  This paper considers the problem of service placement and task scheduling on
a three-tiered edge-to-cloud platform when user requests must be met by a
certain deadline. Time-sensitive applications (e.g., augmented reality, gaming,
real-time video analysis) have tight constraints that must be met. With
multiple possible computation centers, the "where" and "when" of solving these
requests becomes paramount when meeting their deadlines. We formulate the
problem of meeting users' deadlines while minimizing the total cost of the
edge-to-cloud service provider as an Integer Linear Programming (ILP) problem.
We show the NP-hardness of this problem, and propose two heuristics based on
making decisions on a local vs global scale. We vary the number of users, the
QoS constraint, and the cost difference between remote cloud and cloudlets(edge
clouds), and run multiple Monte-Carlo runs for each case. Our simulation
results show that the proposed heuristics are performing close to optimal while
reducing the complexity.
