EASM: Efficiency-aware switch migration for balancing controller loads in software-defined networking

Hu, Tao; Lan, Julong; Zhang, Jianhui; Zhao, Wei

doi:10.1007/s12083-018-0632-6

Cited by 57 publications

(24 citation statements)

References 17 publications

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“…In other words, it is a challenge to effectively solve the switch redistribution problem in a valid time. Tao Hu et al [16] defined the migration efficiency based on the changes in controller load and balance caused by the redistribution switch, and they proposed efficiency-aware switch migration (EASM) algorithm to achieve load balancing at a minimum cost. The redistribution cost of switches is inevitable, and the message exchange cost cannot be ignored in this process of redistribution [17].…”

Section: Related Workmentioning

confidence: 99%

“…(16), z Ã ¼ ðz Ã 1 ; z Ã 2 Þ is the reference point for the objective function value, and z Ã i ¼ min f i ðxÞ f g. There is a weight vector for any individual on Pareto Front to make the optimal solution of the Eq. (16), and the solution is also the Pareto optimal solution of the original problem [30].…”

Section: Moea/d Algorithmmentioning

confidence: 99%

“…Then MECS6 will send PACKET_IN to the SDN controller to ask for route calculation. In the relevant literatures[16,21,29], PACKET_IN is the main component of the SDN controller load. So the load of the controller considered in this paper is all PACKET_IN sent by the MECS it serves.In this paper, we denote a SDN-enabled MEC network as G ¼ ðV; EÞ, which consists of N MEC servers S ¼ s…”

mentioning

confidence: 99%

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A MECS redistribution algorithm for SDN-enable MEC using response time and transmission overhead

Fan

et al. 2021

Wireless Netw

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The software-defined networks-enable mobile edge computing (SDN-enable MEC) architecture, which integrates SDN and MEC technologies, realizes the flexibility and dynamic management of the underlying network resources by the MEC, reduces the distance between the access terminal and computing resources and network resources, and increases the terminal's access to resources. However, the static distribution relationship between MEC servers (MECSs) and controllers in the multi-controller architecture may result in unbalanced load distribution among the controllers, which would degrade network performance. In this paper, a multi-objective optimization MECS redistribution algorithm (MOSRA) is proposed to decrease the response time and overhead. A controller response time model and link transmit overhead model are introduced as basis of an evolutionary algorithm which is proposed to optimize MECS redistribution. The proposed algorithm aims to select an available sub-optimizes individual by using a strategy based coordination transformation from Pareto Front. That is, when the master controller of the MECS is redistributed, both of the network overhead of the MECS to the controller and the response time of the controller to the MECS processing request are optimized. Finally, the simulation results demonstrate that the MOSRA can solve the redistribution problem in different network load levels and different network sizes within the effective time, and has a lower control plane response time, while making the edge network plane transmission overhead lower, compared with other algorithms .

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Section: Related Workmentioning

confidence: 99%

Section: Moea/d Algorithmmentioning

confidence: 99%

mentioning

confidence: 99%

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A MECS redistribution algorithm for SDN-enable MEC using response time and transmission overhead

Fan

et al. 2021

Wireless Netw

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“…The load increases due to the dynamic arrival of the packets at the data plane. SDN is used for load balancing using efficiency-aware switch migration (EASM) [33]. The sole purpose of EASM is to provide fairness in load balancing.…”

Section: B Load Balancing In Sdnmentioning

confidence: 99%

Towards QoS-Aware Load Balancing for High Density Software Defined Wi-Fi Networks

et al. 2020

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High density Wi-Fi networks require load balancing in order to ensure quality of service (QoS). In the traditional Wi-Fi networks, the wireless stations learn the access points (APs) load and make the association decisions themselves leading to uneven load distribution among the APs. The new paradigm, software defined networking (SDN), has a centralized architecture, which allows to manage, measure and control high density Wi-Fi networks easily. In this paper we propose a QoS-aware load balancing strategy (QALB) for software defined Wi-Fi networks (SD-Wi-Fi), as a solution to address the problem of Wi-Fi congestion among the OpenFlow enabled APs (OAPs). The SDN controller selects a load level up to which the association decisions are made by the OAPs without consulting the controller. The wireless stations from an overloaded OAP are handed to an underloaded OAP by considering multi-metrics such as the packet loss rate, received signal strength indicator (RSSI) and throughput. An emulation platform and a large-scalelow-cost testbed with the same settings are constructed to evaluate the performance of our load balancing strategy. The results show that in comparison to four non-static schemes such as, channel measurement based access selection scheme (CMAS), (DL-SINR) downlink-signal to interference plus noise ratio AP selection scheme (DASA), mean probe delay scheme (MPD) and RSSI scheme, the proposed QALB, optimizes the throughput up to 16%, reduces the average frame delay up to 19%, minimizes the number of re-transmissions by 49%, reduces the number of handoffs by 15%, improves the degree of load balancing by 22% and minimizes the re-association times by 38%, in high density SD-Wi-Fi. INDEX TERMS Emulation, high density, load balancing, QoS, Wi-Fi, SDN, testbed.

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“…A distributed multi-controller environment-based switch migration is proposed in [32]. The authors proposed the strategy called efficiency aware switch migration (EASM) for distributed controllers load balancing.…”

Section: Offloading Schemesmentioning

confidence: 99%

AI-aided Traffic Differentiated QoS Routing and Dynamic Offloading in Distributed Fragmentation Optimized SDN-IoT

Begovic¹,

Čaušević²,

Memić³

et al. 2020

International Journal of Engineering Research and Technology

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Internet of Things (IoT) becomes an emerging network technology that expedites billions of devices to be connected via the Internet to provide real-time intelligent application services. The benefits of Software-Defined Networking (SDN) can be used to fulfill IoT requirements. Quality of Service provisioning is an ongoing demand in software-defined IoT (SD-IoT), particularly for large scale environments. In this paper, we address this issue by proposing a seamless model of AI-aided Traffic Differentiated QoS Routing and Dynamic Offloading in distributed fragmentation optimized SDN-IoT. Firstly, we propose a Multi-Criterion based Deep Packet Inspection method for classifying the network traffic, which is held in Edge Routers (access points). Secondly, we construct a Partially Connected Network Topology using the ISOMAP algorithm for an effective rule placement and routing. We propose a Traffic Differentiated QoS Routing for forwarding data packets via the most suitable switches. We select the optimum route by Deep Alternative Neural Network (DANN). Based on the relationships among switches, the path is selected and flow rules are deployed. The poor QoS is often caused by load imbalance in controllers and switches. To overwhelm this issue, we propose a Dynamic Offloading scheme in SD-IoT. We offload the data packets from the overloaded controller to the underloaded controller using Hassanat Distance-based Knearest neighbors (HDK-NN) algorithm. Similarly, we propose a Ranking-based Entropy function (R-Ef) to allow dynamic offloading among switches. Simulation is performed using the NS3.26 simulator and the results proved that our proposed AIaided SD-IoT model provides superior QoS performance compared to previous approaches.

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EASM: Efficiency-aware switch migration for balancing controller loads in software-defined networking

Cited by 57 publications

References 17 publications

A MECS redistribution algorithm for SDN-enable MEC using response time and transmission overhead

A MECS redistribution algorithm for SDN-enable MEC using response time and transmission overhead

Towards QoS-Aware Load Balancing for High Density Software Defined Wi-Fi Networks

AI-aided Traffic Differentiated QoS Routing and Dynamic Offloading in Distributed Fragmentation Optimized SDN-IoT

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