Intelligent Pooling: Proactive Resource Provisioning in Large-scale Cloud Service

Author:

Ravikumar Deepak1,Yeo Alex2,Zhu Yiwen3,Lakra Aditya3,Nagulapalli Harsha3,Ravindran Santhosh3,Suh Steve3,Dutta Niharika3,Fogarty Andrew3,Park Yoonjae3,Khushalani Sumeet3,Tarafdar Arijit3,Parekh Kunal4,Krishnan Subru5

Affiliation:

1. Purdue University, USA

2. Netflix, USA

3. Microsoft, USA

4. Microsoft, India

5. Microsoft, Spain

Abstract

The proliferation of big data and analytic workloads has driven the need for cloud compute and cluster-based job processing. With Apache Spark, users can process terabytes of data at ease with hundreds of parallel executors. Providing low latency access to Spark clusters and sessions is a challenging problem due to the large overheads of cluster creation and session startup. In this paper, we introduce Intelligent Pooling, a system for proactively provisioning compute resources to combat the aforementioned overheads. Our system (1) predicts usage patterns using an innovative hybrid Machine Learning (ML) model with low latency and high accuracy; and (2) optimizes the pool size dynamically to meet customer demand while reducing extraneous COGS. The proposed system auto-tunes its hyper-parameters to balance between performance and operational cost with minimal to no engineering input. Evaluated using large-scale production data, Intelligent Pooling achieves up to 43% reduction in cluster idle time compared to static pooling when targeting 99% pool hit rate. Currently deployed in production, Intelligent Pooling is on track to save tens of million dollars in COGS per year as compared to traditional pre-provisioned pools.

Publisher

Association for Computing Machinery (ACM)

Reference52 articles.

1. Machine Learning at Microsoft with ML.NET

2. A Aleksiyants, O Borisenko, D Turdakov, A Sher, and S Kuznetsov. 2015. Implementing Apache Spark jobs execution and Apache Spark cluster creation for Openstack Sahara. 27, 5 (2015), 35--48.

3. Amazon. 2022. Amazon AWS. Retrieved July 2 2022 from https://aws.amazon.com

4. Apache. 2022. Apache Flink. Retrieved July 2 2022 from https://flink.apache.org

5. Apache. 2022. Apache Storm. Retrieved July 2 2022 from https://storm.apache.org

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