Using Application Benefit for Proactive Resource Allocation in Asynchronous Real-Time Distributed Systems
Hegazy, Tamir A.
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This thesis presents two proactive resource allocation algorithms, RBA* and OBA, for asynchronous real-time distributed systems. The algorithms consider an application model where timeliness requirements are expressed using Jensen's benefit functions and propose adaptation functions to describe anticipated workload for future time intervals. Furthermore, an adaptation model is considered where processes are replicated for sharing workload increases. A real-time Ethernet system model is considered where message collisions are resolved. Given such models, the objective is to maximize aggregate application benefit and minimize aggregate missed deadline ratio. Since determining the optimal allocation is computationally intractable, the algorithms heuristically compute the allocation so that it is as "close" as possible to the optimal allocation. While RBA* analyzes process response times to determine the allocation, OBA analyzes processor overloads to compute the decision in a much faster way. RBA* incurs a quadratic amortized complexity in terms of subtask arrivals for the most computationally intensive component when DASA is used as the underlying process-scheduling algorithm, whereas OBA incurs a logarithmic amortized complexity for the corresponding component. To study how different process-scheduling and message-scheduling algorithms affect the performance of the algorithms and to compare their performances, benchmark-driven experiments were conducted. The experimental results reveal that RBA* produces higher aggregate benefit and lower missed deadline ratio when DASA is used for process scheduling and message scheduling. Furthermore, it is observed that RBA* produces higher aggregate benefit and lower missed deadline ratio than OBA, confirming the intuition that accurate response time analysis can lead to better results.
- Masters Theses