D2.4 - Revised Semantics for IoT and Cloud Resources

Within an IoT Cloud application, various subsystems and layers of IoT, edge and cloud infrastructures are involved and we need to make sure that the involved components and data are interoperable w.r.t. data models, protocols and access policies. Such requirements must be addressed by IoT Cloud applications and platforms. Furthermore, such requirements often need to be fulfilled at runtime. Tackling such requirements requires huge effort in software development tasks to ensure interoperable interfaces, protocols and data. At runtime, it is also extremely challenging to orchestrate and configure software services for interoperability under elastic demands. In this work, we show our rsiHub toolset which allows to dynamically provision resource slices of IoT, network functions and cloud services that are interoperable for requirements from IoT Cloud applications. We combine various software architecture models and designs with runtime techniques to leverage diverse types of metadata, software services, and data processing functions to enable and simplify interoperability tasks for IoT Cloud developers and users. We will demonstrate the rsiHub framework together with IoTCloudSamples to show how our toolset will substantially simplify effort in building complex interoperable IoT Cloud designs through several real world examples.

Despite the wide utilization of cloud computing (e.g., services, applications, and resources), some of the services, applications, and smart devices are not able to fully benefit from this attractive cloud computing paradigm due to the following issues: (1) smart devices might be lacking in their capacity (e.g., processing, memory, storage, battery, and resource allocation), (2) they might be lacking in their network resources, and (3) the high network latency to centralized server in cloud might not be efficient for delay-sensitive application, services, and resource allocations requests. Fog computing is promising paradigm that can extend cloud resources to edge of network, solving the abovementioned issue. As a result, in this work, we propose an architecture of IoT service delegation and resource allocation based on collaboration between fog and cloud computing. We provide new algorithm that is decision rules of linearized decision tree based on three conditions (services size, completion time, and VMs capacity) for managing and delegating user request in order to balance workload. Moreover, we propose algorithm to allocate resources to meet service level agreement (SLA) and quality of services (QoS) as well as optimizing big data distribution in fog and cloud computing. Our simulation result shows that our proposed approach can efficiently balance workload, improve resource allocation efficiently, optimize big data distribution, and show better performance than other existing methods.