Optimal Joint Spectrum Allocation and Scheduling for Cognitive Radio Networks

Researcher: Xiaohua Xu, PI, Affiliate ICC Member

Sponsor: National Science Foundation

Amount of Support: $244,808

Duration of Support: 2 Years

Abstract: Cognitive Radio Network is considered as a promising paradigm for the future networks. To significantly improve spectrum utilization, we conduct optimal or near-optimal joint spectrum allocation and scheduling in cognitive radio networks. We address critical and practical challenges for spectrum allocation and scheduling in cognitive radio networks, in particular multi-hop cognitive radio networks, such as dynamic traffic demands and pattern, unpredictable primary user activity, wireless interference, and coexistence. We develop creative models and algorithms in the framework of restless multi-armed bandit where the problem for spectrum allocation and scheduling in cognitive radio networks is formulated as a partially observable Markov decision process. The proposed methodology is novel in that it intelligently combines the networked multi-armed bandit modeling, graph theory, and communication scheduling theories. The developed algorithms, models, and protocols significantly improve spectrum utilization in future wireless communication systems and advance the fundamental knowledge and understanding of cognitive radio networks. The proposed algorithms, protocols, and models enable future wireless systems to design, deploy, and operate much more efficiently than today’s systems, which will result in significant economical, societal, and public safety impacts

Objectives: The objective of this project is to significantly improve spectrum utilization through conducting optimal or near-optimal joint spectrum allocation and scheduling in cognitive radio networks. The PIs address critical and practical challenges for spectrum allocation and scheduling in cognitive radio networks, in particular multi-hop cognitive radio networks, such as dynamic traffic demands and pattern, unpredictable primary user activity, wireless interference, and coexistence. A test-bed will be set up to extensively evaluate the designed algorithms and protocols.

Broader Impacts: This project significantly improves the design, deployment, and operation of future wireless communication systems. The proposed algorithms, protocols, and models enable future wireless systems to share spectrum much more efficiently than today’s systems, which will result in significant economical, societal, and public safety impacts. In addition, the proposed research is integrated into education and training for both undergraduate and graduate students. This project also significantly broadens the participation of underrepresented minority groups, e.g., the Native Americans in South Dakota.

Wang, Lixin and Xu, Xiaohua. “Approximation Algorithms for Maximum Weight Independent Set of Links Under the SINR Model,” Ad-hoc \& sensor wireless networks, v.17, 2013, p. 293–311.

Xu, Xiaohua and Li, Xiang-Yang and Song, Min. “Efficient aggregation scheduling in multihop wireless sensor networks with sinr constraints,” Mobile Computing, IEEE Transactions on, v.12, 2013, p. 2518–252.

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The Ontology of Inter-Vehicle Networking with Spatio-Temporal Correlation and Spectrum Cognition

Researcher: Min Song, Professor, Electrical and Computer Engineering

Sponsor: National Science Foundation: NeTS: Small: Collaborative Research

Amount of Support: $221,797

Duration of Support: 3 years

This project investigates fundamental understanding and challenges of inter-vehicle networking, including theoretical foundation and constraints in practice that enable such networks to achieve their performance limits. This is a collaborative research project with Professor Wenye Wang at North Carolina State University.

Summary: Vehicle networks have been playing an increasingly important role in promoting mobile applications, driving safety, network economy, and people’s daily life. It is predicted that there will be over 50 million self-driving cars on the road by 2035; the sheer number and density of vehicles have provided non-negligible resources for computing and communication in vehicular environments. In addition, vehicular communications are also driven by the demands and enforcement of intelligent transportation system (ITS) and standardization activities on DSRC and IEEE 802.11p/WAVE. Many applications, either time-sensitive or delay-tolerant have been proposed and explored, such as cooperative traffic monitoring and control, and recently extended for blind crossing, prevention of collision, real-time detour routes computation, and many others as defined by Car2Car Communication Consortium (C2CCC). Along with the popularity of smart mobile devices, there is also an explosion of mobile applications in various categories, including terrestrial navigation, mobile games, and social networking, through Apple’s App store, Google Play, and Windows phonestore etc. Each aforementioned application seemingly is well-suited for either vehicle-to-vehicle (V2V) ad hoc networks or vehicle-to-infrastructure (V2I) communications. Therefore, vehicular networks have been playing an increasingly important role in promoting mobile applications, driving safety, network economy, and people’s daily life.

In this project, a systematic investigation of vehicular networking properties, which is so called ontology of inter-vehicle communications, will be carried out to acquire in-depth scientific understanding and engineering guidelines that are critical to achieving theoretical performance limits and desirable services. This research includes four key innovative contributions: (i) the discovery of inter-vehicle networks composition by using spectrum cognition in finite and large-scale of V2V and V2I networks, (ii) the space and time domains correlations of vehicles on the move, and development of a set of dissemination strategies using a new constrained mobility model, (iii) detection and identification algorithms to achieve fast neighbor discovery using reinforcement learning, and case-based reasoning scheme; and (iv) theoretical limits of the coverage of messages by following the message trajectory in vehicle networks and schemes to achieve the maximum message coverage in both V2V and V2I networks. The results will advance the knowledge of opportunistic communications and facilitate engineering practice for much-needed applications in vehicular environments.

