Cyclic debugging requires repeatable executions. As non-deterministic or real-time systems typically do not have the potential to provide this, special methods are required. One such method is replay, a process that requires monitoring of a running system and logging of the data produced by that monitoring. We shall discuss the process of preparing the replay, a part of the process that has not been very well described before.
Zambonelli and Netzer @cite_2 proposed a method that, by taking on-line decisions on whether to log or not to log a monitored event, deviates from the strict FIFO-solution. However, sometimes logging will debit the system with a jitter in the execution time, and so will also the algorithm it self. As larger jitter will force more extensive efforts for validation @cite_6 , an increase in jitter is counterproductive to the validation effort.
@cite_6 @cite_2
2143295017 2110140003
The enactment of business processes is generally supported by information systems that record data about each process execution (a.k.a. case). This data can be analyzed via a family of methods broadly known as process mining. Predictive process monitoring is a process mining technique concerned with predicting how running (uncompleted) cases will unfold up to their completion. In this paper, we propose a predictive process monitoring framework for estimating the probability that a given predicate will be fulfilled upon completion of a running case. The framework takes into account both the sequence of events observed in the current trace, as well as data attributes associated to these events. The prediction problem is approached in two phases. First, prefixes of previous (completed) cases are clustered according to control flow information. Secondly, a classifier is built for each cluster using event data attributes to discriminate between cases that lead to a fulfillment of the predicate under examination and cases that lead to a violation within the cluster. At runtime, a prediction is made on a running case by mapping it to a cluster and applying the corresponding classifier. The framework has been implemented in the ProM toolset and validated on a log pertaining to the treatment of cancer patients in a large hospital. This paper presents an analytical model to compute the average service time and jitter experienced by a packet when transmitted in a saturated IEEE 802.11 ad hoc network. In contrast to traditional work in the literature, in which a distribution is usually fitted or assumed, we use a bottom-up approach and build the first two moments of the service time based on the IEEE 802.11 binary exponential backoff algorithm and the events underneath its operation. Our model is general enough to be applied to any type of IEEE 802.11 wireless ad hoc network where the channel state probabilities driving a node's backoff operation are known. We apply our model to saturated single-hop ad hoc networks under ideal channel conditions. We validate our model through extensive simulations and conduct a performance evaluation of a node's average service time and jitter for both direct sequence and frequency-hopping spread spectrum physical layers.