A decentralized prediction-based workflow load balancing architecture for cloud/fog/IoT environments

Processing of data gathered from new communication devices, such as Internet of Things (IoT)-based technology, has grown dramatically in the past decade. Resource management plays a vital role in cloud/fog-based platforms' efficiency. Alternatively, a deadline-based workflow scheduling mechanism is an approach to resource management that increases cloud/fog computing efficiency. However, most proposed methods may overload some resources and underload others. Consequently, adopting a proper load-balancing approach has a major impact on optimizing Quality of Service (QoS) and improving customer satisfaction. This paper presents a 4-layer software architecture for analyzing workflows and dynamic resources in cloud/fog/IoT environments to address such a problem. This approach also considers workload and presence prediction of IoT nodes as dynamic resources. Moreover, the 4 + 1 architectural view models represent architecture layers, components, and significant interactions. Architecture components are ultimately proposed to meet quality attributes such as availability, reliability, performance, and scalability. The proposed architecture evaluation is according to the Architecture Tradeoff Analysis Method (ATAM) as a scenario-based technique. Compared with previous works, various scenarios, and more quality attributes are discussed within this evaluation, in addition to analyzing and predicting workload and the presence prediction of dynamic resources.