基于自律计算的多数据点闭值检测方法

自律计算在实施过程中通常采用阂值来标记性能故障。阂值是系统的性能计数器的边界值，系统自动检测这些边界值。若某个设备的性能指标达到这些边界值，则表示系统的某台设备出现了性能故障，自律系统的自主管理器便会主动采取策略来修复性能故障，从而保证系统一直处于稳定的工作状态。目前，自律计算领域欠缺对于系统性能故障的研究。在研究自律计算自我监视的基础上，提出了一种多数据点的阂值检测方法，即通过多次检测阂值的越界和恢复来判断系统是否出现性能故障，以保证检测的有效性，为系统采取策略修复故障提供有利的依据。

Multiple Data Threshold Detecting Method Based on Autonomic Computing

Autonomic computing system often uses threshold to mark performance faults. Threshold is the boundary value of performance counter,which will be detected automatically by the system. When the performance counter of one of the devices reaches the threshold, it is considered that performance fault occurs on one of the devices. Then the autonomic manager will actively select policies to recover these faults,which therefore makes the whole system to maintain normal state. Nowadays,it lacks of research on system performance faults in autonomic computing field. On the basis of studying the self-monitoring of autonomic computing system, this paper proposed a multiple data threshold detecting method,by detecting the exceeding and recovering of the threshold in multiple times, system can effectively judge whether the performance fault occurs. This method assures the detecting accuracy,which provides the effective basis to recover fault for autonomic computing system.