Trace-based load characterization for generating performancesoftware models

Performance models of software designs can give early warnings of problems such as resource saturation or excessive delays. However models are seldom used because of the considerable effort needed to construct them. The ANGIOTRACE^TM was developed to gather the necessary information from an executable design and develop a model in an automated fashion. It applies to distributed and concurrent software with synchronous (send-reply or RPC) communications, developing a layered queuing network model. The trace-based load characterization (TLC) technique presented here extends the ANGIOTRACE^TM to handle software with both synchronous and asynchronous interactions. TLC also detects interactions which are effectively synchronous or partly-synchronous (forwarding) but are built up from asynchronous messages. These patterns occur in telephony software and in other systems. The TLC technique can be applied throughout the software life-cycle, even after deployment     

Logical clock requirements for reverse engineering scenarios from adistributed system

To reverse engineer scenarios from event traces, one must infer causal relationships between events. The inferences are usually based on a trace with sequence numbers or timestamps corresponding to some kind of logical clock. In practice, there is an explosion of potentially causal relationships in the trace, which limits one's ability to extract scenarios. This work defines a more parsimonious form of causality called scenario causality that concentrates on certain major causal relationships and ignores more subtle, potentially causal links. The influence of an event is restricted to the particular scenario it is part of. An event which is not a message reception is defined to be caused by the previous event in the same software object, while a message reception is caused by a sending event in another object. The events are ordered to form a scenario event graph where typed nodes are events and the typed edges are certain causal relationships. Intuitively, we might say that most logical clocks, which identify events which "happened before" a given event and, thus, are potentially causal, give an upper bound on the set of causal events; scenario causality identifies a lower bound. The much smaller lower bound set makes it possible to reverse engineer and automate the analysis of scenarios