptive penalty function to our evolutionary-based algorithm, to obtain a near-optimal solution in polynomial time.
- The performance of the proposed algorithm is evaluated and compared with three algorithms in terms of total energy consumption and the percentage of SLA violations. The algorithms are evaluated using different constraint requirements and vehicular network characteristics.
Usage
This dataset can be used for:
- Research on edge computing and offloading strategies
- Machine learning experiments in vehicular networks
Citation
If you use this dataset in your research, please cite the associated paper:
Materwala, Huned, Leila Ismail, Raed M. Shubair, and Rajkumar Buyya. "Energy-SLA-aware genetic algorithm for edge–cloud integrated computation offloading in vehicular networks." Future Generation Computer Systems 135 (2022): 205-222.