Characterization of Data Compression in Datacenters

Geonhwa Jeong, Bikash Sharma, Nick Terrell, Abhishek Dhanotia, Zhiwei Zhao, Niket Agarwal, Arun Kejariwal, Tushar Krishna

Abstract: Data compression has emerged as a promising technique to alleviate the memory, storage, and network cost with some associated compute overheads in warehouse-scale datacenter services. Despite being one of the most important components of the overall datacenter taxes, there has not been a comprehensive characterization of compression usage in datacenter workloads. Such characterization is paramount for both compression software developers and hardware accelerator designers as it can help them make optimal design trade-offs decisions in terms of performance, power, and cost while meeting service-level agreements of target applications. Moreover, it can provide data-driven insights to application developers to find optimal compression configuration choices for their services. In this paper, we first provide a holistic characterization of compression as used by various warehouse-scale datacenter services at a global social media provider, Meta. Next, we deep dive into a few representative use cases of compression in the production environment and characterize compression usage of the services while running live traffic. Finally, we conduct sensitivity studies to understand how different compression configurations are relevant to the overall infrastructure cost, followed by future research directions for compression hardware and software development.