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

TL;DR

This paper addresses service placement and task scheduling in an edge-to-cloud platform to meet user deadlines cost-effectively, proposing heuristics that perform near-optimally with reduced complexity.

Contribution

It formulates the deadline-aware service placement and scheduling as an ILP, proves its NP-hardness, and introduces heuristics for practical solutions.

Findings

Heuristics achieve near-optimal performance

Proposed methods reduce complexity significantly

Simulation validates effectiveness across scenarios

Abstract

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.