AF: EAGER: Scheduling with Resource Contraints

AF：EAGER：具有资源约束的调度

• 批准号: 1349602
• 负责人: Clifford Stein
• 金额: $ 10万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1349602&HistoricalAwards=false
• 关键词: AF; EAGER; Scheduling; Resource; Contraints

Even as the availability of computing power, network bandwidth and other resources increases at a rapid rate, users and applications increase their demand for computation and their use of resources at roughly the same pace.  Thus, no matter how much progress is made  on hardware efficiency, we will always need efficient algorithms to manage these resources. The philosophy of managing additional resources in an intelligent manner goes beyond computer systems, and is relevant to many other  scientific and industrial areas. In various real-life systems, processing times may be controllable by allocating resources, such as additional money, overtime, energy, fuel, catalysts, subcontracting, or additional manpower, to the job operations. In such systems, job scheduling and resource allocation decisions should be coordinated carefully to achieve the most efficient system performance. Applications arise in many industrial areas.The PI plans to study several algorithmic problems that arise in scheduling with additional resource constraints.   While this field has received much attention over the past ten years and beyond, the PI will focus on two areas that have received very little attention in the computer science community -- scheduling when the benefit of the schedule and the cost of energy or other resources are monetized, and scheduling in models that go beyond the typically studied computer systems setting.  Because these have received very little attention, this work is somewhat speculative.  If successful, this work could have a high impact, with applications into many new areas. The PI will design simple, low overhead algorithms.

即使计算能力的可用性，网络带宽和其他资源以快速速度增加，用户和应用程序也会增加其对计算的需求以及在大致相同的空间上使用资源的需求。这是，无论硬件效率取得了多少进展，我们将始终需要有效的算法来管理这些资源。以智能方式管理其他资源的理念超出了计算机系统，并且与许多其他科学和工业领域有关。在各种现实生活中，可以通过分配资源来控制处理时间，例如额外的钱，加班，能量，燃料，燃料，催化剂，分包或额外的人力。在这样的系统中，应仔细协调工作计划和资源分配决策，以实现最有效的系统性能。 PI计划在许多工业领域都有应用。尽管该领域在过去十年及以后都受到了很多关注，但PI将重点关注两个领域，这些领域在计算机科学界很少关注的领域 - 安排时间表的收益以及能源或其他资源的收益或其他资源的货币化，并在典型研究的计算机系统设置的模型中进行安排。因为这些受到很少的关注，所以这项工作有些投机。如果成功的话，这项工作可能会产生很大的影响，并将其应用于许多新领域。 PI将设计简单的低顶算法。