一种云环境中的动态细粒度资源调度方法

云计算平台中普遍采用固定资源量的粗粒度资源分配方式，由此会引起资源碎片、过度分配、低集群资源利用率等问题.针对此问题，提出一种细粒度资源调度方法，该方法根据相似任务运行时信息推测任务资源需求；将任务划分为若干执行阶段，分阶段匹配资源，从分配时间和分配资源量两方面细化资源分配粒度；资源匹配过程中，基于资源可压缩特性进一步提高资源利用率和性能；采用资源监控、策略调整、约束检查等机制保证资源使用效率和负载性能.在开源云资源管理平台中，基于细粒度资源调度方法实现了调度器.实验结果表明：细粒度资源调度方法可以在不丧失公平性且调度响应时间可接受的前提下，细化资源匹配的粒度，有效提高云计算平台资源利用率和性能.

Dynamically Fine-grained Scheduling Method in Cloud Environment

The coarse-grained scheduling used in cloud computing platform allocates fixed quantity resources to tasks. However, this allocation can easily lead to problems such as resource fragmentation, over-commitment and inefficient resource utilization. This study proposes a dynamically fine-grained scheduling method to resolve those problems. This method estimates resource requirement of task according to similar tasks and divides tasks into execution stages according to the task requirement, and it also matches task resource requirement and available server resources by stages to refine two aspects of allocation granularity: allocation duration and allocation quantity. Furthermore, this method may compress resource allocation to further improve resource utilization and performance, and this method uses several mechanisms including runtime resource monitoring, allocation policy adjustments, and scheduling constraint checks to ensure resource utilization and performance of cloud computing platform. Based on this method, a scheduler has been implemented in the open source cloud computing platform Yarn. The test results show that the dynamically fine-grained scheduling method can resolve resource allocation problems by significantly improving resource utilization and performance with acceptable fairness and scheduling response times.