NIMBLE Task Scheduling for Serverless Analytics

Systems and Networking Seminar
Speaker Name
Hong Zhang
Date and Time
The talk will be virtual on Zoom.
The Zoom link will be emailed to the CS department, or contact Jennifer Schmidt (jschmidt at to request it.

Serverless platforms facilitate transparent resource elasticity and fine-grained billing, making them an attractive choice for data analytics. We find that while schedulers in server-centric analytics frameworks typically optimize for job runtime, resource utilization and isolation via inter-job scheduling policies, serverless analytics requires them to optimize for job runtime and cost of execution instead, introducing a new task-level scheduling problem. We present NIMBLE, a fine-grained scheduling algorithm to solve this problem. By launching each task at exactly the right time, NIMBLE efficiently pipelines task executions within a job, minimizing execution cost while being Pareto-optimal between cost and runtime for arbitrary analytics jobs. To enable NIMBLE scheduling in practice, we build Caerus, a fine-grained task-level scheduler for serverless analytics frameworks. Our evaluation results show that in practice, Caerus is able to achieve both optimal cost and runtime for queries across a wide range of analytics workloads.  

Short Biography

Hong Zhang is a postdoc researcher at RISELab working with Prof. Ion Stoica. Hong is broadly interested in job scheduling and network scheduling problems to improve application performance. He received a Google PhD Fellowship in Systems and Networking.

Danyang Zhuo