April: An Application-Aware, Predictive and Intelligent Load Balancing Solution for Data-Intensive Science

Deepak Nadig, Byrav Ramamurthy, Brian Bockelman, David Swanson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose an application-aware intelligent load balancing system for high-throughput, distributed computing, and data-intensive science workflows. We leverage emerging deep learning techniques for time-series modeling to develop an application-aware predictive analytics system for accurately forecasting GridFTP connection loads. Our solution integrates with a major U.S. CMS Tier-2 site; we use a real dataset representing 670 million GridFTP transfer connections measured over 18 months to drive our predictive analytics solution. First, we perform extensive analysis on this dataset and use the connection loads as an example to study the temporal dependencies between various user-roles and workflow memberships. We use the analysis to motivate the design of a gated recurrent unit (GRU) based deep recurrent neural network (RNN) for modeling long-term temporal dependencies and predicting connection loads. We develop a novel application-aware, predictive and intelligent load balancer, APRIL, that effectively integrates application metadata and load forecast information to maximize server utilization. We conduct extensive experiments to evaluate the performance of our deep RNN predictive analytics system and compare it with other approaches such as ARIMA and multi-layer perceptron (MLP) predictors. The results show that our forecasting model, depending on the user-role, performs between 5.88%-92.6% better than the alternatives. We also demonstrate the effectiveness of APRIL by comparing it with the load balancing capabilities of an existing production Linux Virtual Server (LVS) cluster. Our approach improves server utilization, on an average, between 0.5 to 11 times, when compared with its LVS counterpart.

Original languageEnglish (US)
Title of host publicationINFOCOM 2019 - IEEE Conference on Computer Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1909-1917
Number of pages9
ISBN (Electronic)9781728105154
DOIs
StatePublished - Apr 2019
Event2019 IEEE Conference on Computer Communications, INFOCOM 2019 - Paris, France
Duration: Apr 29 2019May 2 2019

Publication series

NameProceedings - IEEE INFOCOM
Volume2019-April
ISSN (Print)0743-166X

Conference

Conference2019 IEEE Conference on Computer Communications, INFOCOM 2019
CountryFrance
CityParis
Period4/29/195/2/19

ASJC Scopus subject areas

  • Computer Science(all)
  • Electrical and Electronic Engineering

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    Nadig, D., Ramamurthy, B., Bockelman, B., & Swanson, D. (2019). April: An Application-Aware, Predictive and Intelligent Load Balancing Solution for Data-Intensive Science. In INFOCOM 2019 - IEEE Conference on Computer Communications (pp. 1909-1917). [8737537] (Proceedings - IEEE INFOCOM; Vol. 2019-April). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/INFOCOM.2019.8737537