This paper studies the continuous-time intruder isolation problem on a general road network graph under delayed information scenario. Several Unattended Ground Sensors (UGSs) are pre-installed along certain edges of the graph for detecting intruder motion and recording the detection time. Measurements of a UGS can only be obtained when the UAV is within its communication range. The goal of this paper is to find the optimal path for the UAV to follow in order to capture the intruder within the shortest time, based on the delayed information from the visited UGSs. We propose an unfolding strategy to transform the road network graph to a decision tree incorporating delayed measurement information. Based on the decision tree, both optimal and suboptimal min-max solutions are developed. Several interesting properties of the corresponding optimal value function are also derived. Numerical simulations based on a real road network are presented to demonstrate the effectiveness of the proposed strategies.
Multi-stage user-facing applications on GPUs are widely-used nowadays, and are often implemented to be microservices. Prior research works are not applicable to ensuring QoS of GPU-based microservices due to the di erent communication patterns and shared resource contentions. We propose Astraea to manage GPU microservices considering the above factors. In Astraea, a microservice deployment policy is used to maximize the supported peak service load while ensuring the required QoS. To adaptively switch the communication methods between microservices according to di erent deployments, we propose an auto-scaling GPU communication framework. The framework automatically scales based on the currently used hardware topology and microservice location, and adopts global memory-based techniques to reduce intra-GPU communication. Astraea increases the supported peak load by up to 82.3% while achieving the desired 99%-ile latency target compared with state-of-the-art solutions.
CCS CONCEPTS• Computer systems organization → Cloud computing; Neural networks; • Networks → Cloud computing.
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