Container-based virtualization is a new technology used by cloud providers to provide cloud services to end-user. This technology has various advantages (e.g. lightweight, quickly deployable, and efficient for resource utilization) for executing an application. It reduces the operating cost, carbon emission, and allocates the resources dynamically. Different cloud applications have different requirements. Deploying resources according to peak requirements always can be costly. On the other hand, always having minimum computing resources may not meet workload’s peak requirements, and may cause degraded system performance, less throughput, more response time and service level agreement violations. Hence, it becomes a challenge to maintain optimal level of resources to fulfill the SLA requirements for the applications. To address the above issues, we propose an auto-scaler which uses proactive approach (Support Vector Regression) to perform horizontal elasticity for Docker containers in response to fluctuating workload for real-time applications. As the workload increases, additional resources will be allocated dynamically supporting elasticity. The increase in capacity of a machine dynamically is termed as elasticity. The effective mechanism of elasticity avoids the violation of SLA and penalties in terms of user’s loss. The proposed auto-scaler uses the IBM computing model, MAPE-K principle to perform elasticity using the workload predictions made by the SVR model. The predicted workload helps auto-scaler to find out the minimum numbers of replicas needed for a container of a cluster so that it handles the future workload. The experimental results show that the results of SVM prediction keep the performance of the system sustainable with fluctuating workload.

基于容器的虚拟化是云提供商用来向最终用户提供云服务的一项新技术。该技术在执行应用程序方面具有多种优势（例如，轻量级、可快速部署和资源利用效率高）。降低运营成本，减少碳排放，动态分配资源。不同的云应用有不同的要求。根据高峰需求部署资源总是代价高昂。另一方面，始终拥有最少的计算资源可能无法满足工作负载的峰值要求，并可能导致系统性能下降、吞吐量降低、响应时间增加和违反服务水平协议。因此，保持最佳资源水平以满足应用程序的 SLA 要求成为一项挑战。针对以上问题，我们提出了一种自动缩放器，它使用主动方法（支持向量回归）为 Docker 容器执行水平弹性，以响应实时应用程序的波动工作负载。随着工作负载的增加，将动态分配额外的资源以支持弹性。机器容量的动态增加称为弹性。弹性的有效机制避免了违反SLA和用户损失方面的惩罚。提议的自动缩放器使用 IBM 计算模型，MAPE-K 原理，使用 SVR 模型做出的工作负载预测来执行弹性。预测的工作负载有助于自动缩放器找出集群容器所需的最小副本数，以便处理未来的工作负载。