CG-TTC:在点对点云联盟市场中用于资源共享的基于联盟游戏的方法

IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Parisa Khoshdel , Saeid Abrishami , Mehdi Feizi , Faeze Ramezani
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引用次数: 0

摘要

在当今快节奏的技术环境中,云联盟已经成为一种突破性的解决方案,改变了企业和组织利用云计算的方式。云联合涉及多个云服务提供商(csp)之间的协作,允许它们在统一的环境中共享计算资源。然而,在云联盟中创建功能性市场是一项重大挑战,要求提供商制定最佳策略以实现盈利能力最大化。本文解决了形成有效联盟的挑战,将其概念化为具有可转移效用的合作博弈。建议的框架侧重于将剩余资源的提供者聚集在一起形成联盟,从而满足其他提供者的资源需求。为了解决这个问题,引入了联盟博弈-顶级交易周期(CG-TTC)机制,这是一种分散的方法,使用著名的顶级交易周期分配算法来解决联盟形成的挑战。重要的是,CG-TTC解决方案已被证明是帕累托最优和个体稳定的,确保供应商没有动机离开他们的联盟。实证结果表明,与云联盟形成机制(CFFM)相比,CG-TTC机制将盈利能力提高了34%,公平性提高了32%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CG-TTC: A coalitional game-based approach for resource sharing in a peer-to-peer cloud federation market
In today’s fast-paced technological landscape, cloud federation has emerged as a groundbreaking solution, transforming the way businesses and organizations utilize cloud computing. Cloud federation involves collaboration among multiple Cloud Service Providers (CSPs), allowing them to share computational resources within a unified environment. However, creating a functional marketplace within a cloud federation poses a significant challenge, requiring providers to develop optimal strategies to maximize profitability. This paper addresses the challenge of forming effective federations, conceptualized as a cooperative game with transferable utility. The proposed framework focuses on providers with surplus resources coming together to form coalitions, thereby meeting the resource needs of other providers. To address this, the Coalitional Game-Top Trading Cycles (CG-TTC) mechanism has been introduced—a decentralized approach that uses the well-known Top Trading Cycle assignment algorithm to tackle coalition formation challenges. Importantly, the CG-TTC solution has been shown to be both Pareto optimal and individually stable, ensuring that providers have no incentive to leave their coalitions. Empirical results show that the CG-TTC mechanism improves profitability by up to 34% and enhances fairness by up to 32% compared to the Cloud Federation Formation Mechanism (CFFM).
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来源期刊
CiteScore
19.90
自引率
2.70%
发文量
376
审稿时长
10.6 months
期刊介绍: Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.
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