Research Project: GÖREVDEŞ AĞ SERVİSLERİNDE ENERJİ VERİMLİLİĞİ
Loading...
Contributors
Funders
ID
TB.00033
Authors
Özkasap, Öznur
Faculty Member
Publications
A survey of energy efficiency in SDN: software-based methods and optimization models
(Academic Press, 2019) Assefa, Beakal Gizachew; Özkasap, Öznur; N/A; Department of Computer Engineering; Yes; College of Engineering
Software 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.
Energy efficient video decoding on multi-core devices
(Association for Computing Machinery, 2012) Gürler, Cihat Göktuğ; Kılıçarslan, Damla; Özkasap, Öznur; Tekalp, Ahmet Murat; Department of Electrical and Electronics Engineering; Department of Computer Engineering; Yes; College of Engineering
Emergence of high quality media applications results in larger data sizes as well as higher bitrates of digital multimedia contents, and their significant share on the overall Internet traffic. These lead to an increase in the energy consumption rates and performance requirements for real-time video decoding. In this study, we propose parallel video decoding solutions to provide real-time decoding performance with reduced energy consumption on multi-core devices. Various approaches of parallelism at data and task levels can be incorporated in video decoders, bringing efficiency in energy consumption rates and/or performance. We offer and develop two approaches for the H.264 standard. The former is based on a coarse-grained frame level, and the latter is a fine-grained macroblock level parallelism. The implementations were conducted on a shared memory multi-core platform as an all software solution for real-time scalable video decoding. We also discuss energy efficiency as well as performance results. As part of our ongoing work, further parallelization methods such as parallelism at slice level, and parallel decoding of consecutive groups of pictures on the H.264/SVC decoder are discussed
Task allocation in volunteer computing networks under monetary budget constraints
(Springer, 2015) Güler, Hüseyin; Özkasap, Öznur; Barla Cambazoglu, B.; Department of Computer Engineering; Yes; College of Engineering
In volunteer computing networks, the peers contribute to the solution of a computationally intensive problem by freely providing their computational resources, i.e., without seeking any immediate financial benefit. In such networks, although the peers can set certain bounds on how much their resources can be exploited by the network, the monetary cost that the network brings to the peers is unclear. In this work, we propose a volunteer computing network where the peers can set monetary budgets, limiting the financial burden incurred on them due the usage of their computational resources. Under the assumption that the price of the electricity consumed by the peers has temporal variation, we show that our approach leads to an interesting task allocation problem, where the goal is to maximize the amount of work done by the peers without violating the monetary budget constraints set by them. We propose various heuristics as solution to the problem, which is NP-hard. Our extensive simulations using realistic data traces and real-life electricity prices demonstrate that the proposed techniques considerably increase the amount of useful work done by the peers, compared to a baseline technique.
MER-SDN: Machine learning framework for traffic aware energy efficient routing in SDN
(Institute of Electrical and Electronics Engineers, 2018) Assefa, Beakal Gizachew; Özkasap, Öznur; Department of Computer Engineering; Yes; College of Engineering
Software 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.
Spatial and thermal aware methods for efficient workload management in distributed data centers
(Elsevier, 2024) Ali, Ahsan; Özkasap, Öznur; N/A; Department of Computer Engineering; Graduate School of Sciences and Engineering; Yes; College of Engineering; GRADUATE SCHOOL OF SCIENCES AND ENGINEERING
Geographically distributed data centers provide facilities for users to fulfill the demand of storage and computations, where most of the operational cost is due to electricity consumption. In this study, we address the problem of energy consumption of cloud data centers and identify key characteristics of techniques proposed for reducing operational costs, carbon emissions, and financial penalties due to service level agreement (SLA) violations. By considering computer room air condition (CRAC) units that utilize outside air for cooling purposes as well as temperature and space-varying properties, we propose the energy cost model which takes into account temperature ranges for cooling purposes and operations of CRAC units. Then, we propose spatio-thermal-aware algorithms to manage workload using the variation of electricity price, locational outside and within the data center temperature, where the aim is to schedule the incoming workload requests with minimum SLA violations, cooling cost, and energy consumption. We analyzed the performance of our proposed algorithms and compared the experimental results with the benchmark algorithms for metrics of interest including SLA violations, cooling cost, and overall operations cost. Modeling, experiments, and verification conducted on CloudSim with realistic data center scenarios and workload traces show that the proposed algorithms result in reduced SLA violations, save between 15% to 75% of cooling cost and between 3.89% to 39% of the overall operational cost compared to the existing solutions.
