云计算中使用ACO元启发式算法的多目标、分散式动态虚拟机整合
Multi-objective, Decentralized Dynamic Virtual Machine Consolidation using ACO Metaheuristic in Computing Clouds
摘要：
计算资源利用不足和高功耗是云资源管理领域的两个主要挑战。本文通过在大规模虚拟化数据中心环境中进行离线、迁移影响感知、多目标动态虚拟机（VM）整合来应对这些挑战。该问题被公式化为NP-难离散组合优化问题，同时具有最小化资源浪费、功耗和相关联的VM迁移开销的目标。由于通过虚拟机实时迁移进行的动态虚拟机整合对托管应用程序性能和数据中心组件有负面影响，因此提出了一种虚拟机实时迁移开销估计技术，该技术考虑了实用的迁移参数和开销因素。为了解决可扩展性问题，提出了一个分层的，分散的动态VM整合框架，有助于本地化与迁移相关的网络流量，降低网络成本。此外，一个多目标，动态VM合并算法，提出了利用蚁群优化（ACO）元启发式，与建议的VM迁移开销估计技术的集成。全面的性能评估表明，所提出的动态VM整合方法在所有性能指标上都超过了最先进的离线、迁移感知的动态VM整合算法，其总体功耗降低了47%，资源浪费降低了64%，迁移开销降低了83%。
英文摘要：
Underutilization of computing resources and high power consumption are two primary challenges in the domain of Cloud resource management. This paper deals with these challenges through offline, migration impact-aware, multi-objective dynamic Virtual Machine (VM) consolidation in the context of large-scale virtualized data center environments. The problem is formulated as an NP-hard discrete combinatorial optimization problem with simultaneous objectives of minimizing resource wastage, power consumption, and the associated VM migration overhead. Since dynamic VM consolidation through VM live migrations have negative impacts on hosted applications performance and data center components, a VM live migration overhead estimation technique is proposed, which takes into account pragmatic migration parameters and overhead factors. In order to tackle scalability issues, a hierarchical, decentralized dynamic VM consolidation framework is presented that helps to localize migration-related network traffic and reduce network cost. Moreover, a multi-objective, dynamic VM consolidation algorithm is proposed by utilizing the Ant Colony Optimization (ACO) metaheuristic, with integration of the proposed VM migration overhead estimation technique. Comprehensive performance evaluation makes it evident that the proposed dynamic VM consolidation approach outpaces the state-of-the-art offline, migration-aware dynamic VM consolidation algorithm across all performance metrics by reducing the overall power consumption by up to 47%, resource wastage by up to 64%, and migration overhead by up to 83%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
