LAUSR

Efficient resource provisioning is one of the important research challenges in cloud computing. Cloud providers use auto-scaling techniques for resource provisioning to manage the fluctuating workload as well as SLA violations. Auto-scaling is necessary for optimal and efficient resource allocation to manage the workload that will reduce power consumption and will also guarantee for better quality of service (QoS). It is essential for those services which support rigorous quality of service (QoS) requirements such as minimum response time or maximum throughput. Cloud providers implement service elasticity through auto-scaling mechanisms. This paper uses a novel prediction technique for resource provisioning through the auto-scaling mechanism. The prediction is done using time series data analytics with the help of the deep learning technique (i.e., LSTM). The predictive resource provisioning technique will predict the traffic load over the server and then estimates the required number of computing resources. The estimated computing resources are provisioned through the queuing theory so that it optimizes the response time and satisfies the SLA contract between the end-user and provider. The optimal resource provisioning will also reduce the consumption of computing resources while attenuating resource over-provisioning minimizes energy consumption and reduces the infrastructure’s operating costs. The experimental results show that the workload prediction through LSTM obtains better accuracy than other traditional models.