Title: A Debt-Aware Learning Approach for Resource Adaptations in Cloud Elasticity Management,
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.