Working Together: Constraint Programming and Cloud Computing

这一提议结合了云计算和约束编程领域的两个活跃而重要的研究流，两者都将从协同工作中实现显著和持续的好处。约束规划是一种解决复杂组合问题的成熟技术。然而，这些问题的固有困难意味着性能可能是可变的，通常需要专家进行调整才能获得最佳结果。获得更稳健性能的一种方法是使用优势互补的求解器组合。云提供的可扩展资源非常适合部署这样的投资组合，并提供了使用大型求解器投资组合来解决异常困难的挑战问题的机会。相反，云计算的主要关注点是如何在可用的基础设施上部署应用程序，从而最大化性能并最大限度地降低运营成本。在处理大数据场景时，为了最大限度地减少网络延迟并最大限度地提高应用程序的性能，计算必须尽可能靠近数据(就网络距离而言)，这就增加了复杂性。这是一个复杂的组合问题，包含大量变量，包括：公共云提供商、云配置、地理区域、定价等，约束编程非常适合这些变量。我们ICT领域的两个主要研究流将相互作用，并与天文学中的第三个研究流合作，为一个主要挑战应用程序提供解决方案：安排望远镜观测以测量整个银河系的行星丰度。如果成功，对天文学的好处是显而易见的，但我们的两个主要流派也将从对他们合作解决大型复杂问题的能力的重大评估中受益匪浅。

This proposal combines two active, important research streams in cloud computing and constraint programming, both of which will realise significant and sustained benefits from working in concert. Constraint programming is a proven technology for solving complex combinatorial problems. However, the inherent difficulty of these problems means that performance can be variable, often requiring tuning by an expert to obtain best results. One approach to obtaining more robust performance is to employ a portfolio of solvers with complementary strengths. The scalable resource offered by the cloud is perfectly suited to the deployment of such portfolios and presents the opportunity to employ large solver portfolios to tackle challenge problems of exceptional difficulty. Conversely, a major concern in cloud computing is how to deploy an application on the available infrastructure so as to maximise performance and minimise operating costs. Added complexity arises when dealing with Big Data scenarios where it is important to run computation as closely (in terms of network distance) as possible to the data, in order to minimise network latency and maximise the performance of an application. This is a difficult combinatorial problem with a large set of variables including: public cloud provider, cloud configuration, geographical region, pricing etc. to which constraint programming is ideally suited.Our two primary research streams in ICT will interact and work together with a third in astronomy to deliver a solution to a major challenge application: scheduling telescope observations to measure the abundance of planets throughout the Milky Way. If successful, the benefit to astronomy is clear, but our two primary streams will also benefit greatly from a major evaluation of their ability to work together to solve a large, complex problem.