ABSTRACT
Existing cloud resource scheduling approaches have mainly concentrated on enhancing the reducing power
consumption and resource utilization by enhancing the legacy heuristic algorithms. Although, different resourceintensive applications running on cloud data centers in realistic scenarios have significant results on the power
consumption and cloud application performance. Furthermore, occurring peak loads may lead to a scheduling
error, which can significantly effects on the energy efficiency of scheduling algorithms. At peak loads may lead to
scheduling errors because there is no prediction model to predict the coming resource utilization of a data center
through the data collected by the monitoring model. Effective scheduling mechanism gives an optimal solutions
for complex problems while providing the Quality-of-Service (QoS) and avoiding Service Level Agreement (SLA)
violations. To enhance the resource scheduling mechanism in cloud environment, predicting future workload to
the each virtual machine pool in different manners like number of physical machines, number of virtual
machines, number of requests and resource utilization etc., is an essential step. According to the prediction
results, resource scheduling can be done in the right time, while avoiding QoS dropping and SLA violations. To
achieve efficient resource scheduling, proposed approach lease advantages of prediction models. The proposed
algorithm consists of a prediction model which is based on iterative fractal model and a scheduler which is based
on an improved heuristic algorithms. Proposed scheduler algorithm is responsible for scheduling of resources
while reducing the energy consumption and giving the guaranteeing the QoS.
Keywords: - Cloud computing, Energy efficient, Prediction model, Scheduling algorithm, Virtual machine.