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Memory Elasticity Benchmark Cloud computing handles a vast share of the world's computing, but it is not as efficient as it could be due to its lack of support for memory elasticity. An environment that supports memory elasticity can dynamically change the size of the application's memory while it's running, thereby optimizing the entire system's use of memory. However, this means at least some of the applications must be memory-elastic. A memory elastic application can deal with memory size changes enforced on it, making the most out of all of the memory it has available at any one time. The performance of an ideal memory-elastic application would not be hindered by frequent memory changes. Instead, it would depend on global values, such as the sum of memory it receives over time. Memory elasticity has not been achieved thus far due to a circular dependency problem. On the one hand, it is difficult to develop computer systems for memory elasticity without proper benchmarking, driven by actual applications. On the other, application developers do not have an incentive to make their applications memory-elastic, when real-world systems do not support this property nor do they incentivize it economically. To overcome this challenge, this document propose a system of memory-elastic benchmarks and an evaluation methodology for an application's memory elasticity characteristics. We validate this methodology by using it to accurately predict the performance of an application, with a maximal deviation of 8% on average. The proposed benchmarks and methodology have the potential to help bootstrap computer systems and applications towards memory elasticity. ACM Reference Format: Liran Funaro, Orna Agmon Ben-Yehuda, and Assaf Schuster. 2020. Memory Elasticity Benchmark. In The 13th ACM International Sys- tems and Storage Conference (SYSTOR ’20), June 2–4, 2020, Haifa, Israel. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/ 3383669.3398277

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