In cloud computing, heterogeneous resources located in different data centers are used to provide pay-per-use performance. Resource provisioning encounters problems due to uncertainty and dispersion of resources in cloud computing environments. According to our research, there are various techniques, but the existing methods and frameworks cannot handle the behaviors of these applications, environment, and resources. The cloud provider wants a framework to execute the task for achieving cost-effective delivery and reliable cloud services. Cloud workload's essential requirement is Quality of Service (QoS) based on adequate resource provisioning as per cloud workload needs. The resource discovery and best paring of workload-resource as per the cloud user's QoS need for workload requirements is an optimization problem. The resource provisioning is appropriate and can be acceptable if offered as per QoS needs in a controlled way. A mutual contract of Service Level Agreement (SLA) is signed between Cloud Service Provider (CSP) and cloud users, which determines the Service Level Objectives (SLO). The SLA contains the QoS parameters and notifies the dependent SLA's recent status. The user will get Quality of Service (QoS), based on prediction, where the resource would manage efficiently and lead to perform loads and deliver cost-effective and reliable cloud services. This paper presents a prediction-based resource management technique called Predictive Cloud Computing Systems (PCCSs). Focus on the self-healing-based prediction that handles unexpected failures and self-configuration-based prediction of resources and applications. Predictive Cloud Computing Systems (PCCSs) performance evaluated in the cloud simulator. The simulation results reveal that Predictive Cloud Computing Systems (PCCSs) achieve better results than existing techniques as execution time, cost-effective, resource conflict, SLA breach while delivering reliable services.
Key words: Quality of Service, Cloud Service Provider, Service Level Agreement, Service Level Objective, and Predictive Cloud Computing System.
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