A Benchmark Suite for Microservices


Modern On-Line Data Intensive (OLDI) applications have evolved from monolithic systems to instead comprise numerous, distributed microservices interacting via Remote Procedure Calls (RPCs). Microservices face single-digit millisecond RPC latency goals (implying sub-ms medians)---much tighter than their monolithic ancestors that must meet ≥ 100 ms latency targets. Sub-ms-scale OS/network overheads that were once insignificant for such monoliths can now come to dominate in the sub-msscale microservice regime. It is therefore vital to characterize the influence of OS- and network-based effects on microservices. Unfortunately, widely-used academic data center benchmark suites are unsuitable to aid this characterization as they (1) use monolithic rather than microservice architectures, and (2) largely have request service times ≥ 100 ms. In this paper, we investigate how OS and network overheads impact microservice median and tail latency by developing a complete suite of microservices called µSuite that we use to facilitate our study. µSuite comprises four OLDI services composed of microservices: image similarity search, protocol routing for key-value stores, set algebra on posting lists for document search, and recommender systems. Our characterization reveals that the relationship between optimal OS/network parameters and service load is complex. Our primary finding is that non-optimal OS scheduler decisions can degrade microservice tail latency by up to ∼87%.

In proceedings of the Workshop on Architectures and Systems for Big Data held in association with the International Symposium on Computer Architecture (ASBD ‘18)
Akshitha Sriraman
Akshitha Sriraman
PhD Candidate

Akshitha Sriraman is a PhD candidate in Computer Science and Engineering at the University of Michigan. Her dissertation research is on the topic of enabling hyperscale web services. Specifically, her work bridges computer architecture and software systems, demonstrating the importance of that bridge in realizing efficient hyperscale web services via solutions that span the systems stack. Her systems solutions to improve hardware efficiency have been deployed in real hyperscale data centers and currently serve billions of users, saving millions of dollars and significantly reducing the global carbon footprint. Additionally, her hardware design proposals have influenced the design of Intel’s Alder Lake (Golden Cove and future generation) CPU architectures. Akshitha has been recognized with a Facebook Fellowship, a Rackham Merit Ph.D. Fellowship, and a CIS Full-Tuition Scholarship. She was selected for the Rising Stars in EECS Workshop and the Heidelberg Laureate Forum. Her research has been recognized with an IEEE Micro Top Picks distinction and has appeared in top computer architecture and systems venues like OSDI, ISCA, ASPLOS, MICRO, and HPCA.