Queues in Cloud Computing

云计算是一种现代范例，其中计算任务在共存于大型分布式计算机网络（称为云）中的服务器子集上执行。这些云中的计算机数量正在迅速增加，今天已超过数十万台。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计算几个小时，几周或几个月，远远低于他们维护一个小得多的设施所需的成本。这项研究将提供新的易于处理的数学技术，这些大型systems.The分布式处理的大型计算作业的复杂性，今天使用的许多系统的分析和有效的控制，很难理解的影响，服务器的信息设计调度协议。 通过该奖项进行的研究将首先分析一个数学上易于处理的服务器网络模型，在该模型中，工作在到达时被分成多个部分，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的方式接收服务。这个模型，结合第二个模型，工作等待所需数量的服务器变得可用，将提供一个基准的设计以及量化的收益实际调度政策。通过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信息的极端情况，这项工作将基本上为这种知识提供价格，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.