Abstract: Systems and methods are described for diagnosing behavior of software components in an application server. The application server can comprise a plurality of components that process incoming requests. A diagnostics advisor can be deployed with the application server and can determine an efficiency and/or inefficiency of each of the components of the application server or other middleware system. The efficiency determined by computing a ratio of a number of requests that completed execution in the component during a particular sampling time period to the number of requests that were received by the component during the sampling time period. The inefficiency is the inverse of efficiency, i.e. it is a ratio of the number of requests that are still being executed by the one or more components at the end of the sampling time period to the number of requests that were received by the one or more components during the sampling time period.

Abstract: A mechanism for efficient collection of data is described for runtime middleware environments. Two frequencies are used, a collection frequency (CF) to collect the data and an aggregation frequency (AF) to aggregate and persist the data in a repository. The collection cycle is a shorter time interval than the aggregation cycle. An agent residing in the container periodically collects a set of data upon every collection cycle from the components of the middleware system and caches the set of data locally. Upon every aggregation cycle, the agent applies an aggregation function to the collected set of data and persists the set of data into a repository after the aggregation function has been applied. The aggregation function is such that it resulting data represents the behavior of the runtime environment in the total duration of the aggregation cycle.

Abstract: A method of determining a root cause of a performance problem is provided. The method comprises analyzing a plurality of performance indicators/metrics in a first time period and determining that at least one performance indicators/metric is exhibiting abnormal behavior. The method further comprises analyzing the plurality of performance indicators/metrics over a second time period, the second time period is longer than the first time period, and determining trend information for each performance indicators/metric over the second time period. The method further comprises correlating the trend information for each performance indicators/metric with performance problem information stored in a knowledge base, identifying a potential cause of the abnormal behavior based on the correlation, and alerting a user of the potential cause.