The elasticity characteristic of cloud services attracts application providers to deploy applications in a cloud environment. The scalability feature of cloud computing gives the facility to application providers to dynamically provision the computing power and storage capacity from cloud data centers. The consolidation of services to few active servers can enhance the service sustainability and reduce the operational cost. The state-of-art algorithms mostly focus either on reactive or proactive auto-scaling techniques. In this article, a Robust Hybrid Auto-Scaler (RHAS) is presented for web applications. The time series forecasting model has been used to predict the future incoming workload. The reactive approach is used to deal with the current resource requirement. The proposed auto-scaling technique is designed with the threshold-based rules and queuing model. The security mechanism is used to secure the user’s request and response to the web-applications deployed in cloud environment. The designed approach has been tested with two real-time web application workloads of ClarkNet and NASA. The proposed technique achieves \(14\%\) reduction in cost, and significant improvement in response time, service level agreement (SLA) violation, and gives consistency in CPU utilization.

云服务的弹性特征吸引了应用程序提供商在云环境中部署应用程序。云计算的可伸缩性功能为应用程序提供商提供了便利，可从云数据中心动态地提供计算能力和存储容量。将服务整合到很少的活动服务器可以增强服务的可持续性并降低运营成本。最新的算法主要集中于反应式或主动式自动缩放技术。在本文中，提出了针对Web应用程序的鲁棒混合自动缩放器（RHAS）。时间序列预测模型已用于预测未来的传入工作负载。反应性方法用于处理当前的资源需求。提出的自动缩放技术是基于阈值的规则和排队模型设计的。安全机制用于保护用户对云环境中部署的Web应用程序的请求和响应。该设计方法已通过ClarkNet和NASA的两个实时Web应用程序工作负载进行了测试。所提出的技术实现成本降低了（14％），并显着改善了响应时间，违反了服务水平协议（SLA），并提供了CPU利用率的一致性。