Energy-Efficient Provisioning for Service Function Chains to Support Delay-Sensitive Applications in Network Function Virtualization

Gang Sun

doi:10.1109/JIOT.2020.2970995

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Energy-Efficient Provisioning for Service Function Chains to Support Delay-Sensitive Applications in Network Function Virtualization

2020-07-01

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Athanasios V. Vasilakos

DOI：10.1109/JIOT.2020.2970995

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Abstract

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The efficient deployment of virtual network functions (VNFs) for network service provisioning is key for achieving network function virtualization (NFV); however, most existing studies address only offline or one-off deployments of service function chains (SFCs) while neglecting the dynamic (i.e., online) deployment and expansion requirements. In particular, many methods of energy/resource cost reduction are achieved by merging VNFs. However, the energy waste and device wear for large-scale collections of servers (e.g., cloud networks and data centers) caused by sporadic request updating are ignored. To solve these problems, we propose an energy-aware routing and adaptive delayed shutdown (EAR-ADS) algorithm for dynamic SFC deployment, which includes the following features: 1) energy-aware routing (EAR): by considering a practical deployment environment, a flexible solution is developed based on reusing open servers and selecting paths with the aims of balancing energy and resources and minimizing the total cost and 2) adaptive delayed shutdown (ADS): the delayed shutdown time of the servers can be flexibly adjusted in accordance with the usage of each device in each time slot, thus eliminating the no-load wait time of the servers and frequent on/off switching. Therefore, the EAR-ADS can achieve dual-energy savings by both decreasing the number of open servers and reducing the idle/switching energy consumption of these servers. The simulation results show that EAR-ADS not only minimizes the cost of energy and resources but also achieves an excellent success rate and stability. Moreover, EAR-ADS is efficient compared with an improved Markov algorithm (SAMA), reducing the average deployment time by more than a factor of 40.

Keywords:

Servers

Energy consumption

Internet of Things

Hardware

Network function virtualization

Routing

Heuristic algorithms

Dynamic deployment

energy efficient

network function virtualization (NFV)

service function chain (SFC)

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