Internet of Things and cloud services are one of main enablers in fourth industrial revolution. Real-time industrial systems have high availability requirements of 99.9% to 99.999% whereas architectures built on regional cloud services and IoT do not provide similar guarantees or Service Level Agreement. These differences of QoS and SLA availability between Operational Technology and Information Technology has become a main challenge in adoption of Industrial Internet of Things (IIoT) for real-time applications.This work presents an approach to find end-to-end QoS and availability for an IIoT architecture. Device-to-cloud, cloud-to-cloud and inside-cloud experiments have been performed over eight weeks where each experiment have more then four million QoS measurements. Our availability analysis shows that a remote IoT connected to a less busy cloud region gives higher availability as compared to an IoT device inside a busy cloud region. IIoT and regional cloud services provide good QoS with 99% to 99.9% availability for 1sec soft real-time requirements. In 100ms applications, more efforts are required to achieve higher then 95% availability and design industrial SLA. IIoT applications with 10sec latency like machine learning models can get 99.9% availability with cloud. Availability loss due to communication is almost 1% for 100ms applications. These results also provide requirements and future work of industrial edge computing for IIoT on real-time cloud.