Advanced pattern recognition for detection of complex software aging phenomena in online transaction processing servers
Advanced pattern recognition for detection of complex software aging phenomena in online transaction processing servers
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DOI：
10.1109/dsn.2002.1028933
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发表时间：
2002-06
期刊：
影响因子：
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通讯作者：
Karen J. Cassidy;K. Gross;A. Malekpour
中科院分区：
文献类型：
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作者：
Karen J. Cassidy;K. Gross;A. Malekpour
最近研究了软件老化现象。一种特别复杂的类型是大型OLTP服务器中的共享内存池闩锁争论。闩锁争夺开始会导致严重的性能降解，直到触发DBMS共享内存池的手动恢复活力。自动化复兴的常规方法因闩锁争夺而失败了，因为尚未确定可以监视的单个资源指标以提醒这种复杂机制的发作。当前的调查探讨了应用高级模式识别方法的可行性，该方法体现在商业上可用的设备状况监测系统（SmartSignal ECM/SPL Trade/）中，以主动对软件衰老故障进行主动报表。从20-60秒收集的大型OLTP服务器监视100个数据信号。在5个月内的间隔。结果表明，13个变量始终偏离闩锁事件之前的正常操作，最多可预警2小时。
Software aging phenomena have been recently studied; one particularly complex type is shared memory pool latch contention in large OLTP servers. Latch contention onset leads to severe performance degradation until a manual rejuvenation of the DBMS shared memory pool is triggered. Conventional approaches to automated rejuvenation have failed for latch contention because no single resource metric has been identified that can be monitored to alert the onset of this complex mechanism. The current investigation explores the feasibility of applying an advanced pattern recognition method that is embodied in a commercially available equipment condition monitoring system (SmartSignal eCM/spl trade/) for proactive annunciation of software-aging faults. One hundred data signals are monitored from a large OLTP server, collected at 20-60 sec. intervals over a 5-month period. Results show 13 variables consistently deviate from normal operation prior to a latch event, providing up to 2 hours early warning.