A Multidimensional Virtual Resource Allocation Framework With Energy-Aware Physical Resource Mapping for Green Cloud Computing

IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Ayşenur Uslu, Ali Haydar Özer
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Abstract

Cloud computing has seen a surge in demand, driven by its scalability and cost efficiency. However, the growing energy consumption of data centers poses significant environmental challenges. This study introduces a multidimensional resource allocation model designed to allocate and place virtual resources in an energy-efficient manner using a combinatorial auction approach. Unlike current approaches, which rely on predefined virtual resources, this model allows users to request virtual resources with specific features and capacities tailored to their workflows. Furthermore, it incorporates a flexible bidding language that supports simultaneous requests for multiple resources using logical AND/OR relations. The model accommodates various data centers, allowing users to indicate their preferred locations. Through a combinatorial optimization problem, the model identifies the most resource-efficient allocations and the most energy-efficient placements. This study provides the mathematical definition of the model and the formulation of its optimization problem. Given the complexity of this problem, it explores several heuristic methods, including ant colony optimization and genetic algorithms. A test case generator is developed to simulate real-life scenarios. The effectiveness of the model and the proposed heuristic solutions is assessed through various experiments, demonstrating that these methods can achieve near-optimal solutions within reasonable timeframes.

Abstract Image

面向绿色云计算的具有能量感知物理资源映射的多维虚拟资源分配框架
受其可扩展性和成本效率的推动,云计算的需求激增。然而,数据中心日益增长的能源消耗带来了重大的环境挑战。本文介绍了一种多维资源配置模型,该模型旨在利用组合拍卖的方法以一种节能的方式分配和放置虚拟资源。与当前依赖于预定义虚拟资源的方法不同,该模型允许用户请求具有特定功能和能力的虚拟资源,以适应其工作流程。此外,它还结合了一种灵活的投标语言,支持使用逻辑AND/OR关系同时请求多个资源。该模型可容纳各种数据中心,允许用户指示他们的首选位置。通过组合优化问题,该模型确定了最具资源效率的配置和最具能源效率的配置。本文给出了该模型的数学定义及其优化问题的表述。考虑到这个问题的复杂性,它探讨了几种启发式方法,包括蚁群优化和遗传算法。开发了一个测试用例生成器来模拟真实的场景。通过各种实验评估了模型和提出的启发式解决方案的有效性,表明这些方法可以在合理的时间框架内获得接近最优的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Concurrency and Computation-Practice & Experience
Concurrency and Computation-Practice & Experience 工程技术-计算机:理论方法
CiteScore
5.00
自引率
10.00%
发文量
664
审稿时长
9.6 months
期刊介绍: Concurrency and Computation: Practice and Experience (CCPE) publishes high-quality, original research papers, and authoritative research review papers, in the overlapping fields of: Parallel and distributed computing; High-performance computing; Computational and data science; Artificial intelligence and machine learning; Big data applications, algorithms, and systems; Network science; Ontologies and semantics; Security and privacy; Cloud/edge/fog computing; Green computing; and Quantum computing.
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