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

Deepak Nadig, Byrav Ramamurthy, Brian Bockelman, David Swanson
April 2019
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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.

Type
Conference paper
Publication
IEEE INFOCOM 2019 - IEEE Conference on Computer Communications
Resource Allocation Data Analysis Grid Computing Deep Learning Recurrent Neural Nets Application Metadara Application-Aware Application-Aware Intelligent Load Balancing System Application-Aware Predictive Analytics System Big Data Connection Load Prediction Correlation Data-Intensive Science Workflows Dataset Deeo Recurrent Neural Network Deep Learning Techniques Deep RNN Predictive Analytics System Electronic Data Interchange Forecasting Model Gated Recurrent Unit GridFTP Connection Loads GridFTP Transfer Connections High Energy Physics Instrumentation Computing Intelligent Load Balancer Intelligent Load Balancing Solution Learning (Artificial Intelligence) Linux Linux Virtual Server Cluster Load Forecast Information Load Management Load Modeling Long-Term Temporal Dependencies LVS Cluster Meta Data Multilayer Perceptions Predictive Predictive Analytics Solution Predictive Models Servers Time 18.0 Month Time Series Time-Series Modeling US CMS Tier-2 Site Workflow Memberships
Byrav Ramamurthy
Byrav Ramamurthy
Professor & PI

My research areas include optical and wireless networks, peer-to-peer networks for multimedia streaming, network security and telecommunications. My research work is supported by the U.S. National Science Foundation, U.S. Department of Energy, U.S. Department of Agriculture, NASA, AT&T Corporation, Agilent Tech., Ciena, HP and OPNET Inc.