Efficient usage of IT equipment in Data Centers requires modulating their power-consumption according to the actual workload. The most effective strategy to this aim consists in consolidating as many applications as possible on the smallest number of servers, so that idle devices can be shut down or put in low-power states. Usually, this process is driven by computing and networking resources requested by each application (e.g., CPU and RAM) and applies a certain degree of overcommitment, assuming that such resources are not fully used continuously. However, this approach is critical with real workload patterns, which usually change over time; as a matter of fact, consolidation in real scenarios often leads to either low efficiency or violations of Quality of Service (QoS) constraints, depending on the level of overcommitment.

In this paper, we investigate a novel consolidation strategy based on an enhanced system model for the Infrastructure-as-a-Service cloud paradigm, which targets a better trade-off between Energy Efficiency and Quality of Service. We explicitly target modular cloud applications, which design is split into multiple components deployed in Virtual Machine (VM)s or containers. Our consolidation strategy allows to “freeze” parts of the application which are not currently used, making them available when requested with minimal latency. This improves energy saving with respect to other approaches, especially when idle VMs are present for backup or redundancy purposes, without degrading the service level. We compare multiple heuristics available in Optaplanner to solve our consolidation problem, and investigate improvements with respect to a more traditional approach. Our evaluation includes both simulations and experimentation in a real test-bed. The comparison shows that the Late Acceptance algorithm on average finds better solutions than other alternatives and energy efficiency improves up to 40% with respect to more conventional strategies, with deterioration of QoS indexes below 1%.

数据中心中IT设备的有效使用需要根据实际工作量来调整其功耗。为此目的，最有效的策略是在尽可能少的服务器上整合尽可能多的应用程序，以便可以关闭空闲设备或使其处于低功耗状态。通常，此过程由每个应用程序请求的计算和联网资源（例如，CPU和RAM）驱动，并假定一定程度的超额使用量，前提是这些资源没有被连续使用。但是，这种方法对于实际的工作负载模式至关重要，该模式通常会随着时间而变化。实际上，根据超额使用的级别，在实际场景中进行整合通常会导致效率低下或违反服务质量（QoS）约束。

在本文中，我们针对基础设施即服务云范例研究了一种基于增强型系统模型的新型整合策略，该策略旨在在能源效率和服务质量之间取得更好的权衡。我们明确针对模块化云应用程序，该应用程序的设计被分为部署在虚拟机（VM）或容器中的多个组件。我们的整合策略允许“冻结”应用程序中当前未使用的部分，从而在请求时以最小的延迟提供它们。相对于其他方法，这可以提高节能效果，尤其是在出于备份或冗余目的而存在空闲VM的情况下，而不会降低服务水平。我们比较了Optaplanner中提供的多种启发式方法来解决合并问题，并研究相对于传统方法的改进。我们的评估包括在真实测试床上的模拟和实验。比较结果表明，与其他替代方案相比，后期接受算法平均可以找到更好的解决方案，相对于更传统的策略，能效提高了40％，而QoS指标的下降低于1％。