CloudSim 7G: An Integrated Toolkit for Modeling and Simulation of Future Generation Cloud Computing Environments
<|reference_start|>CloudSim 7G: An Integrated Toolkit for Modeling and Simulation of Future Generation Cloud Computing Environments: Cloud Computing has established itself as an efficient and cost-effective paradigm for the execution of web-based applications, and scientific workloads, that need elasticity and on-demand scalability capabilities. However, the evaluation of novel resource provisioning and management techniques is a major challenge due to the complexity of large-scale data centers. Therefore, Cloud simulators are an essential tool for academic and industrial researchers, to investigate on the effectiveness of novel algorithms and mechanisms in large-scale scenarios. This article unveils CloudSim7G, the seventh generation of CloudSim, one of the first simulators specialized in evaluating resource management techniques for Cloud infrastructures. In particular, CloudSim7G features a re-engineered and generalized internal architecture to facilitate the integration of multiple CloudSim extensions, which were previously available independently and often had compatibility issues, within the same simulated environment. Such architectural change is coupled with an extensive refactoring and refinement of the codebase, leading to the removal of over 13,000 lines of code without loss of functionality. As a result, CloudSim7G delivers significantly better performance in both run-time and total memory allocated (up to ~20% less heap memory allocated), along with increased flexibility, ease-of-use, and extensibility of the framework. These improvements benefit not only CloudSim developers but also researchers and practitioners using the framework for modeling and simulating next-generation cloud computing environments.<|reference_end|>
@article{andreoli2024cloudsim,
    title={CloudSim 7G: An Integrated Toolkit for Modeling and Simulation of Future
  Generation Cloud Computing Environments},
    author={Remo Andreoli, Jie Zhao, Tommaso Cucinotta and Rajkumar Buyya},
    journal={arXiv preprint arXiv:2408.13386},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.13386},
    primaryClass={cs.DC}
}