The advances of Machine Learning (ML) have sparked a growing demand of ML-as-a-Service: developers train ML models and publish them in the cloud as online services to provide low-latency inference at scale. The key challenge of ML model serving is to meet the response-time Service-Level Objectives (SLOs) of inference workloads while minimizing the serving cost. In this paper, we tackle the dual challenge of SLO compliance and cost effectiveness with MArk (Model Ark), a general-purpose inference serving system built in Amazon Web Services (AWS). MArk employs three design choices tailor-made for inference workload. First, MArk dynamically batches requests and opportunistically serves them using expensive hardware accelerators (e.g., GPU) for improved performance-cost ratio. Second, instead of relying on feedback control scaling or over-provisioning to serve dynamic workload, which can be too slow or too expensive for inference serving, MArk employs predictive autoscaling to hide the provisioning latency at low cost. Third, given the stateless nature of inference serving, MArk exploits the flexible, yet costly serverless instances to cover the occasional load spikes that are hard to predict. We evaluated the performance of MArk using several state-of-the-art ML models trained in popular frameworks including TensorFlow, MXNet, and Keras. Compared with the premier industrial ML serving platform SageMaker, MArk reduces the serving cost up to 7.8× while achieving even better latency performance.
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AMPS-Inf: Automatic Model Partitioning for Serverless Inference with Cost Efficiency
The salient pay-per-use nature of serverless computing has driven
its continuous penetration as an alternative computing paradigm
for various workloads. Yet, challenges arise and remain open when
shifting machine learning workloads to the serverless environment.
Specifically, the restriction on the deployment size over serverless
platforms combining with the complexity of neural network models
makes it difficult to deploy large models in a single serverless
function. In this paper, we aim to fully exploit the advantages of the
serverless computing paradigm for machine learning workloads targeting
at mitigating management and overall cost while meeting the
response-time Service Level Objective (SLO). We design and implement
AMPS-Inf, an autonomous framework customized for model inferencing in serverless computing. Driven by the cost-efficiency
and timely-response, our proposed AMPS-Inf automatically generates
the optimal execution and resource provisioning plans for
inference workloads. The core of AMPS-Inf relies on the formulation
and solution of a Mixed-Integer Quadratic Programming
problem for model partitioning and resource provisioning with the
objective of minimizing cost without violating response time SLO.
We deploy AMPS-Inf on the AWS Lambda platform, evaluate with
the state-of-the-art pre-trained models in Keras including ResNet50,
Inception-V3 and Xception, and compare with Amazon SageMaker
and three baselines. Experimental results demonstrate that AMPSInf
achieves up to 98% cost saving without degrading response time performance.
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- Award ID(s):
- 2019511
- NSF-PAR ID:
- 10338304
- Date Published:
- Journal Name:
- Proceedings of the 50th International Conference on Parallel Processing (ICPP 2021)
- Page Range / eLocation ID:
- 1 to 12
- 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/10.1145/3472456.3472501
