SoftSKU: Optimizing Server Architectures for Microservice Diversity @Scale

By Akshitha Sriraman, Abhishek Dhanotia, Thomas F. Wenisch. 2019

115 citations recorded by the source corpus.

The variety and complexity of microservices in warehouse- scale data centers has grown precipitously over the last few years to support a growing user base and an evolving product portfolio. Despite accelerating microservice diversity, there is a strong requirement to limit diversity in underlying server hardware to maintain hardware resource fungibility, preserve procurement economies of scale, and curb qualification/test overheads. As such, there is an urgent need for strategies that enable limited server CPU architectures (a.k.a “SKUs”) to provide performance and energy efficiency over diverse microservices. To this end, we first undertake a comprehensive characterization of the top seven microservices that run on the compute-optimized data center fleet at Facebook. Our characterization reveals profound diversity in OS and I/O interaction, cache misses, memory bandwidth utilization, instruction mix, and CPU stall behavior. Whereas customizing a CPU SKU for each microservice might be beneficial, it is prohibitive. Instead, we argue for “soft SKUs”, wherein we exploit coarse-grain (e.g., boot time) configuration knobs to tune the platform for a particular microservice. We develop a tool, μSKU, that automates search over a soft-SKU design space using A/B testing in production and demonstrate how it can obtain statistically significant gains (up to 7.2% and 4.5% performance improvement over stock and production servers, respectively) with no additional hardware requirements.

Subfields: Computer Networks and Communications, Information Systems, ML Systems & Hardware

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Citations in the corpus

Sustainable AI: Environmental Implications, Challenges and Opportunities (2021, background): “At-Scale Efficiency Optimization for Facebook Data Centers: Servers in Facebook data center fleets are customized for internal workloads only — machine learning tasks [24] or not [34], [35].”
Chasing Carbon: The Elusive Environmental Footprint of Computing (2020, background): “Recently proposed schedulers optimize infrastructure efﬁciency in terms of total power consumption while balancing performance for latency-critical and batch-processing workloads [79], [80], [89]–[91].”
TPP: Transparent Page Placement for CXL-Enabled Tiered-Memory (2022, background): “Theseworkloads constitute a significant portion of the server fleet and represent a wide variety of our workloads [67, 68].”
Accelerometer: Understanding Acceleration Opportunities for Data Center Overheads at Hyperscale (2020, methodology): “Microservices such as Cache oversubscribe threads to improve service throughput [111].”