Some oil and gas companies are drilling and developing fields in the Arctic Ocean, which has an environment with sea ice called ice floes. These companies must protect their platforms from ice floe collisions. One proposal is to use a system that consists of autonomous underwater vehicles (AUVs) and docking stations. The AUVs measure the under-water topography of the ice floes, while the docking stations launch the AUVs and recharge their batteries. Given resource constraints, we optimize quantities and locations for the docking stations and the AUVs, as well as the AUV scheduling policies, in order to provide the maximum protection level for the platform. We first use an queueing approach to model the problem as a queueing system with abandonments, with the objective to minimize the abandonment probability. Both M/M/k+M and M/G/k+G queueing approximations are applied and we also develop a detailed simulation model based on the queueing approximation. In a complementary approach, we model the system using a multi-stage stochastic facility location problem in order to optimize the docking station locations, the AUV allocations, and the scheduling policies of the AUVs. A two-stage stochastic facility location problem and several efficient online scheduling heuristics are developed to provide lower bounds and upper bounds for the multi-stage model, and also to solve large-scale instances of the optimization model. Even though the model is motivated by an oil industry project, most of the modeling and optimization methods apply more broadly to any radial detection problems with queueing dynamics. / text

A scheduler, as a central components of a computing site, aggregates computing resources and is responsible to distribute the incoming load (jobs) between the resources. Under such an environment, the optimum performance of the system against the service level agreement (SLA) based workloads, can be achieved by calculating the priority of SLA bound jobs using integrated heuristic. The SLA defines the service obligations and expectations to use the computational resources. The integrated heuristic is the combination of different SLA terms. It combines the SLA terms with a specific weight for each term. Theweights are computed by applying parameter sweep technique in order to obtain the best schedule for the optimum performance of the system under the workload. The sweepingof parameters on the integrated heuristic observed to be computationally expensive. The integrated heuristic becomes more expensive if no value of the computed weights result in improvement in performance with the resulting schedule. Hence, instead of obtaining optimum performance it incurs computation cost in such situations. Therefore, there is a need of detection of situations where the integrated heuristic can be exploited beneficially. For that reason, in this thesis we propose a metric based on the concept of utilization, to evaluate the SLA based parallel workloads of independent jobs to detect any impact of integrated heuristic on the workload.