Scheduling of Distributed Information Processing

存在成熟的技术来调度不同的传感器混合的使用（例如，无人机上的相机或分布在大的地理区域上的传感器阵列），以最大化可以从感测到的数据导出的信息的效用。然而，只有少量的研究已经调查了如何调度分布式处理的不同组合的子集被应用。例如，给定接收数据的传感器网络，未来的中间件需要推理将数据传送到中央服务器以执行准确但耗时的分析的长期影响，而不是使用与传感器紧密接近的处理来提供及时但不太准确的分析，在这种情况下，关键的挑战是确保我们能够推理出我们在其他地方和未来可能计算的内容，而无需实际执行计算本身：我们需要为未来的分布式处理任务建立统计模型，就像传感器管理为未来的传感任务使用模型一样。2因此，本项目的重点将集中在开发统计模拟器，这些模拟器可以预测计算如何处理数据和生成信息。一旦这些模拟器存在，重点将放在使用模拟器调度分布式计算资源上。

Mature techniques exist to schedule the use of a disparate mix of sensors (e.g., cameras on drones or sensor arrays distributed across a large geographic area) to maximise the utility of the information that can be derived from the sensed data. However, only a modest quantity of research has investigated how to schedule what subset of a disparate mix of distributed processing is applied. For example, given a network of sensors receiving data, future middleware needs to reason about the long-term impact of communicating data to a central server to perform an accurate, but time consuming, analysis, rather than using processing in close proximity to the sensor to provide a timely, but less accurate, output.The key challenge in this context is ensuring we can reason about what we might calculate elsewhere and in the future without actually performing the calculation itself: we need statistical models for the future distributed processing tasks just as sensor management uses models for future sensing tasks.The focus of this project will therefore centre on developing statistical emulators that can predict how computation will process data and generate information. Once those emulators exist, the focus will be on using the emulators to schedule distributed computational resources.