Cloud computing is incredibly significant in recent technologies in IT sector. Various types of cloud computing services and applications are available via an internet connection. As cloud computing is serving millions of users simultaneously, it must have the ability to meet all users requests with high performance and guarantee of quality of service (QoS). The energy-aware scheduling algorithm is concentrated on both makespan and also in energy consumption. In this book a novel scheduling algorithm based on the factors of workload and job type to predict the makespan and also energy consumption. The motivation of this scheduling algorithm is to achieve energy-efficient green task scheduling and to optimize the scheduler that uses the sigmoid neural task predictor for the implementation. Resource provisioning in cloud computing is a major component that can improve the performance of a cloud system to a huge extent. High dimensionality and high variability in the cloud workloads pose major challenges in the allocation process. This part of the work presents an architecture that performs resource provisioning based on demand prediction and range-based resource allocation.