2018
2.
Nadig, D.; Ramamurthy, B.; Bockelman, B.; Swanson, D.
Large Data Transfer Predictability and Forecasting using Application-Aware SDN Proceedings Article
In: 2018 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), pp. 1–6, 2018.
Abstract | BibTeX | Tags: Aggregates, Analytical models, Data analysis, Data models, Data transfer, Forecasting, Predictive models | Links:
@inproceedings{nadig_large_2018,
title = {Large Data Transfer Predictability and Forecasting 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/Nadig-et-al.-2018-Large-Data-Transfer-Predictability-and-Forecasting.pdf},
doi = {10.1109/ANTS.2018.8710165},
year = {2018},
date = {2018-12-01},
urldate = {2018-12-01},
booktitle = {2018 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)},
pages = {1--6},
abstract = {Network management for applications that rely on large-scale data transfers is challenging due to the volatility and the dynamic nature of the access traffic patterns. Predictive analytics and forecasting play an important role in providing effective resource allocation strategies for large data transfers. We propose a predictive analytics solution for large data transfers using an application-aware software defined networking (SDN) approach. We perform extensive exploratory data analysis to characterize the GridFTP connection transfers dataset and present various strategies for its use with statistical forecasting models. We develop a univariate autoregressive integrated moving average (ARIMA) based prediction framework for forecasting GridFTP connection transfers. Our prediction model tightly integrates with an application-aware SDN solution to preemptively drive network management decisions for GridFTP resource allocation at a U.S. CMS Tier-2 site. Further, our framework has a mean absolute percentage error (MAPE) ranging from 6% to 10% when applied to make rolling forecasts.},
keywords = {Aggregates, Analytical models, Data analysis, Data models, Data transfer, Forecasting, Predictive models},
pubstate = {published},
tppubtype = {inproceedings}
}
Network management for applications that rely on large-scale data transfers is challenging due to the volatility and the dynamic nature of the access traffic patterns. Predictive analytics and forecasting play an important role in providing effective resource allocation strategies for large data transfers. We propose a predictive analytics solution for large data transfers using an application-aware software defined networking (SDN) approach. We perform extensive exploratory data analysis to characterize the GridFTP connection transfers dataset and present various strategies for its use with statistical forecasting models. We develop a univariate autoregressive integrated moving average (ARIMA) based prediction framework for forecasting GridFTP connection transfers. Our prediction model tightly integrates with an application-aware SDN solution to preemptively drive network management decisions for GridFTP resource allocation at a U.S. CMS Tier-2 site. Further, our framework has a mean absolute percentage error (MAPE) ranging from 6% to 10% when applied to make rolling forecasts.
1.
Nadig, D.; Ramamurthy, B.; Bockelman, B.; Swanson, D.
Optimized Service Chain Mapping and reduced flow processing with Application-Awareness Proceedings Article
In: 2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft), pp. 303–307, 2018.
Abstract | BibTeX | Tags: AAFR algorithm, application-aware flow reduction algorithm, application-awareness, Bandwidth, capacitated-SFC mapping case, capacitated/uncapacitated flow, cloud computing, commercial-off-the-shelf hardware, computer centres, Conferences, cost gains, data center, Data centers, Data models, flow processing cost, flow-processing costs, flow-to-SFC mappings, integer linear programming formulation, integer programming, Internet, linear programming, multiple data centers, network function virtualization, network functions, network functions virtualization, optimally map service function chains, optimized service chain mapping, security, Service Chaining, service function chains, SFC mapping, SFC mapping problem, SFC-ILP, software defined networks, Substrates, virtualisation, virtualized services | Links:
@inproceedings{nadig_optimized_2018,
title = {Optimized Service Chain Mapping and reduced flow processing with Application-Awareness},
author = {D. Nadig and B. Ramamurthy and B. Bockelman and D. Swanson},
url = {https://deepaknadig.com/wp-content/uploads/2021/09/Nadig-et-al.-2018-Optimized-Service-Chain-Mapping-and-reduced-flow-p.pdf},
doi = {10.1109/NETSOFT.2018.8459912},
year = {2018},
date = {2018-06-01},
urldate = {2018-06-01},
booktitle = {2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft)},
pages = {303--307},
abstract = {Network Function Virtualization (NFV) brings a new set of challenges when deploying virtualized services on commercial-off-the-shelf (COTS) hardware. Network functions can be dynamically managed to provide the necessary services on-demand and further, services can be chained together to form a larger composite. In this paper, we address an important technical problem of mapping service function chains (SFCs) across different data centers with the objective of reducing the flow processing costs. We develop an integer linear programming (ILP) formulation to optimally map service function chains to multiple data centers while adhering to the data center's capacity constraints. We propose a novel application-aware flow reduction (AAFR) algorithm to simplify the SFC-ILP to significantly reduce the number of flows processed by the SFCs. We perform a thorough study of the SFC mapping problem for multiple data centers and evaluate the performance of our proposed approach with respect to three parameters: i) impact of number of SFCs and SFC length on flow processing cost, ii) capacitated/uncapacitated flow processing cost gains, and iii) balancing flow-to-SFC mappings across data centers. Our evaluations show that our proposed AAFR algorithm reduces flow-processing costs by 70% for the capacitated-SFC mapping case over the SFC-ILP. In addition, our uncapacitated AAFR (AAFR-U) algorithm provides a further 4.1% cost-gain over its capacitated counterpart (AAFR-C).},
keywords = {AAFR algorithm, application-aware flow reduction algorithm, application-awareness, Bandwidth, capacitated-SFC mapping case, capacitated/uncapacitated flow, cloud computing, commercial-off-the-shelf hardware, computer centres, Conferences, cost gains, data center, Data centers, Data models, flow processing cost, flow-processing costs, flow-to-SFC mappings, integer linear programming formulation, integer programming, Internet, linear programming, multiple data centers, network function virtualization, network functions, network functions virtualization, optimally map service function chains, optimized service chain mapping, security, Service Chaining, service function chains, SFC mapping, SFC mapping problem, SFC-ILP, software defined networks, Substrates, virtualisation, virtualized services},
pubstate = {published},
tppubtype = {inproceedings}
}
Network Function Virtualization (NFV) brings a new set of challenges when deploying virtualized services on commercial-off-the-shelf (COTS) hardware. Network functions can be dynamically managed to provide the necessary services on-demand and further, services can be chained together to form a larger composite. In this paper, we address an important technical problem of mapping service function chains (SFCs) across different data centers with the objective of reducing the flow processing costs. We develop an integer linear programming (ILP) formulation to optimally map service function chains to multiple data centers while adhering to the data center's capacity constraints. We propose a novel application-aware flow reduction (AAFR) algorithm to simplify the SFC-ILP to significantly reduce the number of flows processed by the SFCs. We perform a thorough study of the SFC mapping problem for multiple data centers and evaluate the performance of our proposed approach with respect to three parameters: i) impact of number of SFCs and SFC length on flow processing cost, ii) capacitated/uncapacitated flow processing cost gains, and iii) balancing flow-to-SFC mappings across data centers. Our evaluations show that our proposed AAFR algorithm reduces flow-processing costs by 70% for the capacitated-SFC mapping case over the SFC-ILP. In addition, our uncapacitated AAFR (AAFR-U) algorithm provides a further 4.1% cost-gain over its capacitated counterpart (AAFR-C).