Queues in Cloud Computing

云计算中的队列

• 批准号: 1852282
• 负责人: Mariana Olvera-Cravioto
• 金额: $ 15.26万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1852282&HistoricalAwards=false
• 关键词: Queues; Cloud; Computing

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.

云计算是一种现代范式，其中计算任务是在一个在大型计算机网络中共存的服务器的子集，称为云。这些云中的计算机数量正在迅速增加，如今超过了数十万。从业务和科学的角度来看，建立大型且平行的计算设施都可以使用。具体来说，当今的研究人员不需要拥有昂贵的超级计算机来研究复杂的系统，因为他们一次可以一次，数小时或几个月的时间在云中，远低于维护较小设施所需的费用。这项研究将为这些大规模系统的分析和有效控制提供新的可拖动数学技术。当今用于大型计算作业的分布式处理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。 通过该奖项进行的研究将首先分析一个可用于数学上的可操作模型的服务器网络，在该网络到达后，作业将分为多个部分，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所有工作的所有部分都必须以同步的方式获得服务。该模型与第二个模型相结合，在该模型中等待所需的服务器数量可用，将为设计和量化实际调度策略的收益提供基准。通过指出这两种模型构成了没有服务器信息而不是拥有完整的集中信息的极端情况，这项工作实质上将为这一知识提供代价，这将在设计未来的云系统中发挥重要作用。