Type of Document Master's Thesis Author Devarasetty, Ravi Kiran Author's Email Address email@example.com URN etd-02132001-142541 Title Heuristic Algorithms for Adaptive Resource Management of Periodic Tasks in Soft Real-Time Distributed Systems Degree Master of Science Department Electrical and Computer Engineering Advisory Committee
Advisor Name Title Ravindran, Binoy Committee Chair Kachroo, Pushkin Committee Member Midkiff, Scott F. Committee Member Keywords
- Adaptive Resource Management
- Prediction-based algorithms
- Distributed Real-time Systems
- Heuristic-based algorithms
- Dynamic Real-time Systems
Date of Defense 2001-02-06 Availability unrestricted AbstractDynamic 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.
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