Maximizing the Profit of Cloud Broker with Priority Aware Pricing

Wang, Xinhou, Wu, Song, Wang, Kezhi, Di, Sheng, Jin, Hai, Yang, Kun and Ou, Shumao (2018) Maximizing the Profit of Cloud Broker with Priority Aware Pricing. In: 2017 IEEE 23rd International Conference on Parallel and Distributed Systems. IEEE, pp. 511-518. ISBN 978-1-5386-2129-5

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Abstract

A practical problem facing Infrastructure-as-aService (IaaS) cloud users is how to minimize their costs by choosing different pricing options based on their own demands. Recently, cloud brokerage service is introduced to tackle this problem. But due to the perishability of cloud resources, there still exists a large amount of idle resource waste during the reservation period of reserved instances. This idle resource waste problem is challenging cloud broker when buying reserved instances to accommodate users’ job requests. To solve this challenge, we find that cloud users always have low priority jobs (e.g., non latency-sensitive jobs) which can be delayed to utilize these idle resources. With considering the priority of jobs, two problems need to be solved. First, how can cloud broker leverage jobs’ priorities to reserve resources for profit maximization? Second, how to fairly price users’ job requests with different priorities when previous studies either adopt pricing schemes from IaaS clouds or just ignore the pricing issue. To solve these problems, we first design a fair and priority aware pricing scheme, PriorityPricing, for the broker which charges users with different prices based on priorities. Then we propose three dynamic algorithms for the broker to make resource reservations with the objective of maximizing its profit. Experiments show that the broker’s profit can be increased up to 2.5× than that without considering priority for offline algorithm, and 3.7× for online algorithm.

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