Network Scheduling and Compute Resource Aware Task Placement in Datacenters

Ali Munir, Ting He, Ramya Raghavendra, Franck Le, Alex X. Liu

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

To improve the performance of data-intensive applications, existing datacenter schedulers optimize either the placement of tasks or the scheduling of network flows. The task scheduler strives to place tasks close to their input data (i.e., maximize data locality) to minimize network traffic, while assuming fair sharing of the network. The network scheduler strives to finish flows as quickly as possible based on their sources and destinations determined by the task scheduler, while the scheduling is based on flow properties (e.g., size, deadline, and correlation) and not bound to fair sharing. Inconsistent assumptions of the two schedulers can compromise the overall application performance. In this paper, we propose NEAT+, a task scheduling framework that leverages information from the underlying network scheduler and available compute resources to make task placement decisions. The core of NEAT+ is a task completion time predictor that estimates the completion time of a task under given network condition and a given network scheduling policy. NEAT+ leverages the predicted task completion times to minimize the average completion time of active tasks. Evaluation using ns2 simulations and real-testbed shows that NEAT+ improves application performance by up to 3.7times for the suboptimal network scheduling policies and up to 33% for the optimal network scheduling policy.

Original languageEnglish (US)
Article number9171497
Pages (from-to)2435-2448
Number of pages14
JournalIEEE/ACM Transactions on Networking
Volume28
Issue number6
DOIs
StatePublished - Dec 2020

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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