Network Congestion-aware Online Service Function Chain Placement and Load Balancing

Network function virtualization (NFV) is a key technology to decouple hardware device and software function. Several virtual network functions (VNFs) combine into a function sequence in a certain order, that is defined as service function chain (SFC). A significant challenge is guaranteeing reliability. First, deployment server is selected to place VNF, then, backup server is determined to place the VNF as a backup which is running when deployment server is failed. Moreover, how to determine the accurate locations dynamically with machine learning is challenging. This paper focuses on resource requirements of SFC to measure its priority meanwhile calculates node priority by current resource capacity and node degree, then, a novel priority-awareness deep reinforcement learning (PA-DRL) algorithm is proposed to implement reliable SFC dynamically. PA-DRL determines the backup scheme of each VNF, then, the model jointly utilizes delay, load balancing of network as feedback factors to optimize the quality of service. In the experimental results, resource efficient utilization, survival rate, and load balancing of PA-DRL were improved by 36.7%, 35.1%, and 78.9% on average compared with benchmark algorithm respectively, average delay was reduced by 14.9%. Therefore, PA-DRL can effectively improve reliability and optimization targets compared with other benchmark methods.

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In-network Congestion-aware Load Balancing at Transport Layer

2019 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) ◽

10.1109/nfv-sdn47374.2019.9040109 ◽

2019 ◽

Author(s):

Ashkan Aghdai ◽

Michael I.-C. Wang ◽

Yang Xu ◽

Charles H.-P. Wenz ◽

H. Jonathan Chao

Keyword(s):

Load Balancing ◽

Transport Layer ◽

Network Congestion ◽

Congestion Aware

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Efficient Placement of Service Function Chains in Cloud Computing Environments

Electronics ◽

10.3390/electronics10030323 ◽

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.

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