On a Feedback Control-based Mechanism of Bidding for Cloud Spot Service

TL;DR

This paper introduces a feedback control-based bidding mechanism for cloud spot services that simplifies decision-making and improves success rates by mimicking human intuition, validated through simulations on Amazon's spot prices.

Contribution

It proposes a novel feedback control approach for cloud spot bidding that reduces complexity and enhances bidding success compared to existing strategies.

Findings

Achieves better trade-off between rationality and success rate.

Outperforms five comparable bidding strategies in simulations.

Can be extended to incorporate external constraints.

Abstract

As a cost-effective option for Cloud consumers, spot service has been considered to be a significant supplement for building a full-fledged market economy for the Cloud ecosystem. However, unlike the static and straightforward way of trading on-demand and reserved Cloud services, the market-driven regulations of employing spot service could be too complicated for Cloud consumers to comprehensively understand. In particular, it would be both difficult and tedious for potential consumers to determine suitable bids from time to time. To reduce the complexity in applying spot resources, we propose to use a feedback control to help make bidding decisions. Based on an arccotangent-function-type system model, our novel bidding mechanism imitates fuzzy and intuitive human activities to refine and issue new bids according to previous errors. The validation is conducted by using Amazon's historical spot price trace to perform a set of simulations and comparisons. The result shows that the feedback control-based mechanism obtains a better trade-off between bidding rationality and success rate than the other five comparable strategies. Although this mechanism is only for black-box bidding (price prediction) at this current stage, it can be conveniently and gradually upgraded to take into account external constraints in the future.