Function-as-a-Service (FaaS) is becoming an increasingly popular cloud-deployment paradigm for serverless computing that frees application developers from managing the infrastructure. At the same time, it allows cloud providers to assert control in workload consolidation, i.e., co-locating multiple containers on the same server, thereby achieving higher server utilization, often at the cost of higher end-to-end function request latency. Interestingly, a key aspect of serverless latency management has not been well studied: the trade-off between application developers' latency goals and the FaaS providers' utilization goals.
This paper presents a multi-faceted, measurement-driven study of latency variation in serverless platforms that elucidates this trade-off space. We obtained production measurements by executing FaaS benchmarks on IBM Cloud and a private cloud to study the impact of workload consolidation, queuing delay, and cold starts on the end-to-end function request latency. We draw several conclusions from the characterization results. For example, increasing a container's allocated memory limit from 128 MB to 256 MB reduces the tail latency by 2× but has 1.75× higher power consumption and 59% lower CPU utilization.
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Gemini: Learning to Manage CPU Power for Latency-Critical Search Engines
Saving energy for latency-critical applications like
web search can be challenging because of their strict tail latency
constraints. State-of-the-art power management frameworks use
Dynamic Voltage and Frequency Scaling (DVFS) and Sleep states
techniques to slow down the request processing and finish the
search just-in-time. However, accurately predicting the compute
demand of a request can be difficult. In this paper, we present
Gemini, a novel power management framework for latency-
critical search engines. Gemini has two unique features to
capture the per query service time variation. First, at light loads
without request queuing, a two-step DVFS is used to manage
the CPU power. Our two-step DVFS selects the initial CPU
frequency based on the query specific service time prediction
and then judiciously boosts the initial frequency at the right
time to catch-up to the deadline. The determination of boosting
time further relies on estimating the error in the prediction of
individual query’s service time. At high loads, where there is
request queuing, only the current request being executed and
the critical request in the queue adopt a two-step DVFS. All the
other requests in-between use the same frequency to reduce the
frequency transition overhead. Second, we develop two separate
neural network models, one for predicting the service time and
the other for the error in the prediction. The combination of
these two predictors significantly improves the power saving and
tail latency results of our two-step DVFS. Gemini is implemented
on the Solr search engine. Evaluations on three representative
query traces show that Gemini saves 41% of the CPU power,
and is better than other state-of-the-art techniques.
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- Award ID(s):
- 1763929
- NSF-PAR ID:
- 10299300
- Date Published:
- Journal Name:
- 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO)
- Page Range / eLocation ID:
- 637 to 349
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/MICRO50266.2020.00059
