Embedding Dependence-Aware Service Function Chains

Jalalitabar, Maryam; Guler, Evrim; Zheng, Danyang; Luo, Guangchun; Tian, Ling; Cao, Xiaojun

doi:10.1364/jocn.10.000c64

Cited by 26 publications

(8 citation statements)

References 12 publications

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“…where p a represents the percentage of execution time for action a. Next, we introduce Markov approximation technique to approximate the formula in (16). First, a differentiable function is defined.…”

Section: A Log-sum-exp Approximation Methodsmentioning

confidence: 99%

“…In [12], authors formulate a mixed-integer Linear Programming model to efficiently place VNF instances and schedule service flows in distributed server clusters or data centers, and then derive a relaxation-based method to minimize both placement and communication cost with rational computational complexity. Additionally, authors in [13]- [16] also present some methods to guarantee the QoE and resource utilization in SC mapping. However, above methods ignore the effect of multiple VNF instances, which limits the effective improvement of cost efficiency.…”

Section: Related Workmentioning

confidence: 99%

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Markov Approximation Method for Optimal Service Orchestration in IoT Network

Guo

Liang

et al. 2019

IEEE Access

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With the evolution toward 5G, the Internet of Things (IoT) is expected to manage resource dynamically and provide customized services for different users in a cost-effective manner. Many technologies, such as edge computing and network function virtualization (NFV), have responded to these demands and been even more critical for system mobility, scalability, flexibility, and resource utilization. In this vein, the efficient provisioning method of the IoT services in distributed clouds based on the diverse quality of experience (QoE) requirements is highly needed. This paper focuses on service chain (SC) orchestration and studies the optimal placement of virtual network functions (VNFs) with multiple instances to minimize cost and delay, as well as guarantee network load balancing. We propose a multi-objective optimization problem model and then convert this combinatorial optimization problem to the one which can be solved by distributed methods based on the Markov approximation. At last, a VNF placement with multiple instances algorithm (VPMIA) based on Markov chain is designed to solve the above problem in a distributed manner. The simulation results show that the proposed algorithm can outperform the random placement algorithm and the single-path placement algorithm in cost saving by 22% and 31%, respectively, with a high SC acceptance rate. Besides, it can guarantee the QoE requirements and make the network load balanced with the different numbers of SCs.INDEX TERMS Distributed algorithm, Internet of Things, load balancing, Markov approximation, multiple instances, service chain, VNF placement.

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Section: A Log-sum-exp Approximation Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Markov Approximation Method for Optimal Service Orchestration in IoT Network

Guo

Liang

et al. 2019

IEEE Access

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“…The placement of VNFs with dependency and traffic volume scaling has been studied in several articles. Jalalitabar et al [22,35] proposed an efficient way to design users service requests while considering the computing resource, VNF dependency and service request. Ye et al [15] proposed a heuristic algorithm to solve the multiple SFCs design, optimize the network reconfigurations and protect the service steadiness.…”

Section: Related Workmentioning

confidence: 99%

SFC Design and VNF Placement Based on Traffic Volume Scaling and VNF Dependency in 5G Networks

Zeng¹,

Xia²,

Zhang³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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The development of Fifth-Generation (5G) mobile communication technology has remarkably promoted the spread of the Internet of Things (IoT) applications. As a promising paradigm for IoT, edge computing can process the amount of data generated by mobile intelligent devices in less time response. Network Function Virtualization (NFV) that decouples network functions from dedicated hardware is an important architecture to implement edge computing, deploying heterogeneous Virtual Network Functions (VNF) (such as computer vision, natural language processing, intelligent control, etc.) on the edge service nodes. With the NFV MANO (Management and Orchestration) framework, a Service Function Chain (SFC) that contains a set of ordered VNFs can be constructed and placed in the network to offer a customized network service. However, the procedure of NFV orchestration faces a technical challenge in minimizing the network cost of VNF placement due to the complexity of the changing effect of traffic volume and the dependency on the VNF relationship. To this end, we jointly optimize SFC design and VNF placement to minimize resource cost while taking account of VNF dependency and traffic volume scaling. First, the problem is formulated as an Integer Linear Programming (ILP) model and proved NPhard by reduction from Hamiltonian Cycle problem. Then we proposed an efficient heuristic algorithm called Traffic Aware and Interdependent VNF Placement (TAIVP) to solve the problem. Compared with the benchmark algorithms, emulation results show that our algorithm can reduce network cost by 10.2% and increase service request acceptance rate by 7.6% on average.

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“…• t proc (12) The resource cost for traversing a link consists of two components: estimated propagation delay D prop vw as in (10) and estimated queuing delay D queue vw as in (11). The resource cost for traversing a VM consists of the estimated processing delay D proc v,m as in (12).…”

Section: Definition Of the Resource Costmentioning

confidence: 99%

“…Authors in [9] defined a model for formalizing the chaining of network functions using a context-free language. In [10], a topological dependence sorting method was used to jointly optimize the processes of SFC designing and deployment. Secondly, a lot of researchers have focused on how to deploy the SFC optimally to achieve a good network performance.…”

Section: Introductionmentioning

confidence: 99%

Resource Aware Chaining and Adaptive Capacity Scaling for Service Function Chains in Distributed Cloud Network

et al. 2019

IEEE Access

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With the development of network technology such as software-defined network (SDN) and network function virtualization (NFV), Internet service providers (ISPs) are increasingly placing the virtual network function(VNF) instances at the network edge to provide network service. However, there are some issues to be tackled in the distributed SDN/NFV enabled cloud. Firstly, VNF instances require to be chained in predefined order to provide network services. It is a challenge to optimally select and chain VNF instances from the multi-instances. Moreover, due to the capacity limitation of the distributed edge nodes. The capacity of the Virtual Machines (VMs) that host VNFs should be proactively adjusted to cope with traffic demands. Since most existing works ignore the vertical capacity scaling problem in routing commodities with Service Function Chain (SFC) requests. In this paper, a fine-grained scheduling scheme at VM-level is proposed. Firstly, we formulate the SFC chaining problem as an Integer Linear Programming (ILP) model aiming to embed SFC requests with minimum estimated latency cost. Furthermore, we formulate the adaptive VNF resource allocation (VNF-AR) problem as a convex optimization. The theoretical optimal capacity for each VM can be derived from the Karush-Kuhn Tucker (KKT) conditions. At last, a novel joint optimization approach of VNF chaining and adaptive scaling (VNF-CAS) is proposed to efficiently embed the SFC requests. Performance evaluation shows that VNF-CAS can achieve better performance in SFC requests acceptance rate, average effective throughput, average load utilization and VM load balancing when it is compared with other algorithms in existing works. INDEX TERMS Network function virtualization, service function chain, distributed cloud network, resource optimization.

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Embedding Dependence-Aware Service Function Chains

Cited by 26 publications

References 12 publications

Markov Approximation Method for Optimal Service Orchestration in IoT Network

Markov Approximation Method for Optimal Service Orchestration in IoT Network

SFC Design and VNF Placement Based on Traffic Volume Scaling and VNF Dependency in 5G Networks

Resource Aware Chaining and Adaptive Capacity Scaling for Service Function Chains in Distributed Cloud Network

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