TY - GEN
T1 - Introducing Network Multi-Tenancy for Cloud-Based Enterprise Resource Planning
T2 - 27th IEEE International Symposium on Industrial Electronics, ISIE 2018
AU - Tiwary, Mayank
AU - Kumar, Sunil
AU - Agrawal, Pankaj Kumar
AU - Puthal, Deepak
AU - Rodrigues, Joel J.P.C.
AU - Sahoo, Kshira Sagar
AU - Sahoo, Bibhudatta
N1 - Funding Information:
ACKNOWLEDGMENTS This work was partially supported by national funding from the FCT - Fundac¸ão para a Ciência e a Tecnologia through the UID/EEA/50008/2013 Project; by Brazilian National Council for Research and Development (CNPq) via Grant No. 309335/2017-5; and by Finep, with resources from Funttel, Grant No. 01.14.0231.00, under the Centro de Referência em Radiocomunicac¸ões - CRR project of the Instituto Nacional de Telecomunicac¸ões (Inatel), Brazil.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/8/10
Y1 - 2018/8/10
N2 - The cloud service providers make a considerable investment in setting up the data centers backbone network with the aim to maximize the network resource. However, the actual utilization of the network resources is hard to predict. With the invent of Software Defined Networking (SDN) and OpenFlow protocol, the network control layer has got the capability to communicate with the applications or services which are offered by the service provider. Moreover, a Software Defined Data center suggests resource virtualization at computing, storage, and network layer. The multi-tenancy is a well-accepted architecture in cloud computing where a single instance of a software application serves multiple customers. This work is a first of its kind, which aims at maximizing the network resources with respect to multi-tenancy at the network layer. In this work, with network multitenancy, different customers IoT traffic flows are prioritized, and then network resources are allocated to the traffic flows dynamically based on the priority. We considered a scenario of Enterprise Resource Planning (ERP) solutions deployed in the cloud which offers services in the form of Software as a Service to the customers. The IoT devices deployed at the manufacturing site makes transactions on the cloud ERP. This work focuses on prioritizing the ERP- IoT traffic to meets the demands of a multi-tenant data center network. The ERP-IoT flows are prioritized using a regression based machine learning technique for predicting the response time for execution of a query caused by a traffic flow in the ERP backend server. Later, the ERP-IoT flows are assigned to multiple queues created on each network device in data center. This assignment is performed based on the traffic flow priority and Demand Supply scores, which aims at maximizing network resource utilization. During performance evaluation, we observed that the proposed work with network multi-tenancy shows more than 10% increase in service providers utility with respect to standard data center single queue operations.
AB - The cloud service providers make a considerable investment in setting up the data centers backbone network with the aim to maximize the network resource. However, the actual utilization of the network resources is hard to predict. With the invent of Software Defined Networking (SDN) and OpenFlow protocol, the network control layer has got the capability to communicate with the applications or services which are offered by the service provider. Moreover, a Software Defined Data center suggests resource virtualization at computing, storage, and network layer. The multi-tenancy is a well-accepted architecture in cloud computing where a single instance of a software application serves multiple customers. This work is a first of its kind, which aims at maximizing the network resources with respect to multi-tenancy at the network layer. In this work, with network multitenancy, different customers IoT traffic flows are prioritized, and then network resources are allocated to the traffic flows dynamically based on the priority. We considered a scenario of Enterprise Resource Planning (ERP) solutions deployed in the cloud which offers services in the form of Software as a Service to the customers. The IoT devices deployed at the manufacturing site makes transactions on the cloud ERP. This work focuses on prioritizing the ERP- IoT traffic to meets the demands of a multi-tenant data center network. The ERP-IoT flows are prioritized using a regression based machine learning technique for predicting the response time for execution of a query caused by a traffic flow in the ERP backend server. Later, the ERP-IoT flows are assigned to multiple queues created on each network device in data center. This assignment is performed based on the traffic flow priority and Demand Supply scores, which aims at maximizing network resource utilization. During performance evaluation, we observed that the proposed work with network multi-tenancy shows more than 10% increase in service providers utility with respect to standard data center single queue operations.
UR - http://www.scopus.com/inward/record.url?scp=85052395296&partnerID=8YFLogxK
U2 - 10.1109/ISIE.2018.8433724
DO - 10.1109/ISIE.2018.8433724
M3 - Conference contribution
AN - SCOPUS:85052395296
SN - 9781538637050
T3 - IEEE International Symposium on Industrial Electronics
SP - 1263
EP - 1269
BT - Proceedings - 2018 IEEE 27th International Symposium on Industrial Electronics, ISIE 2018
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 13 June 2018 through 15 June 2018
ER -
