Notice of Violation of IEEE Publication Principles: Dynamic Embedding and Scheduling of Service Function Chains for Future SDN/NFV-Enabled Networks

Cao, Haotong; Zhu, Hongbo; Yang, Longxiang

doi:10.1109/access.2019.2906874

Cited by 26 publications

(8 citation statements)

References 17 publications

(25 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Few authors performed a joint optimization method for VNF mapping and VNF scheduling which are performed using the MILP model. In [8] achieving the optimal solution for a smaller instance is presented. However, for dense and complex networks, the authors proposed a heuristic model which provided a suboptimal solution with a reduced amount of computation time.…”

Section: Literature Reviewmentioning

confidence: 99%

Virtual Network Function Embedding under Nodal Outage using Reinforcement Learning

Chetty

Ahmadi

Nag

2020

2020 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)

View full text Add to dashboard Cite

With the emergence of various types of applications such as delay-sensitive applications, future communication networks are expected to be increasingly complex and dynamic. Network Function Virtualization (NFV) provides the necessary support towards efficient management of such complex networks, by disintegrating the dependency on the hardware devices via virtualizing the network functions and placing them on shared data centres. However, one of the main challenges of the NFV paradigm is the resource allocation problem which is known as NFV-Resource Allocation (NFV-RA). NFV-RA is a method of deploying software-based network functions on the substrate nodes, subject to the constraints imposed by the underlying infrastructure and the agreed Service Level Agreement (SLA). This work investigates the potential of Reinforcement Learning (RL) as a fast yet accurate means (as compared to integer linear programming) for deploying the softwarized network functions onto substrate networks under several Quality of Service (QoS) constraints. In addition to the regular resource constraints and latency constraints, we introduced the concept of a complete outage of certain nodes in the network. This outage can be either due to a disaster or unavailability of network topology information due to proprietary and ownership issues. We have analyzed the network performance on different network topologies, different capacities of the nodes and the links, and different degrees of the nodal outage. The computational time escalated with the increase in the network density to achieve the optimal solutions; this is because Q-Learning is an iterative process which results in a slow exploration. Our results also show that for certain topologies and a certain combination of resources, we can achieve between 70-90% service acceptance rate even with a 40% nodal outage.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Virtual Network Function Embedding under Nodal Outage using Reinforcement Learning

Chetty

Ahmadi

Nag

2020

2020 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)

View full text Add to dashboard Cite

show abstract

“…Moreover, a resource aware routing algorithm was presented to solve the model. In their study, Cao et al 63 studied the dynamic SFC embedding and scheduling problem and established a mixed ILP (MILP) model to minimize resource consumption cost. Meanwhile, a dynamic heuristic SFC embedding and scheduling algorithm was proposed.…”

Section: Sdn and Deep Learning For Multi‐domain Sfc Deploymentmentioning

confidence: 99%

The intelligent multi‐domain service function chain deployment: Architecture, challenges and solutions

Zhang

Wang

Dong

et al. 2020

Int J Communication

View full text Add to dashboard Cite

Network function virtualization (NFV) technology achieves flexible service deployment by replacing the middleboxes with virtual network functions (VNFs). In NFV, a set of VNFs are chained in a given order, called service function chain (SFC), and accordingly, data flow is steered to traverse all the VNFs in order to offer a service. With a large number of network devices and end users being connected into Internet, there is a growing demand for largescale multi-domain networks to dynamically deploy the SFC across multiple network domains, in order to support efficient service provisioning. To this end, in this paper, we first investigate the state of the art of multi-domain SFC deployment, and then propose an intelligent multi-domain SFC deployment (IMSD) architecture by leveraging software-defined networking (SDN), NFV, and deep learning technologies. Furthermore, we discuss the potential challenges to realize the IMSD and provide some promising solutions. K E Y W O R D S deep learning, multi-domain networks, SDN, SFC deployment 1 | INTRODUCTION Recently, a great variety of middleboxes have been designed to efficiently provide various network services. 1 However, the service provisioning method is not flexible and leads to large capital expenditure (CAPEX) and operational expenditure (OPEX) since the middleboxes are heavily dependent on specific hardware devices. Fortunately, the emergence of network function virtualization (NFV) technology provides a promising solution to address the issues. NFV decouples network functions from hardware devices and implements them in software, called virtual network functions (VNFs). 2,3 Unlike the middleboxes, the VNFs can be flexibly deployed in the commercial-off-the-shelf devices. NFV

show abstract

“…They also ensure that the storage infrastructure is following software-defined principles. A novel software-defined cooperative caching framework is presented in [59] which manage data placement concerns for multi-tier servers and storage applications in a coherent way. Similarly, a vision for service modularization based on real-world customer requirements is provided in [60] which describes the pros and cons of different classes of APIs from customers and cloud service provider perspective.…”

Section: Recent Developments In Sdcc Model-based Solutionsmentioning

confidence: 99%

Software-Defined Cloud Computing: A Systematic Review on Latest Trends and Developments

Abbasi

Shamshirband

et al. 2019

IEEE Access

View full text Add to dashboard Cite

Cloud computing concepts offer effective and efficient tools for addressing resource-hungry computational problems. While conventional methods, architectures, and processing techniques may limit cloud data center performance, software-defined cloud computing (SDCC) is an approach where virtualization services to all network resources in a dc are software-defined and where software-defined networking (SDN) and cloud computing go hand in hand. SDCC-related concepts change the previous state of affairs by promoting the centralized control of networking functions in a data center. A key objective of developing software-driven cloud infrastructure is that the networking hardware, software, storage, security, and network traffic management is open and interoperable. This facilitates easy installation and management of networking functions in the cloud infrastructure. Employing SDCC concepts to cloud data centers can improve resource administration challenges to a greater extent. This paper presents a survey on SDCC. We begin by introducing SDCC environments and explain its main architectural components. We identify the essential contributions of various developments to this field and discuss the implementation challenges and limitations faced in their adoption. We also explore the potential of SDCC in two domains, namely, resource orchestration and application development, as case studies of specific interest. In an attempt to anticipate the future evolution, we discuss the important research opportunities and challenges in this promising field.

show abstract

Notice of Violation of IEEE Publication Principles: Dynamic Embedding and Scheduling of Service Function Chains for Future SDN/NFV-Enabled Networks

Cited by 26 publications

References 17 publications

Virtual Network Function Embedding under Nodal Outage using Reinforcement Learning

Virtual Network Function Embedding under Nodal Outage using Reinforcement Learning

The intelligent multi‐domain service function chain deployment: Architecture, challenges and solutions

Software-Defined Cloud Computing: A Systematic Review on Latest Trends and Developments

Contact Info

Product

Resources

About