Efficient virtual network function placement strategies for Cloud Radio Access Networks

Bhamare, Deval; Erbad, Aiman; Jain, Raj; Zolanvari, Maede; Samaka, Mohammed

doi:10.1016/j.comcom.2018.05.004

Cited by 37 publications

(32 citation statements)

References 42 publications

(74 reference statements)

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“…Different form the work in [9], [43], we propose a general model to quantitatively calculate flow traversing delay of a SFC in edge clouds, and devise a randomized rounding algorithm to jointly solve the delay-aware VNF placement and traffic routing problem in edge clouds. Moreover, there are also some studies about VNF placement in virtual evolved packet core (4G/5G) networks [44], [45], Cloud-Radio Access Networks [46], etc.…”

Section: Delay-aware Vnf/rule Placement and Routing In Edge Clouds Anmentioning

confidence: 99%

Delay-Aware Virtual Network Function Placement and Routing in Edge Clouds

Yang,

Li,

Trajanovski

et al. 2021

IEEE Trans. on Mobile Comput.

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Mobile Edge Computing (MEC) offers a way to shorten the cloud servicing delay by building the small-scale cloud infrastructures at the network edge, which are in close proximity to the end users. Moreover, Network Function Virtualization (NFV) has been an emerging technology that transforms from traditional dedicated hardware implementations to software instances running in a virtualized environment. In NFV, the requested service is implemented by a sequence of Virtual Network Functions (VNF) that can run on generic servers by leveraging the virtualization technology. Service Function Chaining (SFC) is defined as a chain-ordered set of placed VNFs that handles the traffic of the delivery and control of a specific application. NFV therefore allows to allocate network resources in a more scalable and elastic manner, offer a more efficient and agile management and operation mechanism for network functions and hence can largely reduce the overall costs in MEC. In this paper, we study the problem of how to place VNFs on edge and public clouds and route the traffic among adjacent VNF pairs, such that the maximum link load ratio is minimized and each user's requested delay is satisfied. We consider this problem for both totally ordered SFCs and partially ordered SFCs. We prove that this problem is NP-hard, even for the special case when only one VNF is requested. We subsequently propose an efficient randomized rounding approximation algorithm to solve this problem. Extensive simulation results show that the proposed approximation algorithm can achieve close-to-optimal performance in terms of acceptance ratio and maximum link load ratio.

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Section: Delay-aware Vnf/rule Placement and Routing In Edge Clouds Anmentioning

confidence: 99%

Delay-Aware Virtual Network Function Placement and Routing in Edge Clouds

Yang,

Li,

Trajanovski

et al. 2021

IEEE Trans. on Mobile Comput.

View full text Add to dashboard Cite

show abstract

“…So far, most of the literatures, such as literature [20], have studied the issues related to the migration of virtual machines under a certain migration trigger time, but such a migration strategy does not consider multiple SFC business scenarios at the same time. Literature [21] studies the VNF migration problem under the one-to-one mapping relationship between service functional chain VNF and node VNF instances. In the shared state of VNF instances, if the performance of a slice fails to meet user requirements, the current research cannot formulate an effective strategy to achieve VNF migration.…”

Section: Related Workmentioning

confidence: 99%

Energy Efficiency Opposition‐Based Learning and Brain Storm Optimization for VNF‐SC Deployment in IoT

Xuan

Zhao

Liu

et al. 2021

Wireless Communications and Mobile Computing

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Network Function Virtualization (NFV) can provide the resource according to the request and can improve the flexibility of the network. It has become the key technology of the Internet of Things (IoT). Resource scheduling for the virtual network function service chain (VNF-SC) is the key issue of the NFV. Energy consumption is an important indicator for the IoT; we take the energy consumption into the objective and define a novel objective to satisfying different objectives of the decision-maker. Due to the complexity of VNF-SC deployment problem, through taking into consideration of the heterogeneity of nodes (each node only can provide some specific VNFs), and the limitation of resources in each node, a novel optimal model is constructed to define the problem of VNF-SC deployment problem. To solve the optimization model effectively, a weighted center opposition-based learning is introduced to brainstorm optimization to find the optimal solution (OBLBSO). To show the efficiency of the proposed algorithm, numerous of simulation experiments have been conducted. Experimental results indicate that OBLBSO can improve the accuracy of the solution than compared algorithm.

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“…Considering cloud users always assign their VMs with specific PM (physical machine) requirements [30], an efficient solution for VM placement is proposed. Under the cloud deployment cost limitation, an enhanced BnB (branch-and-bound) heuristic approach for virtual function placement is proposed [31]. In [32], the VM placement is defined as a multi-objective optimization problem and a multi-objective memetic algorithm is proposed to solve it.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Service Function Chain Scheduling in Mobile Edge Computing via Deep Reinforcement Learning

Wang

et al. 2020

IEEE Access

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MEC (Mobile Edge Computing) provides both IT service environment and cloud computation on the edge of the network. This technology not only minimizes the end-to-end latency but also increases the efficiency of computing. Some latency-sensitive applications, such as cloud video, online game, and augmented reality, take advantage of the MEC system to provide fast and stable services. Several new network techniques, including the implementation of NFV (Network Function Virtualization), the placement of VNF (Virtual Network Function) and the scheduling of SFC (Service Function Chain), should be considered to be applied in the MEC system. In this paper, we focus on the research about the scheduling of SFC in the NFV enabled MEC system and propose a solution accordingly. First, we make reasonable assumptions on the settings of MEC systems and model the SFC scheduling problem into a flexible job-shop scheduling problem. Since minimizing the latency can significantly improve the quality of service (QoS) and increase the revenue of Internet Service Providers, our optimization goal is to minimize the overall scheduling latency. To solve this optimization problem, a deep reinforcement learning based algorithm DQS is proposed. DQS can detect the variation of the MEC system's environment and perform adaptive scheduling for SFC requests. As the results of the simulation indicate, DQS works better than the other off-the-shelf algorithms in two key indexes: overall scheduling latency and average resource usage. Moreover, DQS can shorten the decision time and schedule SFCs stably with high performance. It is suitable to be extended to an online scheduling algorithm.

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Efficient virtual network function placement strategies for Cloud Radio Access Networks

Cited by 37 publications

References 42 publications

Delay-Aware Virtual Network Function Placement and Routing in Edge Clouds

Delay-Aware Virtual Network Function Placement and Routing in Edge Clouds

Energy Efficiency Opposition‐Based Learning and Brain Storm Optimization for VNF‐SC Deployment in IoT

Adaptive Service Function Chain Scheduling in Mobile Edge Computing via Deep Reinforcement Learning

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