Microservice provisioning and event-driven adaptation in heterogeneous IoT settings

In mission-critical Internet-of-Things environments, where data from diverse data sources (stationary and mobile) must be analyzed rapidly, ensuring reliable service provisioning for the execution of analytics is critical. Under dynamic conditions and changing infrastructure capabilities, adaptive methods are required to handle surges in data volumes and execute complex services for IoT applications. In this paper, we propose AMPHI–an adaptive microservice provisioning framework that handles various mission-aware workflows in combined stationary-mobile IoT environments to enable flexible deployment of containerized microservices. AMPHI exploits the variability in cost and performance among operators that implement similar functionalities but with distinct cost/quality/time tradeoffs, and provides solutions for intelligent selection, placement, and sharing of operators across a hybrid set of devices (stationary sensors and unmmaned aerial/ground vehicle) to maximize Quality of Service (QoS) and resource efficiency under dynamic situations. We formulate the microservice provisioning and instantiation as an (NP-hard) optimization problem and design efficient heuristic approaches to deploy microservices by utilizing application and system context. The system’s robustness and resilience are guaranteed by two variant paradigms in AMPHI’s design, one provides with optimal microservice provisoining solutions, and the other makes adaptation operations on existing solutions when abnormal events occur. We study a basic version of AMPHI that focuses on optimal microservice provisioning; the basic version is then enhanced to create a robust and extensible variant of AMPHI that ensures dependable operation through operators that can deal with unexpected failures and urgent service requests. AMPHI is evaluated in the context of smart firefighting with high-rise fires utilizing building sensors and autonomous aerial mobile units. Through real-world testbeds and extensive simulations, we show how AMPHI allows flexible and cost-effective execution of dynamically changing IoT workflows.