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 al-location 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 na-ture 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.