Energy efficient routing and lightpath management in software defined networking based inter-DC elastic optical networks

Panda, Satyasen

doi:10.1016/j.yofte.2019.102128

Cited by 9 publications

(7 citation statements)

References 34 publications

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“…The optimal EE solution therefore also requires planning of the power consumption of the SDN components in the pre-deployment phase. Finally, the limitations of the SDN-based solutions in Table 5 show that used algorithms have diminishing power gains for high arrival rate [81], have complexity issues with the increasing number of users [49], and do not consider high resource demanding multimedia services [83]. An SDN-based energy consumption solution for beyond-5G dense mmWave deployments providing eMBB services to users is therefore a topic to consider for future research.…”

Section: Discussionmentioning

confidence: 99%

“…Game theory and matching theory-based solutions are also attractive for distributed decision-making among entities, as centralized optimization is challenging in multi-stakeholder environments [42][43][44][45][46][47]. Finally, AI-based models (artificial neural networks (ANN) [48,49], Q-learning [50]) are also being utilized to resource management optimization problems. The distribution of the main performance metrics according to the resource optimization objectives is given in Table 1 and the objectives are also mapped to their sections in this review.…”

Section: Overview Of the Data Collected From Selected Papersmentioning

confidence: 99%

“…In [78], the EE maximization problem is modeled as a class of optimization problems called fractional programming to minimize the total power consumption of the entire system. a matching heuristic and a user reallocation heuristic user association successful in distributing high and low data rate demanding users multi-connectivity option is not considered for users total power consumption [78] iterative algorithm with Lagrangian dual decomposition proposed algorithm requires low fronthaul bandwidth for a given EE does not consider any interference between the macro BS and the RRHs EE gain (bits/Joule) [49] adaptive ant colony optimization avoids frequent deactivation and reactivation of lightpaths for new traffic requests…”

Section: Selected Papersmentioning

confidence: 99%

“…The network paradigm shift towards softwarized solutions provides novel options to implement EE algorithms in the optical and radio networks, as seen from the SDNbased solutions proposed in [49,81,83]. An SDN application called energy management and monitoring application, and a power consumption optimizer is developed in [81] to optimize the energy consumption of a C-RAN infrastructure with energy consumption estimation based on flow rates.…”

Section: Selected Papersmentioning

confidence: 99%

“…An SDN application called energy management and monitoring application, and a power consumption optimizer is developed in [81] to optimize the energy consumption of a C-RAN infrastructure with energy consumption estimation based on flow rates. An SDN controller-based power control framework with an adaptive ant colony optimization algorithm is proposed in [49] to avoid the frequent deactivation and reactivation of the lightpaths when new traffic request arrives, thus saving switching power. Finally, a joint caching, computing, and bandwidth resource allocation is designed for SDN in [83] to minimize the energy consumption consumed by content caching, data computing and traffic transmission.…”

Section: Selected Papersmentioning

confidence: 99%

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Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review

2021

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Three convergent processes are likely to shape the future of the internet beyond-5G: The convergence of optical and millimeter wave radio networks to boost mobile internet capacity, the convergence of machine learning solutions and communication technologies, and the convergence of virtualized and programmable network management mechanisms towards fully integrated autonomic network resource management. The integration of network virtualization technologies creates the incentive to customize and dynamically manage the resources of a network, making network functions, and storage capabilities at the edge key resources similar to the available bandwidth in network communication channels. Aiming to understand the relationship between resource management, virtualization, and the dense 5G access and fronthaul with an emphasis on converged radio and optical communications, this article presents a review of how resource management solutions have dealt with optimizing millimeter wave radio and optical resources from an autonomic network management perspective. A research agenda is also proposed by identifying current state-of-the-art solutions and the need to shift all the convergent issues towards building an advanced resource management mechanism for beyond-5G.

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Section: Discussionmentioning

confidence: 99%