A Reliability-Aware Network Service Chain Provisioning With Delay Guarantees in NFV-Enabled Enterprise Datacenter Networks

Qu, Long; Assi, Chadi; Shaban, Khaled; Khabbaz, Maurice

doi:10.1109/tnsm.2017.2723090

Cited by 160 publications

(94 citation statements)

References 17 publications

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“…Reducing the end-to-end delay of deployment paths can improve network performance and user experience. Qu et al [27] established a reliability-aware and delay-constrained (READ) routing optimization framework for NFV-enabled datacenter networks. First, a mixed integer linear program model was proposed to reduce the end-to-end delay.…”

Section: B Sfc Deployment For Optimizing the End-to-end Delaymentioning

confidence: 99%

Low-Latency and Resource-Efficient Service Function Chaining Orchestration in Network Function Virtualization

Sun

Zhu

et al. 2020

IEEE Internet Things J.

118

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Recently, network function virtualization (NFV) has been proposed to solve the dilemma faced by traditional networks and to improve network performance through hardware and software decoupling. The deployment of the service function chain (SFC) is a key technology that affects the performance of virtual network function (VNF). The key issue in the deployment of SFCs is proposing effective algorithms to achieve efficient use of resources. In this paper, we propose a service function chain deployment optimization (SFCDO) algorithm based on a breadth-first search (BFS). The algorithm first uses a BFS based algorithm to find the shortest path between the source node and the destination node. Then, based on the shortest path, the path with the fewest hops is preferentially chosen to implement the SFC deployment. Finally, we compare the performances with the greedy and simulated annealing (G-SA) algorithm. The experiment results show that the proposed algorithm is optimized in terms of end-to-end delay and bandwidth resource consumption. In addition, we also consider the load rate of the nodes to achieve network load balancing.

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Section: B Sfc Deployment For Optimizing the End-to-end Delaymentioning

confidence: 99%

Low-Latency and Resource-Efficient Service Function Chaining Orchestration in Network Function Virtualization

Sun

Zhu

et al. 2020

IEEE Internet Things J.

118

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“…Carpio and Jukan [7] investigated how to improve service reliability through the joint consideration of replications and migrations. Qu et al [29] aimed to minimize the communication bandwidth usage across the network while considering the availability requirements, by developing a heuristic. Aidi et al [1] recently proposed a framework to efficiently manage survivability of service function chains and the backup VNFs.…”

Section: Related Workmentioning

confidence: 99%

Reliability-Aware Virtualized Network Function Services Provisioning in Mobile Edge Computing

Huang

Liang

Shen

et al. 2020

IEEE Trans. on Mobile Comput.

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Along with Network Function Virtualization (NFV), Mobile Edge Computing (MEC) is becoming a new computing paradigm that enables accommodating innovative applications and services with stringent response delay and resource requirements, including autonomous vehicles and augmented reality. Provisioning reliable network services for users is the top priority of most network service providers, as unreliable services or severe service failures can result in tremendous losses of users, particularly for their mission-critical applications. In this paper, we study reliability-aware VNF instances provisioning in an MEC, where different users request different network services with different reliability requirements through paying their requested services with the aim to maximize the network throughput. To this end, we first formulate a novel reliability-aware VNF instance placement problem by provisioning primary and secondary VNF instances at different cloudlets in MEC for each user while meeting the specified reliability requirement of the user request. We then show that the problem is NP-hard and formulate an Integer Linear Programming (ILP) solution. Due to the NP-hardness of the problem, we instead devise an approximation algorithm with a logarithmic approximation ratio for the problem. Moreover, we also consider two special cases of the problem. For one special case where each request only requests one primary and one secondary VNF instances, the problem is still NP-hard, and we devise a constant approximation algorithm for it. For another special case where different VNFs have the same amounts of computing resource demands, we show that it is polynomial-time solvable by developing a dynamic programming solution for it. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising, and the empirical results of the algorithms outperform their analytical counterparts as theoretical estimations usually are very conservative.

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“…For example, for an industrial control NSI, the latency threshold will be lower as the service is latency-sensitive, whereas a higher one is set for serving latency-tolerant applications. The amount of required VNUs can not exceed the available substrate resources, so we indicate the corresponding VNU capacity that the substrate processing link (u, v) accommodates in (10). Constraint (11) ensures that the resource capacity of substrate link (u, v) can afford the forwarded traffic f s of slice s to deploy and constraint (12) ensures flow conservation.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Meanwhile, both Huawei and Vodafone have pointed out that low latency is also a key metric to improve the user experience of mobile wide-band services, e.g., VR of 5G eMBB. On the other hand, for instance, E2E latency reduction may promote reliability as well, as the service's traffic can be steered through additional redundant VNFs [10]. Furthermore, the network state and requested traffic are time-varying, whose future information is hard to learn in advance.…”

Section: Introductionmentioning

confidence: 99%

Latency-Optimal Virtual Network Functions Resource Allocation for 5G Backhaul Transport Network Slicing

Feng

et al. 2019

Applied Sciences

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The concept of network slicing (NS) has been proposed for flexible resource provisioning where a physical resource is partitioned into logically independent networks on demand. The NS resource allocation implies the definition of a feasible path in the infrastructure network with adequate resource availability. However, due to complex structural characteristics of the backhaul transport network, a number of issues arise when fast deploying the end-to-end (E2E) slices onto network infrastructures. In this paper, a pair-decision resource allocation model is firstly formulated to construct the mapping relationship between logical networks and substrate networks in a coordinated way. In order to improve extreme quality of service (QoS) and user experiment, latency-optimal virtual resource allocation problem is defined, subject to the backhaul capacity and bandwidth constraints. The problem is formulated as an integer linear programming (ILP) and solved with the branch-and-bound scheme, whose resolution yields an optimal virtual network function (VNF) placement and traffic routing policy. Numerical results reveal that the proposed scheme can enable the transport network latency optimization with a reduction of up to 30% and 41.6% compared to the Network Slice Design Problem (NSDP) and Random Fit Placement Algorithm (RFPA) schemes respectively. In the meanwhile, the network load balance and serviceability have been improved efficiently with better resource utilization as well.

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A Reliability-Aware Network Service Chain Provisioning With Delay Guarantees in NFV-Enabled Enterprise Datacenter Networks

Cited by 160 publications

References 17 publications

Low-Latency and Resource-Efficient Service Function Chaining Orchestration in Network Function Virtualization

Low-Latency and Resource-Efficient Service Function Chaining Orchestration in Network Function Virtualization

Reliability-Aware Virtualized Network Function Services Provisioning in Mobile Edge Computing

Latency-Optimal Virtual Network Functions Resource Allocation for 5G Backhaul Transport Network Slicing

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