Online Mechanism Design for Cloud Computing

TL;DR

This paper develops online incentive-compatible mechanisms for resource allocation in cloud computing, providing algorithms with strong competitive ratios and analyzing their performance under different priority functions.

Contribution

It introduces two novel DSIC online mechanisms for cloud resource allocation, with proven competitive bounds and improved performance based on demand and priority functions.

Findings

The greedy mechanism with exponential priority has a better competitive ratio.

The dynamic programming mechanism outperforms the greedy one when demand is high.

Both mechanisms have tight competitive bounds.

Abstract

In this work, we study the problem of online mechanism design for resources allocation and pricing in cloud computing (RAPCC). We show that in general the allocation problems in RAPCC are NP-hard, and therefore we focus on designing dominant-strategy incentive compatible (DSIC) mechanisms with good competitive ratios compared to the offline optimal allocation (with the prior knowledge about the future jobs). We propose two kinds of DSIC online mechanisms. The first mechanism, which is based on a greedy allocation rule and leverages a priority function for allocation, is very fast and has a tight competitive bound. We discuss several priority functions including exponential and linear priority functions, and show that the former one has a better competitive ratio. The second mechanism, which is based on a dynamic program for allocation, also has a tight competitive ratio and performs better than the first one when the maximum demand of cloud customers is close to the capacity of the cloud provider.