The DOPPLER project: modelling and optimizing workloads for the cloud

DOPPLER 项目：云工作负载建模和优化

• 批准号: 458908-2013
• 负责人: Holt, Richard
• 金额: $ 7.79万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=555420
• 关键词: DOPPLER; project; modelling; optimizing; workloads

As organizations grow, their data centres inevitably become larger and more complex. These centres usually provide numerous services to the internal and external users; such services are commonly deployed on virtual machines (VMs) across a cluster of computing devices, which can either be the property of the organization (i.e., a "private cloud") or be rented from a public cloud provider such as Amazon EC2. In both cases, private or public, managing "the cloud" requires appropriate virtualization and cost-effective provisioning of the infrastructure with respect to the type and size of the service requests (i.e., the workload). The implementation of this virtualization and provisioning needs to be carefully considered. Over-estimating the required infrastructure for a set of requested services in a specified period of time leads to waste, under-utilization, and increased costs. At the same time, under-estimation of the future workload is also unacceptable, since it eventually degrades the quality of the services and may lead to violations of in the Service-Level Agreements (SLAs) with the clients. Minimizing the infrastructure costs, while at the same time ensuring that SLAs are satisfied, requires not only state-of-the-art techniques for resource assignment and load balancing but also a detailed understanding of the cloud workload itself.

随着组织的发展，其数据中心不可避免地变得更大、更复杂。这些中心通常向内部和外部用户提供许多服务;这些服务通常部署在跨计算设备集群的虚拟机（VM）上，这些虚拟机可以是组织的财产（即，“私有云”）或从公共云提供商（如Amazon EC2）租用。在这两种情况下，无论是私有还是公共，管理“云”都需要针对服务请求的类型和大小（即，工作量）。这种虚拟化和资源调配的实施需要仔细考虑。在指定的时间段内，过高估计一组请求的服务所需的基础设施会导致浪费、利用不足和成本增加。同时，低估未来的工作量也是不可接受的，因为它最终会降低服务质量，并可能导致违反与客户的服务级别协议（SLA）。最大限度地降低基础设施成本，同时确保满足SLA，不仅需要最先进的资源分配和负载平衡技术，还需要详细了解云工作负载本身。