Auto-scaling systems can dynamically scale the required resources for cloud-based services at runtime. This is an effective mechanism, enabling services to adapt to environmental changes. These systems establish the foundation for achieving elasticity in the modern cloud computing paradigm. Given the dynamic and uncertain nature of the shared cloud infrastructure, cloud auto-scaling systems are one of the most complex and sophisticated created artifacts, aiming to achieve self-aware, self-adaptive, and dependable runtime scaling. To find an effective solution to this problem, an accurate prediction of the required amount of workload as well as the system metrics for future time periods are needed. Various solutions have already been proposed to tackle this problem. Many solutions make use of machine learning, statistical, and ensemble methods. In this paper, we view the auto-scaling problem as a sequence model and apply the convolutional neural networks to predict the future workload of cloud services. Also, by using neural networks, we obtain a mapping between the predicted workload as well as the real-time and future amounts of the required resources. We have also proposed a decision-making mechanism that takes into account different and sometimes conflicting user criteria resulting in the best-compromised decision. To this aim, we have used TOPSIS as a multi-criteria decision-making method for the decision-making component. In the evaluation section, we have examined the amount of prediction error, the amount of service level agreement violations, as well as the amount of resources' under-utilization. Evaluations demonstrate that the proposed approach for predicting the workload shows a 4 percent improvement over the existing approaches.

自动扩展系统可以在运行时动态扩展基于云的服务所需的资源。这是一种有效的机制，可以使服务适应环境变化。这些系统为在现代云计算范式中实现弹性奠定了基础。鉴于共享云基础架构的动态和不确定性，云自动扩展系统是创建的最复杂和最复杂的工件之一，旨在实现自我感知，自适应和可靠的运行时扩展。为了找到有效解决此问题的方法，需要对所需工作量以及未来时间段的系统指标进行准确的预测。已经提出了各种解决方案来解决这个问题。许多解决方案都利用了机器学习，统计和集成方法。在本文中，我们将自动扩展问题视为一个序列模型，并应用卷积神经网络来预测云服务的未来工作量。此外，通过使用神经网络，我们可以在预测的工作量以及所需资源的实时和将来数量之间获得映射。我们还提出了一种决策机制，该机制考虑了导致最佳折衷决策的不同甚至有时相互冲突的用户标准。为此，我们将TOPSIS用作决策组件的多标准决策方法。在评估部分，我们检查了预测错误的数量，违反服务水平协议的数量以及资源的未充分利用的数量。