Machine Intelligence by Central Pivot Ranges (MICPR): An Optimal Resource Scheduling Strategy for Cloud Services

Abstract Alongside the increasing demand for cloud computing solutions, the other critical factor in focusing on cloud computing solutions is effective cloud resource sensing and scheduling. In the real-time scenario, there are numerous resource scheduling models available to support effective managing resource scheduling. However, resource sensing and scheduling challenges remains in the increasing complexities of background processing in the cloud servers. Thus, in this manuscript, a machine intelligence model that provides significant insights into how the load balance can be sensed and handled using the sever performance's critical metrics is proposed. The model is an automated machine intelligence (MI) approach that uses pivot range (CPR), support, and resistance models for estimating if the load of the system as per the metrics chosen is running heavy load or normal. Accordingly, the decision can be triggered automatically by MICPR to choose the fundamental resource sensing and scheduling algorithms. The experimental study of the model refers to potential ways in which the system can be feasible, and if implemented on a large scale, it can help reduce the backend load on the cloud servers.