Journal
2017 IEEE 23RD INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED SYSTEMS (ICPADS)
Volume -, Issue -, Pages 511-518Publisher
IEEE
DOI: 10.1109/ICPADS.2017.00073
Keywords
Brokerage; Priority; Fairness; Pricing; Resource reservation
Funding
- National Key Research and Development Program [2016YEB1000501]
- 863 Hi-Tech Research and Development Program [2015AA01A203]
- National Science Foundation of China [61232008]
- International Science & Technology Cooperation Program of China [2015DFE12860]
- U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research Program [DE-AC02-06CH11357]
Ask authors/readers for more resources
A practical problem facing Infrastructure-as-a-Service (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 2.5x than that without considering priority for offline algorithm, and 3.7x for online algorithm.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available