2021
Nadig, Deepak; Alaoui, Sara El; Ramamurthy, Byrav; Pitla, Santosh
ERGO: A Scalable Edge Computing Architecture for Infrastructureless Agricultural Internet of Things Proceedings Article
In: 2021 IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN), pp. 1–2, 2021, (ISSN: 1944-0375).
Abstract | BibTeX | Tags: Ag-IoT, cloud computing, Computer architecture, Edge Computing, Infrastructureless, Instruments, machine learning, Metropolitan area networks, Performance evaluation, Throughput | Links:
@inproceedings{nadig_ergo_2021,
title = {ERGO: A Scalable Edge Computing Architecture for Infrastructureless Agricultural Internet of Things},
author = {Deepak Nadig and Sara El Alaoui and Byrav Ramamurthy and Santosh Pitla},
url = {https://deepaknadig.com/wp-content/uploads/2021/09/Nadig-et-al.-2021-ERGO-A-Scalable-Edge-Computing-Architecture-for-I.pdf},
doi = {10.1109/LANMAN52105.2021.9478811},
year = {2021},
date = {2021-07-01},
urldate = {2021-07-01},
booktitle = {2021 IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN)},
pages = {1--2},
abstract = {In this paper, we propose ERGO (edge architecture for Ag-IoT), an edge-computing architecture for infrastructureless smart agriculture environments. We also develop Ag-IoT application APIs and the associated microservice infrastructure. Our implementation and evaluations show that ERGO can operate independently of cloud-backed assistance, is highly scalable, modular, and affords composability benefits to Ag-IoT systems. We also demonstrate that ERGO outperforms traditional infrastructure in response latencies and transactional throughput, on average, by over 54% and 77%, respectively.},
note = {ISSN: 1944-0375},
keywords = {Ag-IoT, cloud computing, Computer architecture, Edge Computing, Infrastructureless, Instruments, machine learning, Metropolitan area networks, Performance evaluation, Throughput},
pubstate = {published},
tppubtype = {inproceedings}
}
2018
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}
}