Data Analytics Using Two-Stage Intelligent Model Pipelining for Virtual Network Functions

Author(s):  
Takaya Miyazawa ◽  
Ved P. Kafle ◽  
Hitoshi Asaeda
Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 323
Author(s):  
Marwa A. Abdelaal ◽  
Gamal A. Ebrahim ◽  
Wagdy R. Anis

The widespread adoption of network function virtualization (NFV) leads to providing network services through a chain of virtual network functions (VNFs). This architecture is called service function chain (SFC), which can be hosted on top of commodity servers and switches located at the cloud. Meanwhile, software-defined networking (SDN) can be utilized to manage VNFs to handle traffic flows through SFC. One of the most critical issues that needs to be addressed in NFV is VNF placement that optimizes physical link bandwidth consumption. Moreover, deploying SFCs enables service providers to consider different goals, such as minimizing the overall cost and service response time. In this paper, a novel approach for the VNF placement problem for SFCs, called virtual network functions and their replica placement (VNFRP), is introduced. It tries to achieve load balancing over the core links while considering multiple resource constraints. Hence, the VNF placement problem is first formulated as an integer linear programming (ILP) optimization problem, aiming to minimize link bandwidth consumption, energy consumption, and SFC placement cost. Then, a heuristic algorithm is proposed to find a near-optimal solution for this optimization problem. Simulation studies are conducted to evaluate the performance of the proposed approach. The simulation results show that VNFRP can significantly improve load balancing by 80% when the number of replicas is increased. Additionally, VNFRP provides more than a 54% reduction in network energy consumption. Furthermore, it can efficiently reduce the SFC placement cost by more than 67%. Moreover, with the advantages of a fast response time and rapid convergence, VNFRP can be considered as a scalable solution for large networking environments.


2021 ◽  
Author(s):  
Seyeon Jeong ◽  
Nguyen Van Tu ◽  
Jae-Hyoung Yoo ◽  
James Won-Ki Hong

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Dashmeet Anand, Hariharakumar Narasimhakumar, Et al.

Service Function Chaining (SFC) is a capability that links multiple network functions to deploy end-to-end network services. By virtualizing these network functions also known as Virtual Network Functions (VNFs), the dependency on traditional hardware can be removed, hence making it easier to deploy dynamic service chains over the cloud environment. Before implementing service chains over a large scale, it is necessary to understand the performance overhead created by each VNF owing to their varied characteristics. This research paper attempts to gain insights on the server and networking overhead encountered when a service chain is deployed on a cloud orchestration tool such as OpenStack. Specifically, this research will measure the CPU utilization, RAM usage and System Load of the server hosting OpenStack. Each VNF will be monitored for its varying performance parameters when subjected to different kinds of traffic. Our focus lies on acquiring performance parameters of the entire system for different service chains and compare throughput, latency, and VNF statistics of the virtual network. Insights obtained from this research can be used in the industry to achieve optimum performance of hardware and network resources while deploying service chains.


2018 ◽  
Vol 18 (03) ◽  
pp. e23 ◽  
Author(s):  
María José Basgall ◽  
Waldo Hasperué ◽  
Marcelo Naiouf ◽  
Alberto Fernández ◽  
Francisco Herrera

The volume of data in today's applications has meant a change in the way Machine Learning issues are addressed. Indeed, the Big Data scenario involves scalability constraints that can only be achieved through intelligent model design and the use of distributed technologies. In this context, solutions based on the Spark platform have established themselves as a de facto standard. In this contribution, we focus on a very important framework within Big Data Analytics, namely classification with imbalanced datasets. The main characteristic of this problem is that one of the classes is underrepresented, and therefore it is usually more complex to find a model that identifies it correctly. For this reason, it is common to apply preprocessing techniques such as oversampling to balance the distribution of examples in classes. In this work we present SMOTE-BD, a fully scalable preprocessing approach for imbalanced classification in Big Data. It is based on one of the most widespread preprocessing solutions for imbalanced classification, namely the SMOTE algorithm, which creates new synthetic instances according to the neighborhood of each example of the minority class. Our novel development is made to be independent of the number of partitions or processes created to achieve a higher degree of efficiency. Experiments conducted on different standard and Big Data datasets show the quality of the proposed design and implementation.


Author(s):  
Evangelos Markakis ◽  
Anargyros Sideris ◽  
George Alexiou ◽  
Athina Bourdena ◽  
Evangelos Pallis ◽  
...  

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