Little Boxes: A Dynamic Optimization Approach for Enhanced Cloud Infrastructures

TL;DR

This paper introduces a dynamic optimization method for cloud infrastructure that strategically places cloudlets in metropolitan hotspot areas to efficiently meet fluctuating user demands, balancing computational effort and solution quality.

Contribution

It proposes a novel heuristic approach for cloudlet placement that significantly reduces computation time while maintaining high solution quality in dynamic cloud environments.

Findings

Heuristic reduces computational effort by over 1000 times.

Solution quality is maintained with less than 6% cost increase.

Effective placement of cloudlets improves QoS in metropolitan areas.

Abstract

The increasing demand for diverse, mobile applications with various degrees of Quality of Service requirements meets the increasing elasticity of on-demand resource provisioning in virtualized cloud computing infrastructures. This paper provides a dynamic optimization approach for enhanced cloud infrastructures, based on the concept of cloudlets, which are located at hotspot areas throughout a metropolitan area. In conjunction, we consider classical remote data centers that are rigid with respect to QoS but provide nearly abundant computation resources. Given fluctuating user demands, we optimize the cloudlet placement over a finite time horizon from a cloud infrastructure provider's perspective. By the means of a custom tailed heuristic approach, we are able to reduce the computational effort compared to the exact approach by at least three orders of magnitude, while maintaining a high solution quality with a moderate cost increase of 5.8% or less.