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Simulation-based optimization of acoustic liner design in a turbofan engine nacelle for noise reduction purposes can dramatically reduce the cost and time needed for experimental designs. Because uncertainties are inevitable in the design process, a stochastic optimization algorithm is posed based on the conditional value-at-risk measure so that an ideal acoustic liner impedance is determined that is robust in the presence of uncertainties. A parallel reduced-order modeling framework is developed that dramatically improves the computational efficiency of the stochastic optimization solver for a realistic nacelle geometry. The reduced stochastic optimization solver takes less than 500 seconds to execute. In addition, well-posedness and finite element error analyses of the state system and optimization problem are provided.

We extend the definition of a Stochastic Hybrid Automaton (SHA) to overcome limitations that make it difficult to use for on-line control. Since guard sets do not specify the exact event causing a transition, we introduce a clock structure (borrowed from timed automata), timer states, and guard functions that disambiguate how transitions occur. In the modified SHA, we formally show that every transition is associated with an explicit element of an underlying event set. This also makes it possible to uniformly treat all events observed on a sample path of a stochastic hybrid system and generalize the performance sensitivity estimators derived through Infinitesimal Perturbation Analysis (IPA). We eliminate the need for a case-by-case treatment of different event types and provide a unified set of matrix IPA equations. We illustrate our approach by revisiting an optimization problem for single node finite-capacity stochastic flow systems to obtain performance sensitivity estimates in this new setting.

A barrage relay network (BRN) is a broadcast oriented ad hoc network involving autonomous cooperative communication, a slotted time-division frame format, and a coarse slot-level synchronization. While inherently a broadcast protocol, BRNs can support unicast transmission by superimposing a plurality of controlled barrage regions (CBRs) onto the network. Within each CBRs, a new packet is injected by the unicast source during the first time slot of each new radio frame. When a CBRs is sufficiently long that a packet might not be able to reach the other end within a radio frame, multiple packets can be active at the same time via spatial pipelining, resulting in interference within the CBRs. In this paper, the dynamics of packet transmission within a CBRs is described as a Markov process, and the outage probability of each link within the CBRs is evaluated in closed form, thereby accounting for fading and co-channel interference. In order to account for the linkage between simultaneous active packets and their temporal correlation, a Viterbi-like algorithm is used. Using this accurate analytical framework, a line network is optimized, which identifies the code rate, the number of relays, and the length of a radio frame that maximizes the transport capacity.

A barrage relay network (BRN) is a broadcast oriented ad hoc network involving autonomous cooperative communication, a slotted time-division frame format, and a coarse slot-level synchronization. While inherently a broadcast protocol, BRNs can support unicast transmission by superimposing a plurality of controlled barrage regions (CBRs) onto the network. Within each CBRs, a new packet is injected by the unicast source during the first time slot of each new radio frame. When a CBRs is sufficiently long that a packet might not be able to reach the other end within a radio frame, multiple packets can be active at the same time via spatial pipelining, resulting in interference within the CBRs. In this paper, the dynamics of packet transmission within a CBRs is described as a Markov process, and the outage probability of each link within the CBRs is evaluated in closed form, thereby accounting for fading and co-channel interference. In order to account for the linkage between simultaneous active packets and their temporal correlation, a Viterbi-like algorithm is used. Using this accurate analytical framework, a line network is optimized, which identifies the code rate, the number of relays, and the length of a radio frame that maximizes the transport capacity.

Solid state drives (SSDs) have seen wide deployment in mobiles, desktops, and data centers due to their high I/O performance and low energy consumption. As SSDs write data out-of-place, garbage collection (GC) is required to erase and reclaim space with invalid data. However, GC poses additional writes that hinder the I/O performance, while SSD blocks can only endure a finite number of erasures. Thus, there is a performance-durability tradeoff on the design space of GC. To characterize the optimal tradeoff, this paper formulates an analytical model that explores the full optimal design space of any GC algorithm. We first present a stochastic Markov chain model that captures the I/O dynamics of large-scale SSDs, and adapt the mean-field approach to derive the asymptotic steady-state performance. We further prove the model convergence and generalize the model for all types of workload. Inspired by this model, we propose a randomized greedy algorithm (RGA) that can operate along the optimal tradeoff curve with a tunable parameter. Using trace-driven simulation on DiskSim with SSD add-ons, we demonstrate how RGA can be parameterized to realize the performance-durability tradeoff.