Budget-Aware Resource Pricing in Cloud and Edge Computing Continuum

The emergence of new computing paradigms such as Edge Computing, Fog Computing, and Far-Edge Computing is driven by the increasing demands of modern applications. Together, these paradigms form the Cloud-Edge Computing Continuum (CECC), presenting new challenges in resource allocation and incentive-driven interactions. New stakeholders are joining the business market to make a profit by selling their services (i.e., infrastructure resources, applications, or virtual resources). These actors, namely, infrastructure providers and service providers, have conflicting goals in terms of making a profit. There is a need to study and model the business interaction between these actors, especially considering the distributed nature of continuum. In this paper, we tackle the resource allocation and pricing problem in the context of CECC. We first propose a system model of the incentive interactions between actors of the continuum, where the price of resources varies based on different factors. Then, we formulate a budget-aware resource bidding problem where the objective is to jointly maximize the budget of a service provider and minimize Service Level Agreement (SLA) violations. To address this challenge, we propose a Deep Reinforcement Learning (DRL) approach that efficiently balances budget expenditure and SLA compliance. Our experimental results demonstrate that the proposed method effectively achieves a favorable trade-off between budget management and SLA satisfaction.