Researcher: Assefa, Beakal Gizachew
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Assefa, Beakal Gizachew
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Publication Metadata only Framework for traffic proportional energy efficiency in software defined networks(IEEE, 2018) N/A; Department of Computer Engineering; Assefa, Beakal Gizachew; Özkasap, Öznur; PhD Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 113507Software Defined Networking (SDN) achieves programmability of network elements by separating the control and the forwarding planes, and provides efficiency through optimized routing and flexibility in network management. As the energy costs contribute largely to the overall costs in networks, energy efficiency is a significant design requirement for modern networking mechanisms. However, designing energy efficient solutions is complicated since there is a trade-off between energy efficiency and network performance. In this paper, we propose traffic proportional energy efficient framework for SDN and heuristics algorithm that maintains the tradeoff between efficiency and performance. We also present IP formulation for traffic proportional energy efficiency problem. Comprehensive experiments conducted on Mininet emulator and PDX controller using Abilene, Atlanta, and Nobel-Germany real-world topologies and traffic traces show that our approach saves up to 50% energy while achieving a performance closer to the algorithms prioritizing performance.Publication Metadata only A survey of energy efficiency in SDN: Software-based methods and optimization models(Elsevier, 2019) N/A; N/A; Department of Computer Engineering; Assefa, Beakal Gizachew; Özkasap, Öznur; PhD Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; 113507Software Defined Networking (SDN) paradigm has the benefits of programmable network elements by separating the control and the forwarding planes, efficiency through optimized routing and flexibility in network management. As the energy costs contribute largely to the overall costs in networks, energy efficiency has become a significant design requirement for modem networking mechanisms. However, designing energy efficient solutions is non-trivial since they need to tackle the trade-off between energy efficiency and network performance. In this article, we address the energy efficiency capabilities that can be utilized in the emerging SDN. We provide a comprehensive and novel classification of software-based energy efficient solutions into subcategories of traffic aware, end system aware and rule placement. We propose general optimization models for each subcategory, and present the objective function, the parameters and constraints to be considered in each model. Detailed information on the characteristics of state-of-the-art methods, their advantages, drawbacks are provided. Hardware-based solutions used to enhance the efficiency of switches are also described. Furthermore, we discuss the open issues and future research directions in the area of energy efficiency in SDN.Publication Metadata only Link utility and traffic aware energy saving in software defined networks(Institute of Electrical and Electronics Engineers (IEEE), 2018) N/A; Department of Computer Engineering; Assefa, Beakal Gizachew; Özkasap, Öznur; PhD Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 113507Software Defined Networking (SDN) is an emerging network paradigm that gains increasing attention both from academia and industry. Energy saving aspects of network protocols, being studied for different network technologies, have also recently been addressed in SDN. In this paper, we address the traffic and energy aware routing problem in SDN and propose link utility based heuristic algorithms, namely NSP and NMU, that are not only general in their applicability but also balance the trade-off between energy saving and performance. We propose an IP formulation for traffic and energy aware routing problem based on link utility information, and evaluate the algorithms using real traces of low, medium and high traffic volumes and network topologies. NSP and NMU are shown to outperform the existing solutions in terms of average path length and achieve up to 37% energy saving.Publication Metadata only A novel utility based metric and routing for energy efficiency in software defined networking(Institute of Electrical and Electronics Engineers (IEEE), 2019) N/A; Department of Computer Engineering; Assefa, Beakal Gizachew; Özkasap, Öznur; PhD Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 113507Software defined networking (SDN) is a rapidly growing networking paradigm in both industry and research areas, with network programmability as its powerful feature which enables propagating changes in the network easily. However, the links and switches are designed to accommodate maximum traffic load and their power consumption is not traffic aware. The logically centralized control in SDN enables dynamically minimizing the energy consumption of the links and the switches by diverting paths of packets. Energy efficiency and performance are opposite objectives that have to be addressed simultaneously. As the main contributions in this study, we first propose an energy efficiency metric Energy Profit Threshold (EPT) that is applicable to SDN. Then, we provide Integer Programming (IP) formulation with the objective of maximizing the EPT of a software defined network environment. Experimental results show that maximizing the EPT value exhibits energy saving of more than 35 % as compared to other utility based energy saving algorithms.Publication Metadata only KUNLPLab: sentiment analysis on twitter data(Association for Computational Linguistics (ACL), 2014) N/A; Assefa, Beakal Gizachew; PhD Student; Graduate School of Sciences and Engineering; N/AThis paper presents the system submitted by KUNLPLab for SemEval-2014 Task9 - Subtask B: Message Polarity on Twitter data. Lexicon features and bag-of-words features are mainly used to represent the datasets. We trained a logistic regression classifier and got an accuracy of 6% increase from the baseline feature representation. The effect of pre-processing on the classifier’s accuracy is also discussed in this work. © 8th International Workshop on Semantic Evaluation, SemEval 2014 - co-located with the 25th International Conference on Computational Linguistics, COLING 2014, Proceedings.Publication Metadata only MER-SDN(IEEE, 2018) N/A; Department of Computer Engineering; Assefa, Beakal Gizachew; Özkasap, Öznur; PhD Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 113507Software Defined Networking (SDN) achieves programmability of a network through separation of the control and data planes. It enables flexibility in network management and control. Energy efficiency is one of the challenging global problems which has both economic and environmental impact. A massive amount of information is generated in the controller of an SDN based networks. Machine learning gives the ability to computers to progressively learn from data without having to write specific instructions. In this work, we propose MER-SDN: a machine learning framework for traffic aware energy efficient routing in SDN. Feature extraction, training, and testing are the three main stages of the learning machine. Experiments are conducted on Mininet and POX controller using real-world network topology and dynamic traffic traces from SNDlib. Results show that our approach achieves more than 65% feature size reduction, more than 70% accuracy in parameter prediction of an energy efficient heuristics algorithm, also our prediction refine heuristics converges the predicted value to the optimal parameters values with up to 25X speedup as compared to the brute force method.Publication Open Access RESDN: a novel metric and method for energy efficient routing in software defined networks(Institute of Electrical and Electronics Engineers (IEEE), 2020) N/A; Department of Computer Engineering; Assefa, Beakal Gizachew; Özkasap, Öznur; PhD Student; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 113507Software-defined networking (SDN) paradigm, with the flexible and logically centralized control, enables dynamically minimizing the network energy consumption by redirecting paths of packets. However, the links and switches are designed to accommodate maximum traffic volume and their power consumption is not traffic proportional. Moreover, there exists a trade-off between energy efficiency and network performance that need to be considered together. Addressing these issues, we propose an energy efficiency metric named Ratio for Energy Saving in SDN (RESDN) that quantifies energy efficiency based on link utility intervals. We provide integer programming formulation and method for maximizing the RESDN of the network. To the best of our knowledge, RESDN approach is novel as it measures how links are profitably utilized in terms of the amount of energy they consume with respect to their utility. We analyze our approach considering various metrics of interest and different types of SDN enabled switches. Experiments show that maximizing the RESDN value improves energy efficiency while maintaining acceptable network performance. In comparison to state-of-the-art utility-based heuristics, RESDN method achieves up to 30% better ratio for energy saving, 14.7 watts per switch power saving, 38% link saving, 2 hops decrease in average path length, and 5% improved traffic proportionality.