This paper presents a new opportunistic scheduling and resource consolidation system based on an economic model related to different service level agreements (SLAs) classes. The goal is to address the problem of companies that manage a private infrastructure of machines, i.e., a cloud platform and would like to optimize the scheduling of several requests submitted online by users. For the sake of simplicity of the presentation, the proposed economic model has two SLAs classes (qualitative and quantitative) with three Quality of Service for each SLA class (Premium, Advanced and Best effort). The consequence of this choice as well as the need to serve requests as they come have an impact on the algorithmic ways to consolidate an infrastructure. Indeed, our system proposes a new allocation heuristic that adapts the number of active machines in the cloud according to the global resources usage of all machines inside the infrastructure. This heuristic can be examined as a consolidation heuristic, based on the idea that the system can make reasonable choices, based on the SLAs, for the placement and the allocation of resources for each request. Experimentation with our system is conducted on Prezi (Web workload) and Google Cloud Data (HPC-oriented workload) traces, and they demonstrate the potential of our approach under different scenarios. From a methodological point of view, we propose a general framework which is limited in scope, for the sake of simplicity in reading the paper, with a small number of SLAs, but the idea can be extended to many more SLAs and performance metrics. In this way, the user or the provider operating the cloud have more latitude, thanks to our multi-criteria approach, to control the workload without a sacrifice on performance.

中文翻译：

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基于新经济模型的机会调度与资源整合系统

本文基于与不同服务水平协议 (SLA) 类别相关的经济模型，提出了一种新的机会调度和资源整合系统。目标是解决管理机器私有基础设施（即云平台）并希望优化用户在线提交的多个请求的调度的公司的问题。为了演示的简单起见，建议的经济模型具有两个 SLA 类（定性和定量），每个 SLA 类（高级、高级和尽力而为）具有三个服务质量。这种选择的结果以及在请求到来时提供服务的需求会对整合基础设施的算法方式产生影响。确实，我们的系统提出了一种新的分配启发式方法，它根据基础设施内所有机器的全局资源使用情况调整云中活动机器的数量。基于系统可以基于 SLA 为每个请求的资源放置和分配做出合理选择的想法，可以将这种启发式检查为合并启发式方法。在 Prezi（Web 工作负载）和 Google Cloud Data（面向 HPC 的工作负载）跟踪上对我们的系统进行了实验，它们展示了我们的方法在不同场景下的潜力。从方法论的角度来看，我们提出了一个范围有限的通用框架，为了阅读论文的简单起见，使用少量的 SLA，但这个想法可以扩展到更多的 SLA 和性能指标。这样，