Using a Predator-Prey Model to Explain Variations of Cloud Spot Price

TL;DR

This paper introduces a Predator-Prey model to simulate and understand the complex interactions behind cloud spot price variations, aiming to clarify market dynamics for better decision-making.

Contribution

It proposes a novel Predator-Prey modeling approach to analyze cloud spot market behaviors based on visible price traces, simplifying market understanding.

Findings

Identified regular patterns in spot price variations

Simulated demand-resource interactions effectively

Provided insights into market activity dynamics

Abstract

The spot pricing scheme has been considered to be resource-efficient for providers and cost-effective for consumers in the Cloud market. Nevertheless, unlike the static and straightforward strategies of trading on-demand and reserved Cloud services, the market-driven mechanism for trading spot service would be complicated for both implementation and understanding. The largely invisible market activities and their complex interactions could especially make Cloud consumers hesitate to enter the spot market. To reduce the complexity in understanding the Cloud spot market, we decided to reveal the backend information behind spot price variations. Inspired by the methodology of reverse engineering, we developed a Predator-Prey model that can simulate the interactions between demand and resource based on the visible spot price traces. The simulation results have shown some basic regular patterns of market activities with respect to Amazon's spot instance type m3.large. Although the findings of this study need further validation by using practical data, our work essentially suggests a promising approach (i.e.~using a Predator-Prey model) to investigate spot market activities.