QoS Aware load balance in software defined LTE networks

Rangisetti, Anil Kumar; Pasca, S Thomas Valerrian; Tamma, Bheemarjuna Reddy

doi:10.1016/j.comcom.2016.09.005

Cited by 40 publications

(22 citation statements)

References 11 publications

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“…Nowadays, mobile operators see network offloading as the key towards relieving congestion in 4G/5G networks. In such a way, several research works have addressed the problem of load balancing in mobile networks during the last years . Nonetheless, deriving a dynamic optimal load balancing proposal that combines delay and energy awareness is challenging.…”

Section: Related Workmentioning

confidence: 99%

“…The relevance of load balancing in emerging networks is clear; even in the cutting‐edge framework of network function virtualization, some ideas are proposed to provide traffic offloading in 5G environments . Among the most recent studies, a QoS‐aware load balancing algorithm is proposed in Rangisetti and Tamma for software‐defined 4G environment. That heuristics uses loads of the cells, QoS profiles of users and their expected cell throughputs so as to make load balancing decisions.…”

Section: Related Workmentioning

confidence: 99%

“…[8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]25,26 Nonetheless, deriving a dynamic optimal load balancing proposal that combines delay and energy awareness is challenging. The relevance of load balancing in emerging networks is clear [8][9][10][11] ; even in the cutting-edge framework of network function virtualization, some ideas are proposed to provide traffic offloading in 5G environments. 8 Among the most recent studies, a QoS-aware load balancing algorithm is proposed in Rangisetti and Tamma 9 for software-defined 4G environment.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Delay‐ and energy‐aware load balancing in ultra‐dense heterogeneous 5G networks

Taboada

Aalto

Lassila

et al. 2017

Trans. Emerging Tel. Tech.

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In the context of ultra‐dense heterogeneous networks in 5G, we focus on the load balancing problem of elastic traffic in a single macrocell that is supported by a number of small cells. Each small cell is modeled by a single‐class processor sharing queue, whereas the macrocell is modelled by a multiclass processor sharing queue. We assume that the macrocell is always consuming energy, while a small cell can be switched off when it is idle with the setup delay penalty. As the main contribution, aimed at minimizing the weighted sum of the mean delay and the mean power consumption in the system, we develop static and dynamic load balancing policies that take into account the setup delay. All the obtained results are analytically founded. The performance of the static policy is evaluated using the achieved analytical expressions, which include the static optimal probabilities achieved before, whereas for the dynamic policy we need to perform simulations using previously obtained numerical results from the first policy iteration procedure. As concluded, numerical results indicate that the dynamic policy outperforms the static policy under realistic settings.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Delay‐ and energy‐aware load balancing in ultra‐dense heterogeneous 5G networks

Taboada

Aalto

Lassila

et al. 2017

Trans. Emerging Tel. Tech.

View full text Add to dashboard Cite

show abstract

“…3) and, their optimization may present two conflicting objectives [17]. LB Algorithms, which are based on QoS constraints [4][5][6], optimize the SE as the throughput requirement (Equation 6) relies on an efficient use of bandwidth. As far as that goes, the solvers taking into account the SINR [8], walk in the same way because a low level of ICI means a good rate (Equation 5).…”

Section: Spectrum Time and Energy Modelmentioning

confidence: 99%

“…As soon as the standard LB scheme has been published, it has been demonstrated that its original formulation could be optimized in terms of SE and EE. Therefore, papers [4][5][6] approach the phenomenon with the same principle by considering the user quality of service (QoS) constraints. Differing from this approach, authors in [7] introduce an EE scaling factor as a criterion for target cell selection in LB procedure.…”

Section: Introductionmentioning

confidence: 99%

Theory of Algorithm Suitability on Managing Radio Resources in Next Generation Mobile Networks

Fall

Zytoune

Yahyai

2018

JCOMSS

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Abstract-Beyond 2020, wireless networking model will be radically changed and oriented to business-driven concept as foreseen by the next generation mobile network (NGMN) alliance. As the available spectrum granted to a given operator is physically limited, new radio resource management techniques are required to ensure massive connectivity for wireless devices. Given this situation, in this paper we investigate how the key network functionalities as self-optimizing network (SON) must be thought to meet NGMN requirements. We propose therefore, algorithm suitability theory (AST) combined with the notion of network operator infrastructure convergence. The approach is based on software-defined networking (SDN) principle that allows an adaptability of the load balance algorithm to the dynamic network status. Besides, we use the concept of network function virtualization (NFV) that alleviates the constraint of confining the wireless devices to their home network operator only. Relying on these two technologies, we build AST through a lexicographic optimality criterion based on SPC (Status, Performance, and Complexity) order. Numerical results demonstrate a better network coverage verified by the improvement of metrics such as call blocking rate, spectrum efficiency, energy efficiency and load balance index.

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Nature‐inspired meta‐heuristic algorithms for solving the load balancing problem in the software‐defined network

Neghabi

Navimipour

Hosseinzadeh

et al. 2019

Int J Communication

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The growth of the networks has difficult network management. Recently, a concept called software-defined network (SDN) has been proposed to address this issue, which makes network management more adaptable. Control and forwarding planes are separated in SDN. The control plane is a centralized logical controller that controls the network. The forwarding plane that consists of transfer devices is responsible for transmitting packets. Because the network resources are limited, optimizing the use of resources in the networks is an important issue. Load balancing improves the balanced distribution of loads across multiple resources in order to maximize the reliability and network resources efficiency. SDN controllers can create an optimal load balancing compared to traditional networks because they have a network global view. The load-balancing problem can be solved using many different natureinspired meta-heuristic techniques because it has the NP-complete nature.Hence, for solving load balancing problem in SDN, nature-inspired metaheuristic techniques are important methods. However, to the best of our knowledge, there is not a survey or systematic review on studying these matters. Accordingly, in the area of the load balancing in the SDN, this paper reviews systematically the nature-inspired meta-heuristic techniques. Also, this study demonstrates advantages and disadvantages regarded of the chosen nature-inspired meta-heuristic techniques and considers their algorithms metrics. Moreover, to apply better load balancing techniques in the future, the important challenges of these techniques have been investigated.

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QoS Aware load balance in software defined LTE networks

Cited by 40 publications

References 11 publications

Delay‐ and energy‐aware load balancing in ultra‐dense heterogeneous 5G networks

Delay‐ and energy‐aware load balancing in ultra‐dense heterogeneous 5G networks

Theory of Algorithm Suitability on Managing Radio Resources in Next Generation Mobile Networks

Nature‐inspired meta‐heuristic algorithms for solving the load balancing problem in the software‐defined network

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