Cloud Deployment Tradeoffs for the Analysis of Spatially-Distributed Systems of Internet-of-Things

Internet-enabled things and devices operating in the physical world are increasingly integrated in modern distributed systems, supporting functionalities that require assurances that certain critical requirements are satisfied by the overall system. We focus here on spatially-distributed Internet-of-Things systems such as smart environments, where the dynamics of spatial distribution of entities in the system is crucial to requirements satisfaction. Analysis techniques need to be in place while systems operate to ensure that requirements are fulfilled. This may be achieved by keeping a model of the system at runtime, monitoring events that lead to changes in the spatial environment, and performing analysis. This computationally-intensive runtime assurance method cannot be supported by resource-constrained devices that populate the space and must be offloaded to the cloud. However, challenges arise regarding resource allocation and cost, especially when the workload is unknown at the system's design time. As such, it may be difficult or even impossible to guarantee application service level agreements, e.g., on response times. To this end, we instantiate spatial verification processes, integrating them to the service layer of an IoT-cloud architecture based on microservices. We propose several cloud deployments for such an architecture for assurance of spatial requirements -- based on virtual machines, containers, and the recent Functions-as-a-Service paradigm. Then, we assess deployments' tradeoffs in terms of elasticity, performance and cost by using a workload scenario from a known dataset of taxis roaming in Beijing. We argue that the approach can be replicated in the design process of similar kinds of spatially distributed Internet-of-Things systems.