Long-term resource fairness : towards economic fairness on pay-as-you-use computing systems

• Main Authors: Tang, Shanjiang, Lee, Bu-Sung, He, Bingsheng, Liu, Haikun
• Other Authors: School of Computer Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2014
• Subjects: DRNTU::Engineering::Computer science and engineering
• Online Access: https://hdl.handle.net/10356/79632; http://hdl.handle.net/10220/20381
• Institution: Nanyang Technological University

Fair resource allocation is a key building block of any shared computing system. However, MemoryLess Resource Fairness (MLRF), widely used in many existing frameworks such as YARN, Mesos and Dryad, is not suitable for pay-as-you-use computing. To address this problem, this paper proposes Long-Term Resource Fairness (LTRF), a novel fair resource allocation mechanism. We show that LTRF satisfies several highly desirable properties. First, LTRF incentivizes clients to share resources via group-buying by ensuring that no client is better off in a computing system that she buys and uses individually. Second, LTRF incentivizes clients to submit non-trivial workloads and be willing to yield unneeded resources to others. Third, LTRF has a resource-as-you-pay fairness property, which ensures the amount of resources that each client should get according to her monetary cost, despite that her resource demand varies over time. Finally, LTRF is strategy-proof, since it can make sure that a client cannot get more resources by lying about her demand. We have implemented LTRF in YARN by developing LTYARN, a long-term YARN fair scheduler, and shown that it leads to a better resource fairness than other state-of-the-art fair schedulers.