Orchestration of energy efficiency capabilities in networks

Riekstin, Ana Carolina; Januario, Guilherme C.; Rodrigues, Bruno; Nascimento, Viviane Tavares; Carvalho, Tereza Cristina Melo de Brito; Meiroşu, Cătălin

doi:10.1016/j.jnca.2015.06.015

Cited by 18 publications

(6 citation statements)

References 25 publications

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“…DT was combined with SVM in this model because SVM can only handle problems with binary classification. For energy-saving solutions for data centers and network infrastructures, a solution that is not a base technique was proposed in [43]. Following the trend of using DTs, Soltani and Mutka [44] presented a scheme using DT cognitive routing to determine the optimal path for sending data over a mesh cognitive radio network.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

et al. 2019

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The 5G network is a next-generation wireless form of communication and the latest mobile technology. In practice, 5G utilizes the Internet of Things (IoT) to work in high-traffic networks with multiple nodes/sensors in an attempt to transmit their packets to a destination simultaneously, which is a characteristic of IoT applications. Due to this, 5G offers vast bandwidth, low delay, and extremely high data transfer speed. Thus, 5G presents opportunities and motivations for utilizing next-generation protocols, especially the stream control transmission protocol (SCTP). However, the congestion control mechanisms of the conventional SCTP negatively influence overall performance. Moreover, existing mechanisms contribute to reduce 5G and IoT performance. Thus, a new machine learning model based on a decision tree (DT) algorithm is proposed in this study to predict optimal enhancement of congestion control in the wireless sensors of 5G IoT networks. The model was implemented on a training dataset to determine the optimal parametric setting in a 5G environment. The dataset was used to train the machine learning model and enable the prediction of optimal alternatives that can enhance the performance of the congestion control approach. The DT approach can be used for other functions, especially prediction and classification. DT algorithms provide graphs that can be used by any user to understand the prediction approach. The DT C4.5 provided promising results, with more than 92% precision and recall.

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Section: Related Workmentioning

confidence: 99%

Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

et al. 2019

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“…Policy has been used in several contexts as a vehicle for representing configuration of secure groups [30], network management [35], threat mitigation [18], access control [13], and data retrieval systems [15]. These approaches define a schema for their target problem and do not consider the challenges in secure data exchange.…”

Section: Related Workmentioning

confidence: 99%

Curie

Celik

Acar

Aksu

et al. 2019

Proceedings of the Ninth ACM Conference on Data and Application Security and Privacy

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Data sharing among partners-users, companies, organizations-is crucial for the advancement of collaborative machine learning in many domains such as healthcare, finance, and security. Sharing through secure computation and other means allow these partners to perform privacy-preserving computations on their private data in controlled ways. However, in reality, there exist complex relationships among members (partners). Politics, regulations, interest, trust, data demands and needs prevent members from sharing their complete data. Thus, there is a need for a mechanism to meet these conflicting relationships on data sharing. This paper presents Curie 1 , an approach to exchange data among members who have complex relationships. A novel policy language, CPL, that allows members to define the specifications of data exchange requirements is introduced. With CPL, members can easily assert who and what to exchange through their local policies and negotiate a global sharing agreement. The agreement is implemented in a distributed privacy-preserving model that guarantees sharing among members will comply with the policy as negotiated. The use of Curie is validated through an example healthcare application built on recently introduced secure multi-party computation and differential privacy frameworks, and policy and performance trade-offs are explored. CCS CONCEPTS• Information systems → Data exchange; • Security and privacy → Economics of security and privacy. KEYWORDSCollaborative learning; policy language; secure data exchange 1 arXiv:1702.08342v3 [cs.CR]

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“…As a result, GAL aims to provide scalability and optimally allocates resources with reduced energy consumption in the network. Moreover, Riekstin et al 54 demonstrates Sustainability Oriented System (SOS) 55 with Green SDN 16 approach for orchestrating and testing various energy efficient capabilities and dealing with the conflicts between them. Furthermore, GreSDN 2 architecture is introduced for an improvement of energy efficiency, whereas the working status of the network is calculated based on the routing algorithms.…”

Section: Green Control Layermentioning

confidence: 99%

GREEN SDN — An enhanced paradigm of SDN: Review, taxonomy, and future directions

Moin

Karim

Safdar

et al. 2018

Concurrency and Computation

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Network architectures nowadays are not able to address the requirements of future Internet, as they are not sufficient, ossified, and are power hungry on the TCP/IP paradigm. Therefore, to promote the realizable progression toward the new paradigms and modern protocols, modern routers are required to become programmable for the flexible control and dynamic management of both power states and traffic flows. In this scenario, Software Defined Networking (SDN) is considered as an exemplary approach for providing the support of dynamic traffic flows of each node on a network. Besides the benefits that SDN brought to the networking area, there are still crucial challenges such as to design an effective, lightweight API, rich algorithms, and traffic forwarding rules for SDN layers along with the adoption of an energy efficient infrastructure that could have the least effect on the environment. The aim of this research is to drive the attention toward environment friendly adoption and implementation of SDN that could deal with the growing number of users and network equipment. For this purpose, in this research, a comprehensive review on SDN while mapping the characteristics of green computing with SDN is provided. In addition to that, the research stressed on the benefits of Green SDN through a proposed G-SDN framework. Some well-defined case studies are presented to highlight the need of greening an SDN infrastructure. Finally, various research directions are presented to the wide adoption of SDN technology for academia and industry alike. KEYWORDS green computing, green SDN, SDN, software defined networks 1 INTRODUCTION Software Defined Networking (SDN) offers a high level of flexibility, controllability, and programmability in networks with the power of centralized control. The centralized controller at SDN faces a significant performance drawback when dealing with huge network flows. 1 On the other hand, features provided by SDN allows an ease in deploying the strategies that are designed to overcome the existing problems of power and energy-waste. 2 Green computing refers to the reduction of hazard and toxic materials, improved efficiency of energy including recycling of factory wastage, 3 and it has become an integral part to enhance the reliability of each emerging computing domains. Even the cloud and green computing are applicable side by side to make greener internet and enhance services. The former techniques allow the services with ease when required at a cost of complex infrastructure. However, the later approach has edge in providing all of these services with efficient energy usage, recyclability, low environmental impact, reduced carbon emission, and much more facilities. 4 In addition, to provide the promising services to the intended users, wired and wireless networks 5,6 have been followed by many directions in prior studies 5-7 such as the closing of forward and feedback loops. 8 In this way, there exists a variety of designs such as low-power solutions with slow and fast dynamics. Solutions with sl...

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Orchestration of energy efficiency capabilities in networks

Cited by 18 publications

References 25 publications

Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

Curie

GREEN SDN — An enhanced paradigm of SDN: Review, taxonomy, and future directions

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