Multi-resource Load Balancing for Virtual Network Functions

2017 ◽  
Author(s):  
Tao Wang ◽  
Hong Xu ◽  
Fangming Liu
Keyword(s):  
Load Balancing ◽  
Virtual Network ◽  
Network Functions

scholarly journals Efficient Placement of Service Function Chains in Cloud Computing Environments

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 323
Author(s):  
Marwa A. Abdelaal ◽  
Gamal A. Ebrahim ◽  
Wagdy R. Anis
Keyword(s):  
Energy Consumption ◽  
Load Balancing ◽  
Response Time ◽  
Optimization Problem ◽  
Service Providers ◽  
Virtual Network ◽  
Placement Problem ◽  
Service Function ◽  
Network Functions ◽  
Bandwidth Consumption

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.

Proactive Live Migration for Virtual Network Functions using Machine Learning

2021 ◽  
Author(s):  
Seyeon Jeong ◽  
Nguyen Van Tu ◽  
Jae-Hyoung Yoo ◽  
James Won-Ki Hong
Keyword(s):  
Machine Learning ◽  
Virtual Network ◽  
Live Migration ◽  
Network Functions

scholarly journals SDN/NFV VNF Service Chaining

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Dashmeet Anand, Hariharakumar Narasimhakumar, Et al.
Keyword(s):  
Large Scale ◽  
Virtual Network ◽  
Network Services ◽  
Entire System ◽  
Performance Parameters ◽  
Cloud Environment ◽  
Network Resources ◽  
Service Chain ◽  
Network Functions ◽  
Multiple Network

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.

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