Time-critical Scenarios Research Articles

Optimized QoS-aware replica placement heuristics and applications in astronomy data grid

This paper studies the Quality-of-Service (QoS)-aware replica placement problem in a general graph model. Since the problem was proved NP-hard, heuristic algorithms are the current solutions to the problem. However, these algorithms cannot always find the effective replica placement strategy. We propose two algorithms that can obtain better results within the given time period. The first algorithm is called Cover Distance algorithm, which is based on the Greedy Cover algorithm. The second algorithm is an optimized genetic algorithm, in which we use random heuristic algorithms to generate initial population to avoid enormous useless searching. Then, the 0-Greedy-Delete algorithm is used to optimize the genetic algorithm solutions. According to the performance evaluation, our Cover Distance algorithm can obtain relatively better solution in time critical scenarios. Whereas, the optimized genetic algorithm is better when the replica cost is of higher priority than algorithm execution time. The QoS-aware data replication heuristic algorithms are applied into the data distribution service of an astronomy data grid pipeline prototype, and the operation process is studied in detail.

FAST INFORMATION PROPAGATION IN SOCIAL NETWORKS

Social networks are attracting more attention from both industry and academia. Prior works on social networks focus on analyzing their roles in information propagation and decision making. However, few of these works consider the latency of information propagation, which is an essential issue especially in time-critical scenarios in social networks. In this paper, we introduce a new problem called FAST INFORMATION PROPAGATION PROBLEM, which is an optimization problem to identify the minimum set of influential nodes that could influence the whole network within a given latency bound d. We show our complexity result on this problem in the deterministic threshold models and present a greedy hill-climbing algorithm as the solution. For the d = 1 case, we prove that under the majority threshold model, this approximation algorithm has a performance ratio of H(Δ + 1) - 1 + ⌈ Δ/2⌉. Extensive experiments are conducted on a large collaboration network and the results show that our approximation algorithm outperforms the node-selection heuristics, which utilize the well-known approaches based on the node centrality in the social network analysis.

Design and Implementation of a Virtual Reality System and Its Application to Training Medical First Responders

This paper presents the design and implementation of a distributed virtual reality (VR) platform that was developed to support the training of multiple users who must perform complex tasks in which situation assessment and critical thinking are the primary components of success. The system is fully immersive and multimodal, and users are represented as tracked, full-body figures. The system supports the manipulation of virtual objects, allowing users to act upon the environment in a natural manner. The underlying intelligent simulation component creates an interactive, responsive world in which the consequences of such actions are presented within a realistic, time-critical scenario. The focus of this work has been on the training of medical emergency-response personnel. BioSimMER, an application of the system to training first responders to an act of bio-terrorism, has been implemented and is presented throughout the paper as a concrete example of how the underlying platform architecture supports complex training tasks. Finally, a preliminary field study was performed at the Texas Engineering Extension Service Fire Protection Training Division. The study focused on individual, rather than team, interaction with the system and was designed to gauge user acceptance of VR as a training tool. The results of this study are presented.