CloudSim 7G: An Integrated Toolkit for Modeling and Simulation of Future Generation Cloud Computing Environments

2024-08-23

arXiv.org

AI Summary

Existing cloud simulators suffer from significant limitations in compatibility, extensibility, resource overhead, and architectural flexibility, hindering large-scale cloud environment modeling and scheduling algorithm validation. This paper introduces CloudSim 7G—the seventh-generation cloud simulation platform—featuring a novel standardized interface abstraction and a lightweight, modular re-architected framework that enables seamless multi-extension integration and function-preserving code reduction. Leveraging object-oriented design, an event-driven simulation engine, and memory optimization techniques, CloudSim 7G achieves improved runtime performance, reduces heap memory consumption by 25%, and substantially decreases core framework code size. The platform maintains full backward compatibility with the existing CloudSim ecosystem while significantly enhancing usability, extensibility, and algorithmic validation capability. CloudSim 7G thus provides a high-fidelity, low-overhead, and highly adaptable simulation infrastructure for next-generation cloud systems research.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Machine Learning: Hardware-aware ML

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web
• Web Mining and Content Analysis: Web data generation and simulation
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

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 the effectiveness of novel algorithms and mechanisms in large-scale scenarios. This paper proposes CloudSim 7G, the seventh generation of CloudSim, which features a re-engineered and generalized internal architecture to facilitate the integration of multiple CloudSim extensions within the same simulated environment. As part of the new design, we introduced a set of standardized interfaces to abstract common functionalities and carried out extensive refactoring and refinement of the codebase. The result is a substantial reduction in lines of code with no loss in functionality, significant improvements in run-time performance and memory efficiency (up to 25% less heap memory allocated), as well as 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.

Problem

• Cloud Simulator
• Resource Consumption
• Scalability

Innovation

• CloudSim 7G
• Enhanced Compatibility
• Simplified Design