Abstract
When renting computing power, fairness and overall performance are important for customers and service providers. However, strict fairness usually results in poor performance. In this paper, we study this trade-off. In our experiments, equal cache partitioning results in 131 % higher miss ratios than optimal partitioning. In order to balance fairness and performance, we propose two elastic, or movable, cache allocation baselines: elastic miss ratio baseline (EMB) and elastic cache space baseline (ECB). Furthermore, we study optimal partitions for each baseline with different levels of elasticity, and show that EMB is more effective than ECB. We also classify programs from the SPEC 2006 benchmark suite based on how they benefit or suffer from the elastic baselines, and suggest essential information for customers and service provider to choose a baseline.



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Notes
As described below, this miss ratio is predicted based on the Higher Order Theory of locality.
It is also similar to the common-logical time miss ratio defined by [10], which specifies each co-run program’s miss ratio scaled by the interleaved memory accesses from other programs.
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Chencheng Ye is a student visiting University of Rochester, funded by the Chinese Scholarship Council.
The research is supported in part by the National Science Foundation (Contract Nos. CNS-1319617, CCF-1116104, CCF-0963759), IBM CAS Faculty Fellow program, the National Science Foundation of China (Contract No. 61328201) and a grant from Huawei.
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Ye, C., Brock, J., Ding, C. et al. Rochester Elastic Cache Utility (RECU): Unequal Cache Sharing is Good Economics. Int J Parallel Prog 45, 30–44 (2017). https://doi.org/10.1007/s10766-015-0384-3
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DOI: https://doi.org/10.1007/s10766-015-0384-3