Maximizing Profit for Cloud Brokers in The Era of Cloud Computing

Abstract: Cloud computing has experienced significant growth in recent years, with an increasing number of applications migrating to the cloud, where they can centrally manage a wide range of resources, including hardware and software. These resources are delivered over the internet as services, catering to customer demands. One of the key features of cloud computing is the pay-as-you-go model. However, due to the one-hour billing cycle and the discount structure favoring long-term users, many customers end up paying more than their actual usage. To address this issue, cloud brokers rent reserved Virtual Machines (VMs) from cloud providers at a reduced cost and offer them to users on an on-demand basis at a more affordable rate than what the cloud providers offer. Moreover, cloud brokers operate on a shorter billing cycle compared to cloud providers. This study focuses on configuring a cloud broker and determining the optimal pricing for its VMs to maximize its profits. Most cloud providers offer two primary payment options for their instances: On-Demand and Reserved Instances. With On-Demand instances, users pay for compute capacity per hour, making them suitable for short-term workloads. Reserved Instances provide significant discounts but require users to commit to long rental periods, disadvantaging short-term customers. The introduction of the cloud broker serves as a novel intermediary between cloud providers and cloud users, addressing the issues of both onehour billing cycles and the limitations on discounts for short-term users. This manuscript begins by introducing a multi-server queuing model, a revenue model, and a cost model, followed by a comprehensive analysis of the factors affecting them. It defines an optimization problem aiming to determine the optimal configuration for multi-server setups and VM pricing, all aimed at profit maximization. To address this optimization problem, a heuristic method is proposed, which combines partial derivatives and a bisection search. This method offers a practical approach to finding the optimal VM price and system scale, ultimately maximizing profit. This manuscript explores the critical role of cloud brokers in the cloud computing landscape and presents a comprehensive approach to optimizing their operations, ultimately benefitting both cloud providers and users.