(Received: 13-May-2021, Revised: 22-May-2021 , Accepted: 24-May-2021)
Over the internet, application efficiency management has recently emerged as an essential service cloud computing. The Cloud Service Provider (CSP) gives various cloud services based on pay per use, which requires efficient monitoring and measuring of services delivered for management of Quality of Service (QoS) through the Internet of Things (IoT) and therefore needs to fulfil the Service Level Agreements (SLAs). However, avoiding SLA violations and ensuring a user’s dynamic demands as per QoS fulfilment are challenging in cloud computing while delivering dedicated cloud services. Cloud environment intricacy, heterogeneity and dynamism are expanding quickly, making cloud frameworks unmanageable and unreliable. Cloud systems need self-management of services to overcome these issues. Therefore, there is a need to develop a resource-provisioning scheme that automatically fulfils cloud user’s QoS requirements, thus helping the CSP accomplish the SLAs and avoid SLA violations. This paper presents a prediction-based resource management technique called Predictive Cloud Computing Systems (PCCSs). Focus is on the self-healing-based prediction that handles unexpected failures and self-configuration-based prediction of resources for applications. The Predictive Cloud Computing System (PCCS) performance is evaluated in the cloud simulator. The simulation results revealed that Predictive Cloud Computing Systems (PCCSs) achieve better results than existing techniques, in terms of execution time, cost-effectiveness, resource conflict and SLA breach while delivering reliable services.

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