系统内部资源负荷趋势评估的自适应技术

S. Casolari, M. Colajanni, Stefania Tosi
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引用次数: 4

摘要

为了避免性能下降和系统过载,现代分布式系统需要几个运行时管理决策,包括负载平衡和负载共享、过载和准入控制、作业调度和请求重定向。由于现代系统的外部工作负载和内部资源行为是高度复杂和可变的,自适应技术需要对系统行为有一个稳定的认识。在本文中,我们提出了一个趋势模型,保证了负载感知决策算法的鲁棒解释。在Web集群中的各种实验结果表明,所提出的模型和算法保证了更好的负载稳定性,并减少了用户体验到的响应时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Self-Adaptive Techniques for the Load Trend Evaluation of Internal System Resources
Modern distributed systems that have to avoid performance degradation and system overload require several runtime management decisions for load balancing and load sharing, overload and admission control, job dispatching and request redirection. As the external workload and the internal resource behavior of the modern system is highly complex and variable, self-adaptive techniques require a stable vision of the system behavior. In this paper we propose a trend model that guarantees a robust interpretation for load-aware decision algorithms. Various experimental results in a Web cluster demonstrate that the proposed models and algorithms guarantee better stability of the load and a reduction of the response time experienced by the users.
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