| dc.description.abstract | Smart cities and healthcare services have been gaining much attention in recent years, as the benefits provided by this field of research are significant and improve quality of life. Systems can proactively detect health problems by monitoring a person’s vital signs and making automated decisions in order to prevent these problems from worsening. Examples include health services sending notifications to the user’s smartphone when a health problem is detected, or automatically calling an ambulance when vital signs indicate that a severe problem is about to happen in the next minutes. With this context in mind, we highlight two essential requirements that architectures for smart cities should consider to achieve high quality of experience in the field of health. The first is to execute health services with short response times when ingesting high-priority vital signs, so people with comorbidities can have health problems identified as soon as possible. The second is to employ scalability techniques to deal with high usage peaks caused by people concentrating in specific city neighborhoods. Related works already propose solutions to minimize response time, but we argue that considering the semantics of user priority and service priority in the field of health is essential to ensure the appropriate quality of experience. Our understanding is that users with comorbidities should have more priority than healthy users when computing resources are scarce, and specific health services should have higher priority than others. With this in mind, this thesis contributes to this field of research by proposing SmartVSO - a computational model of a hierarchic, scalable, fog-cloud architecture, which executes health services with optimized execution throughput and minimized response time for critical vital signs. We employ fog computing to achieve short response times and cloud computing to achieve virtually infinite computing resources. A first heuristic favors critical vital signs when disputing for scarce, low-latency resources during high usage peaks. This is encompassed by calculating a ranking for the incoming vital sign, which considers both user and service priorities that semantically represent the vital sign’s importance. When vital signs
collide with the same calculated ranking, a second heuristic uses forecasting techniques to favor health services that will complete faster, with the goal of optimizing execution throughput. We consider serverless computing as the primary technology for deploying and running health services because this allows authorized third parties to implement their own health services in a distributed and pluggable approach, without recompiling the proposed decision-making modules. Finally, we introduce a recursive mechanism that offloads vital signs to parent fog nodes when local computing resources are overloaded, until the vital sign can be processed on a fog node with available computing resources, or is offloaded to the cloud as the last resort. An experiment with 80.000 vital signs indicates that our solution processes 60% of critical vital signs in no more than 5,3 seconds, while a naive architecture that does not employ fog computing and does not favor critical vital signs takes up to 231 minutes (around 3 hours and 51 minutes) to process 60% of critical vital signs. | en |
