Online VNF Lifecycle Management in an MEC-Enabled 5G IoT Architecture

Sarrigiannis, Ioannis; Ramantas, Kostas; Kartsakli, Elli; Mekikis, Prodromos-Vasileios; Antonopoulos, Angelos; Verikoukis, Christos

doi:10.1109/jiot.2019.2944695

Cited by 98 publications

(56 citation statements)

References 27 publications

(23 reference statements)

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“…As our future work, we plan to study the IoT QoS management in the distributed SDN control plane and multi-domain network. Finally, "designing more advanced policies for estimating the IoT servers load," "providing IoT services as softwarization," and "developing IoT framework based on Network Functions Virtualization (NFV) for energy and QoS management" are among the most important future works of the paper [53]. Table 6 Comparison between SDN-based framework and ILP model Execution Time (ms) ILP 83 699 4543 13548 SDN-based 143 172 232 299 Objective Function ( (fpup)) ILP 97 223 345 498 SDN-based 96 220 340 491…”

Section: Discussionmentioning

confidence: 99%

Load-Balanced and QoS-Aware Software-Defined Internet of Things

Montazerolghaem

Yaghmaee

2020

IEEE Internet Things J.

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Internet of Things (IoT) offers a variety of solutions to control industrial environments. The new generation of IoT consists of millions of machines generating huge traffic volumes; this challenges the network in achieving the Quality of Service (QoS) and avoiding overload. Diverse classes of applications in IoT are subject to specific QoS treatments. In addition, traffic should be distributed among IoT servers based on their available capacity. In this paper, we propose a novel framework based on Software-defined Networking (SDN) to fulfill the QoS requirements of various IoT services and to balance traffic between IoT servers simultaneously. At first, the problem is formulated as an Integer Linear Programming (ILP) model that is NP-hard. Then, a predictive and proactive heuristic mechanism based on time-series analysis and fuzzy logic is proposed. Afterwards, the proposed framework is implemented in a real testbed, which consists of the Open vSwitch, Floodlight controller, and Kaa servers. To evaluate the performance, various experiments are conducted under different scenarios. The results indicate the improved IoT QoS parameters, including throughput and delay, and illustrate the non-occurrence of overload on IoT servers in heavy traffic. Furthermore, the results show improved performance compared to similar methods.

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Section: Discussionmentioning

confidence: 99%

Load-Balanced and QoS-Aware Software-Defined Internet of Things

Montazerolghaem

Yaghmaee

2020

IEEE Internet Things J.

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“…Xia [19] proposed a heuristic algorithm to get the feasible scheme in polynomial-time and improve the performance in terms of time, cost, and load, but they did not take degradation of service into account. Sarrigiannis [6] proposed a 5G IoT architecture based on the collaboration of edge computing and cloud computing, and designed an online VNF full life cycle algorithm. Eramo [20] considered revenue loss caused by QoS degradation in the migration process, but they neglected the change of weight.…”

Section: Sfc Migrationmentioning

confidence: 99%

“…When designing the mapping algorithm of SFC, many researchers aim at optimizing the cost of deployment, the reliability and end-to-end delay of service chain, the acceptance rate of service request, the rationality and effectiveness of resource allocation and other factors. Particularly, an IoT device and a Docker-based light-weight virtualization functional architecture has been proposed [2], and several SFC mapping algorithms are designed for IoT in [3][4][5][6]. Fu [3] proposed an SFC mapping scheme based on deep reinforcement learning.…”

Section: Introductionmentioning

confidence: 99%

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A Reliability-and-Energy-Balanced Service Function Chain Mapping and Migration Method for Internet of Things

Liao

et al. 2020

IEEE Access

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With the rapid development of Internet of Things (IoT) technology, Network Function Virtualization (NFV) is introduced in the edge network to provide flexible and personalized service. However, there still exist some problems to be solved, such as high cost, unbalanced load, and low availability. Therefore, a reliability-and-energy-balanced Service Function Chain (SFC) mapping and migration method is presented for IoT applications. First, aiming at improving network performance and reducing expenditure, an SFC mapping algorithm based on cost optimization, load balancing, and reliability is proposed to map SFC requests onto the network and provide backup. Second, aiming at optimizing resource configuration, an SFC migration method based on energy consumption and quality of service is proposed to integrate network resources. Simulation results show that the proposed method outperforms the compared algorithms by 15.5% and 24.55% in the acceptance ratio of SFC requests and the overall costs, respectively.

