Dynamic Service Placement for Mobile Micro-Clouds with Predicted Future Costs

TL;DR

This paper presents a dynamic service placement strategy for mobile micro-clouds that leverages cost predictions to optimize placement decisions, balancing offline optimal solutions with real-time online algorithms and analyzing the impact of prediction errors.

Contribution

It introduces an online approximation algorithm for service placement in mobile micro-clouds that is $O(1)$-competitive and considers prediction errors to optimize look-ahead window size.

Findings

The online algorithm is $O(1)$-competitive for broad cost functions.

Prediction error analysis helps determine optimal look-ahead window size.

Simulations with real-world traces validate the approach.

Abstract

Mobile micro-clouds are promising for enabling performance-critical cloud applications. However, one challenge therein is the dynamics at the network edge. In this paper, we study how to place service instances to cope with these dynamics, where multiple users and service instances coexist in the system. Our goal is to find the optimal placement (configuration) of instances to minimize the average cost over time, leveraging the ability of predicting future cost parameters with known accuracy. We first propose an offline algorithm that solves for the optimal configuration in a specific look-ahead time-window. Then, we propose an online approximation algorithm with polynomial time-complexity to find the placement in real-time whenever an instance arrives. We analytically show that the online algorithm is $O(1)$-competitive for a broad family of cost functions. Afterwards, the impact of prediction errors is considered and a method for finding the optimal look-ahead window size is proposed, which minimizes an upper bound of the average actual cost. The effectiveness of the proposed approach is evaluated by simulations with both synthetic and real-world (San Francisco taxi) user-mobility traces. The theoretical methodology used in this paper can potentially be applied to a larger class of dynamic resource allocation problems.