Meeting Users' QoS in a Edge-to-Cloud Platform via Optimally Placing Services and Scheduling Tasks

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 user deadlines. We formulate the problem of meeting users' deadlines while minimizing the total cost to 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 user numbers, the QoS constraint, and the cost difference between a 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 complexity.