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<p>The emerging need for dynamically scheduled real-time systems requires methods for handling transient overloads. Current methods have in common that they deal with transient overloads as they occur, which gives the real-time system limited time to react to the overload. In this work we enable new approaches to overload management. Our work shows that artificial neural networks (ANNs) can predict future transient overloads. This way the real-time system can prepare for a transient overload before it actually occurs. Even though the artificial neural network is not yet integrated into any system, the results show that ANNs are able to satisfactory distinguish different workload scenarios into those that cause future overloads from those that do not. Two ANN architectures have been evaluated, one standard feed-forward ANN and one recurrent ANN. These ANNs were trained and tested on sporadic workloads with different average arrival rates. At best the ANNs are able to predict up to 85% of the transient overloads in the test workload, while causing around 10% false alarms.</p>

The emerging need for dynamically scheduled real-time systems requires methods for handling transient overloads. Current methods have in common that they deal with transient overloads as they occur, which gives the real-time system limited time to react to the overload. In this work we enable new approaches to overload management. Our work shows that artificial neural networks (ANNs) can predict future transient overloads. This way the real-time system can prepare for a transient overload before it actually occurs. Even though the artificial neural network is not yet integrated into any system, the results show that ANNs are able to satisfactory distinguish different workload scenarios into those that cause future overloads from those that do not. Two ANN architectures have been evaluated, one standard feed-forward ANN and one recurrent ANN. These ANNs were trained and tested on sporadic workloads with different average arrival rates. At best the ANNs are able to predict up to 85% of the transient overloads in the test workload, while causing around 10% false alarms.

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Dynamic real-time distributed systems are characterized by significant run-time uncertainties at the mission and system levels. Typically, processing and communication latencies in such systems do not have known upper bounds and event and task arrivals and failure occurrences are non-deterministically distributed. This thesis proposes adaptive resource management heuristic techniques for periodic tasks in dynamic real-time distributed systems with the (soft real-time) objective of minimizing missed deadline ratios. The proposed resource management techniques continuously monitor the application tasks at run-time for adherence to the desired real-time requirements, detects timing failures or trends for impending failures (due to workload fluctuations), and dynamically allocate resources by replicating subtasks of application tasks for load sharing. We present "predictive" resource allocation algorithms that determine the number of subtask replicas that are required for adapting the application to a given workload situation using statistical regression theory. The algorithms use regression equations that forecast subtask timeliness as a function of external load parameters such as number of sensor reports and internal resource load parameters such as CPU utilization. The regression equations are determined off-line and on-line from application profiles that are collected off-line and on-line, respectively. To evaluate the performance of the predictive algorithms, we consider algorithms that determine the number of subtask replicas using empirically determined functions. The empirical functions compute the number of replicas as a function of the rate of change in the application workload during a "window" of past task periods. We implemented the resource management algorithms as part of a middleware infrastructure and measured the performance of the algorithms using a real-time benchmark. The experimental results indicate that the predictive, regression theory-based algorithms generally produce lower missed deadline ratios than the empirical strategies under the workload conditions that were studied. / Master of Science