Abstract
Currently, pay-as-you-go cache systems have been widely available as storage services in cloud computing, and users usually purchase long-term services to obtain higher discounts. However, users’ caching needs are not only constantly changing over time, but also affected by workload characteristics, making it difficult to always guarantee high efficiency of cache resource usage. Cache sharing is an effective way to improve cache usage efficiency. In order to incentivize users to share resources, it is necessary to ensure long-term fairness among users. However, the traditional resource allocation strategy only guarantees instantaneous fairness and is not thus suitable for pay-as-you-go cache systems. This paper proposes a long-term cache fairness allocation policy, named as FairCache, with several desired properties. First, FairCache encourages users to buy and share cache resources through group purchasing, which not only allows users to get more resources than when they buy them individually, but also encourages them to lend free resources or resources occupied by low-frequency data to others to get more revenue in the future. Second, FairCache satisfies pay-as-you-go fairness, ensuring that users’ revenue is proportional to the cost paid in a long term. Furthermore, FairCache satisfies truthfulness property, which ensures that no one can get more resources by lying. Finally, FairCache satisfies pareto efficiency property, ensuring that as long as there are tasks in progress, the system will maximize resource utilization. We implement FairCache in Alluxio, and the experimental results show that FairCache can guarantee long-term cache fairness while maximizing the efficiency of system resource usage.
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This work was funded by National Key Research and Development Program of China (2020YFC1522702) and National Natural Science Foundation of China (61972277).
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Zhou, Z. et al. (2023). Long-Term Fairness Scheduler for Pay-as-You-Use Cache Sharing Systems. In: Meng, W., Lu, R., Min, G., Vaidya, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2022. Lecture Notes in Computer Science, vol 13777. Springer, Cham. https://doi.org/10.1007/978-3-031-22677-9_18
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