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“…In their works, the backhaul bandwidth and the edge server resource allocation are jointly optimized to minimize the total task serving time. In [38], the authors presented a NFV-enabled architecture and proposed orchestration algorithms for VNF onboarding and scheduling.…”

Section: Joint Computation Offloading and Resource Allocationmentioning

confidence: 99%

Collaborative Computation Offloading and Resource Allocation in Cache-Aided Hierarchical Edge-Cloud Systems

Lan

Wang

et al. 2019

Electronics

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The hierarchical edge-cloud enabled paradigm has recently been proposed to provide abundant resources for 5G wireless networks. However, the computation and communication capabilities are heterogeneous which makes the potential advantages difficult to be fully explored. Besides, previous works on mobile edge computing (MEC) focused on server caching and offloading, ignoring the computational and caching gains brought by the proximity of user equipments (UEs). In this paper, we investigate the computation offloading in a three-tier cache-assisted hierarchical edge-cloud system. In this system, UEs cache tasks and can offload their workloads to edge servers or adjoining UEs by device-to-device (D2D) for collaborative processing. A cost minimization problem is proposed by the tradeoff between service delay and energy consumption. In this problem, the offloading decision, the computational resources and the offloading ratio are jointly optimized in each offloading mode. Then, we formulate this problem as a mixed-integer nonlinear optimization problem (MINLP) which is non-convex. To solve it, we propose a joint computation offloading and resource allocation optimization (JORA) scheme. Primarily, in this scheme, we decompose the original problem into three independent subproblems and analyze their convexity. After that, we transform them into solvable forms (e.g., convex optimization problem or linear optimization problem). Then, an iteration-based algorithm with the Lagrange multiplier method and a distributed joint optimization algorithm with the adoption of game theory are proposed to solve these problems. Finally, the simulation results show the performance of our proposed scheme compared with other existing benchmark schemes. and transmission techniques applied in the conventional cellular networks may not be efficient to meet UEs' requirements of high throughput and adequate computational power. To improve the delay performance and operational costs of services in fifth-generation (5G) wireless networks, future communication networks not only need to support seamless wireless access but also to offer the provisioning of computational offloading for UEs [3,4].To meet with such a challenge of limited computational capability of UEs, a typical paradigm of mobile edge computing (MEC) is proposed which combines wireless network service and cloud computing at the edge of the small cell networks (SCNs) [5]. In an MEC system, a huge number of heterogeneous ubiquitous and decentralized devices are enabled to communicate, potentially cooperate and perform computation offloading by uploading their computational tasks to the MEC server via access ratio networks [6,7]. UEs no longer need to offload all of their tasks (e.g., high-quality video streaming, mobile gaming, etc.) to the central and remote cloud. Thus their requirement can be satisfied at any time and anywhere. However, the limited computational capabilities of edge servers may not be sufficient when there exists competition for resources by a large number of device...

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Online VNF Lifecycle Management in an MEC-Enabled 5G IoT Architecture

Cited by 98 publications

References 27 publications

Load-Balanced and QoS-Aware Software-Defined Internet of Things

Load-Balanced and QoS-Aware Software-Defined Internet of Things

A Reliability-and-Energy-Balanced Service Function Chain Mapping and Migration Method for Internet of Things

Collaborative Computation Offloading and Resource Allocation in Cache-Aided Hierarchical Edge-Cloud Systems

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