Queues in Cloud Computing

云计算是一种现代范例，其中计算任务在服务器子集上执行，这些服务器子集共存于称为云的大型分布式计算机网络中。这些云中的计算机数量正在迅速增加，目前已超过数十万台。从商业和科学的角度来看，使大型和并行计算设施普遍可用都是可取的。具体来说，今天的研究人员不需要拥有一台昂贵的超级计算机来研究复杂的系统，因为他们可以一次进入云端数小时、数周或数月，远远低于他们需要支付的维护一个小得多的设施的成本。该研究将为这些大型系统的分析和有效控制提供新的易于处理的数学技术。目前用于分布式处理大型计算作业的许多系统的复杂性使得很难理解服务器信息对设计调度协议的影响。通过该奖项完成的研究将从分析服务器网络的数学易于处理的模型开始，其中任务在到达时被分成许多部分，然后在随机选择的服务器上分配/排队。该模型的主要特征是作业的所有部分必须以同步方式接收服务。该模型与第二个模型（其中作业等待所需数量的服务器可用）相结合，将为设计和量化实际调度策略的收益提供基准。注意到这两种模型构成了没有服务器信息与拥有完整、集中的信息的极端情况，这项工作将从本质上为这些知识提供一个价格，这将在设计未来的云系统中发挥重要作用。

Cloud computing is a modern paradigm where computing tasks are performed on a subset of servers which coexist in a large distributed network of computers, called clouds. The number of computers in these clouds is rapidly increasing, surpassing hundreds of thousands today. Making large and parallel computing facilities generically available is desirable both from the business as well as the scientific perspective. Specifically, researchers today do not need to own an expensive supercomputer for studying complex systems, since they can tap into the cloud for hours, weeks or months at a time, well below the cost that they would need to pay to maintain a much smaller facility. This research will provide new tractable mathematical techniques for the analysis and efficient control of these large-scale systems.The complexity of many of the systems used today for the distributed processing of large computing jobs makes it difficult to understand the impact of server information on designing scheduling protocols. The research done through this award will start by analyzing a mathematically tractable model for a network of servers where jobs are, upon arrival, split into a number of pieces, which are then assigned/queued at randomly chosen servers. The main characteristic of the model is that all pieces of a job must receive service in a synchronized fashion. This model, combined with a second model where jobs wait for the required number of servers to become available, will provide a benchmark for designing as well as quantifying the gains of practical scheduling policies. By noting that the two models constitute the extreme cases of having no server information versus having full, centralized information, this work will essentially provide a price for this knowledge, which will play an important role in designing future cloud systems.