# A Debt-Aware Learning Approach for Resource Adaptations in Cloud Elasticity Management

## TL;DR

This paper introduces a debt-aware reinforcement learning method for cloud resource management that balances economic costs and performance, improving long-term utility.

## Contribution

It proposes a novel reinforcement learning approach that incorporates technical debt considerations into autonomous cloud elasticity management.

## Key findings

- Higher utility achieved for cloud customers.
- Maintains expected performance levels.
- Effective trade-off between economics and performance.

## Abstract

Elasticity is a cloud property that enables applications and its execution systems to dynamically acquire and release shared computational resources on demand. Moreover, it unfolds the advantage of economies of scale in the cloud through a drop in the average costs of these shared resources. However, it is still an open challenge to achieve a perfect match between resource demand and provision in autonomous elasticity management. Resource adaptation decisions essentially involve a trade-off between economics and performance, which produces a gap between the ideal and actual resource provisioning. This gap, if not properly managed, can negatively impact the aggregate utility of a cloud customer in the long run. To address this limitation, we propose a technical debt-aware learning approach for autonomous elasticity management based on a reinforcement learning of elasticity debts in resource provisioning; the adaptation pursues strategic decisions that trades off economics against performance. We extend CloudSim and Burlap to evaluate our approach. The evaluation shows that a reinforcement learning of technical debts in elasticity obtains a higher utility for a cloud customer, while conforming expected levels of performance.