Time-optimized sequential decision making for service management in smart city environments

Edge Computing is a new computing paradigm that aims to enhance the Quality of Service (QoS) applications running close end users. However, edge nodes can only host subset all available services and collected data due their limited storage processing capacity. As result, management faces multiple challenges. One significant challenge present at especially when demand for them may change over time. The execution requested by incoming tasks, however, be absent on an node, which not so rare in real environments, e.g., smart cities setting. Therefore, should deal with timely wisely decision whether perform service replication (pull-action) or tasks offloading (push-action) peer are locally present. In this paper, we address decision-making introducing intelligent mechanism formulated upon principles optimal stopping theory applying our time-optimized scheme different scenarios management. A performance evaluation includes two models comparative assessment one model provided found respective literature expose behavior advantages approach OST. Our methodology (OST) showcases achieved optimized decisions given specific objective functions as demonstrated experimental results.