Working Together: Constraint Programming and Cloud Computing

协同工作：约束编程和云计算

• 批准号: EP/K015745/1
• 负责人: Ian Miguel
• 金额: $ 80.3万
• 依托单位: University of St Andrews
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FK015745%2F1
• 关键词: Working; Together; Constraint; Programming; Cloud

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.

该提案结合了云计算和约束编程中两个活跃的重要研究流，两者都将通过协同工作实现显著和持续的利益。约束编程是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以获得最佳结果。获得更稳健性能的一种方法是采用具有互补优势的求解器组合。云提供的可扩展资源非常适合部署此类组合，并提供了使用大型求解器组合来解决异常困难的挑战问题的机会。相反，云计算的一个主要问题是如何在可用的基础设施上部署应用程序，以最大限度地提高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，尽可能接近数据（就网络距离而言）运行计算非常重要。这是一个困难的组合问题，涉及大量变量，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。我们在ICT领域的两个主要研究方向将与天文学领域的第三个研究方向相互作用并合作，为一个重大挑战应用提供解决方案：安排望远镜观测来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也将大大受益于对它们合作解决大型复杂问题的能力的重大评估。

项目成果

Executing Bag of Distributed Tasks on Virtually Unlimited Cloud Resources

在几乎无限的云资源上执行分布式任务包

• DOI: 10.5220/0005403303730380
• 发表时间: 2015
• 作者: Barker A

Academic Cloud Computing Research: Five Pitfalls and Five Opportunities

学术云计算研究：五个陷阱和五个机遇

• 发表时间: 2014
• 作者: Barker, A.

Generating custom propagators for arbitrary constraints

为任意约束生成自定义传播器

• DOI: 10.1016/j.artint.2014.03.001
• 发表时间: 2014
• 期刊: Artificial Intelligence
• 影响因子: 14.4
• 作者: Gent I

Breaking Conditional Symmetry in Automated Constraint Modelling with Conjure

使用 Conjure 打破自动约束建模中的条件对称性

• 发表时间: 2014
• 期刊: Proceedings of the 21st European Conference on Artificial Intelligence
• 作者: Akgun, O.

Complexity of n-Queens Completion

• DOI: 10.1613/jair.5512
• 发表时间: 2017-01-01
• 期刊: JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH
• 影响因子: 5
• 作者: Gent, Ian P.;Jefferson, Christopher;Nightingale, Peter
• 通讯作者: Nightingale, Peter