2019
Nadig, D.; Ramamurthy, B.; Bockelman, B.; Swanson, D.
APRIL: An Application-Aware, Predictive and Intelligent Load Balancing Solution for Data-Intensive Science Proceedings Article
In: IEEE INFOCOM 2019 - IEEE Conference on Computer Communications, pp. 1909–1917, 2019.
Abstract | BibTeX | Tags: Big Data, Correlation, Deep learning, Load management, Load modeling, Predictive models, Servers | Links:
@inproceedings{nadig_april:_2019,
title = {APRIL: An Application-Aware, Predictive and Intelligent Load Balancing Solution for Data-Intensive Science},
author = {D. Nadig and B. Ramamurthy and B. Bockelman and D. Swanson},
url = {https://deepaknadig.com/wp-content/uploads/2021/09/Nadig-et-al.-2019-APRIL-An-Application-Aware-Predictive-and-Intell.pdf},
doi = {10.1109/INFOCOM.2019.8737537},
year = {2019},
date = {2019-04-01},
urldate = {2019-04-01},
booktitle = {IEEE INFOCOM 2019 - IEEE Conference on Computer Communications},
pages = {1909--1917},
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.},
keywords = {Big Data, Correlation, Deep learning, Load management, Load modeling, Predictive models, Servers},
pubstate = {published},
tppubtype = {inproceedings}
}
2017
Nadig, D.; Ramamurthy, B.; Bockelman, B.; Swanson, D.
Differentiated network services for data-intensive science using application-aware SDN Best Paper Proceedings Article
In: 2017 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), pp. 1–6, 2017.
Abstract | BibTeX | Tags: application-aware SDN, application-aware software-defined networking, application-awareness, Compact Muon Solenoid, Cryptography, Data transfer, data-intensive science, data-intensive science projects, differentiated network services, DiffServ networks, Engines, fault-tolerant protocols, gravitational wave detectors, gridftp, GridFTP protocol, high-delay wide area network, high-energy physics projects, Laser Interferometer Gravitational-Wave Observatory, Metadata, physics computing, policy-driven approach, Protocols, queueing theory, queuing system, Servers, software defined networking, software defined networks, Wide area networks | Links:
@inproceedings{nadig_differentiated_2017,
title = {Differentiated network services for data-intensive science using application-aware SDN},
author = {D. Nadig and B. Ramamurthy and B. Bockelman and D. Swanson},
url = {https://deepaknadig.com/wp-content/uploads/2021/09/Anantha-et-al.-2017-Differentiated-network-services-for-data-intensive.pdf},
doi = {10.1109/ANTS.2017.8384105},
year = {2017},
date = {2017-12-01},
urldate = {2017-12-01},
booktitle = {2017 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)},
pages = {1--6},
abstract = {Data-intensive science projects rely on scalable, high-performance, fault-tolerant protocols for transferring large-volume data over a high-bandwidth, high-delay wide area network (WAN). The commonly used protocol for WAN data distribution is the GridFTP protocol. GridFTP uses encrypted sessions for data transfers and does not exchange any information with the network-layer resulting in reduced flexibility for network management at the site-level. We propose an application-aware software-defined networking (SDN) approach for providing differentiated network services for high-energy physics projects such as Compact Muon Solenoid (CMS) and Laser Interferometer Gravitational-Wave Observatory (LIGO). We demonstrate a policy-driven approach for differentiating network traffic by exploiting application- and network-layer collaboration to achieve accurate accounting of resources used by each project. We implement two strategies, a 7-3 queuing system, and a 10-3 queuing system, and show that the 10-3 strategy provides an additional capacity improvement of 11.74% over the 7-3 strategy.},
keywords = {application-aware SDN, application-aware software-defined networking, application-awareness, Compact Muon Solenoid, Cryptography, Data transfer, data-intensive science, data-intensive science projects, differentiated network services, DiffServ networks, Engines, fault-tolerant protocols, gravitational wave detectors, gridftp, GridFTP protocol, high-delay wide area network, high-energy physics projects, Laser Interferometer Gravitational-Wave Observatory, Metadata, physics computing, policy-driven approach, Protocols, queueing theory, queuing system, Servers, software defined networking, software defined networks, Wide area networks},
pubstate = {published},
tppubtype = {inproceedings}
}