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

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