Predicting demand for computing resources in any system is a vital task since it allows the optimized management of resources. To some degree, cloud computing reduces the urgency of accurate prediction as resources can be scaled on demand, which may, however, result in excessive costs. Numerous methods of optimizing cloud computing resources have been proposed, but such optimization commonly degrades system responsiveness which results in quality of service deterioration. This paper presents a novel approach, using anomaly detection and machine learning to achieve cost-optimized and QoS-constrained cloud resource configuration. The utilization of these techniques enables our solution to adapt to different system characteristics and different QoS constraints. Our solution was evaluated using a system located in Microsoft’s Azure cloud environment, and its efficiency in other providers’ computing clouds was estimated as well. Experiment results demonstrate a cost reduction ranging from 51% to 85% (for PaaS/IaaS) over the tested period.

预测任何系统中对计算资源的需求都是一项至关重要的任务，因为它可以优化资源管理。在某种程度上，由于可以按需扩展资源，因此云计算降低了进行准确预测的紧迫性，但是，这可能会导致成本过高。已经提出了许多优化云计算资源的方法，但是这种优化通常会降低系统响应能力，从而导致服务质量下降。本文提出了一种新颖的方法，利用异常检测和机器学习来实现成本优化和受QoS约束的云资源配置。这些技术的利用使我们的解决方案能够适应不同的系统特性和不同的QoS约束。我们使用位于Microsoft Azure云环境中的系统对我们的解决方案进行了评估，并估算了其在其他提供商的计算云中的效率。实验结果表明，在测试期间，成本降低了51％至85％（对于PaaS / IaaS）。