AF: EAGER: Scheduling with Resource Contraints

即使计算能力、网络带宽和其他资源的可用性以快速的速率增加，用户和应用也以大致相同的速度增加其对计算的需求和其对资源的使用。 因此，无论在硬件效率方面取得了多大的进步，我们总是需要有效的算法来管理这些资源。以智能方式管理额外资源的理念超越了计算机系统，并且与许多其他科学和工业领域相关。在各种现实生活系统中，处理时间可以通过向作业操作分配资源（诸如额外的钱、加班、能量、燃料、催化剂、分包或额外的人力）来控制。在这样的系统中，作业调度和资源分配决策应仔细协调，以实现最有效的系统性能。应用出现在许多工业领域。PI计划研究几个算法问题，出现在调度与额外的资源约束。 虽然这个领域在过去十年及以后受到了很多关注，但PI将专注于两个在计算机科学界很少受到关注的领域-当调度的好处和能源或其他资源的成本被货币化时的调度，以及超越典型研究的计算机系统设置的模型中的调度。 因为这些很少受到关注，这项工作有点投机。 如果成功，这项工作可能会产生很大的影响，并应用于许多新的领域。PI将设计简单、低开销的算法。

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.