Scheduling of Distributed Information Processing

现有成熟的技术可以安排使用不同的传感器组合（例如，无人机上的摄像头或分布在大地理区域的传感器阵列），以最大限度地利用可从感测数据中获得的信息。然而，只有少量的研究调查了如何调度分布式处理的不同混合的哪个子集被应用。例如，给定一个接收数据的传感器网络，未来的中间件需要考虑将数据通信到中央服务器的长期影响，以执行准确但耗时的分析，而不是在靠近传感器的地方使用处理来提供及时但不太准确的输出。在这种情况下，关键的挑战是确保我们能够在不实际执行计算本身的情况下推断出我们可能在其他地方和未来计算的内容：我们需要未来分布式处理任务的统计模型，就像传感器管理为未来的传感任务使用模型一样。因此，这个项目的重点将集中于开发统计模拟器，以预测计算将如何处理数据和产生信息。一旦这些模拟器存在，重点将放在使用模拟器来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.