Intellectual Merit: The intellectual merit of the project centers on the development of theoretical and practical foundations for services using inter-vehicle networks. The project starts from the formation of such opportunistic networks, and then moves on to the coverage of messages, with respect to Euclidean distance and time to stop. Given that an inter-network is in present, the project further studies how resilient such a network under network dynamics, including vehicular movements, message dissemination, and routing schemes. The broader impacts of the proposed research are timely yet long-term, from fully realistic setting of channel modeling, to much-needed applications in vehicular environments, and to transforming performance analysis and protocol design for distributed, dynamic, and mobile systems. Therefore, the proposed research outcome will advance knowledge and understanding not only in the field of vehicular networks, but also mobile ad-hoc networks, cognitive radio networks, wireless sensor networks, and future 5G networks.

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Developing Hands-on Cybersecurity Curriculum with Real-world Case Analysis

Researcher: Yu Cai, Associate Professor, College of Computing

Sponsor: National Security Agency

Amount of Support: $149,184

Duration of Support: 1 year

Abstract: Recent high-profile cyber breaches indicate that cyber attacks are becoming more common, sophisticated and damaging. People with cybersecurity skills are in great demand as the threat environment increasingly becomes more complex and challenging. The need to have well-trained and well-prepared cybersecurity workforce is a pressing issue. The goal of this project is to develop a hands-on cybersecurity curriculum with real-world case analysis. The proposed curriculum includes six cybersecurity related courses: 1. Cyber Ethics; 2. Cyber Security I; 3. Scripting for Automation and Security; 4. Wireless System Administration; 5. Cyber Security II; 6. Digital Forensics. This curriculum is designed for CS and IT students who are interested in cybersecurity.

Under-Ice Mobile Networking: Exploratory Study of Network Cognition and Mobility Control


Min Song, Professor, Electrical and Computer Engineering

Zhaohui Wang, Assistant Professor, Electrical and Computer Engineering

Sponsor: National Science Foundation: EAGER: NeTS

Amount of Support: $299,716

Duration of Support: 3 years

Abstract: Autonomous underwater vehicles (AUVs) with acoustic communication capabilities are the platform of choice for under-ice exploration. Different from commonly studied open-water environment, the sound speed in the under-ice environment exhibits an increasing trend with water depth, which renders sound propagation shadowing and multiple reflections by the ice cover. Such acoustic environment characteristics have to be judiciously accounted in under-ice acoustic communication systems, which otherwise could lead to severe communication disconnection as observed in field experiments. This project focuses on an under-ice AUV network that migrates as a swarm for water sampling in an unknown ice-covered region, and develops algorithms for AUVs to learn the under-ice acoustic environment and adapt AUV mobility to the characteristics of the acoustic environment and the water sample field to achieve optimal under-ice mission performance while maintaining desired acoustic connectivity. This project will expand the frontier of under-ice exploration by autonomous vehicles. Given the vital role of ice-covered regions in many underpinning factors of modern society, such as economic growth and scientific research, this project will yield significant socio-economic impacts. In addition, the project will support two Ph.D. dissertations, and involve junior researchers in both algorithm development and field experiments.

This project will innovate over two interrelated domains: under-ice acoustic environment and network cognition, and adaptive AUV mobility control. Specifically, a recursive algorithm will be developed to estimate the environment parameters pertaining to acoustic propagation, as well as the network state (including AUV positions and velocities), leveraging the acoustic measurements obtained during packet transmissions within the AUV network. The estimated parameters will characterize under-ice acoustic field for AUV mobility control. Moreover, an adaptive algorithm will be designed to adjust the mobility of AUVs to the acoustic field and the water sample field, with a goal of minimizing the sample field estimation error while ensuring desired acoustic connectivity among the AUVs. The developed algorithms will be evaluated via simulations and offline experiment data processing. Within an about 10-month ice-cover period of local lakes in this project, extensive under-ice experiments will be conducted under a wide range of geometric and environment conditions. This project will develop and showcase fundamental and crosscutting techniques for under-ice AUV mobile networking, underlying the synergy of environment cognition, statistical signal processing, and wireless mobile networking.


W. Sun, and Z.-H. Wang. “Modeling and Prediction of Large-Scale Temporal Variation in Underwater Acoustic Channels,” Proc. of MTS/IEEE OCEANS Conference, 2016.

W. Sun, C. Wang, Z.-H. Wang, and M. Song. “Experimental Comparison Between Under-Ice and Open-Water Acoustic Channels,” Proc. of the ACM International Workshop on Underwater Networks (WUWNet), 2015.

Z.-H. Wang, C. Wang, and W. Sun. “Adaptive Transmission Scheduling in Time-Varying Underwater Acoustic Channels,” Proc. of MTS/IEEE OCEANS Conference, 2015.

C. Wang, and Z.-H. Wang. “Signal Alignment for Secure Underwater Coordinated Multipoint Transmissions,” IEEE Transactions on Signal Processing, 2016.

X. Kuai, S. Zhou, Z.-H. Wang. And E. Cheng. “Receiver design for spread-spectrum communications with a small spread in underwater clustered multipath channels,” Journal of Acoustical Society of America, 2017.

C. Wang, and Z.-H. Wang. “Signal Alignment for Secure Underwater Coordinated Multipoint Transmissions,” IEEE Transactions on Signal Processing, 2016.

L. Wei, Y. Tang, Y. Cao, Z.-H. Wang, and M. Gerla. “A Simulation Platform for Software-Defined Underwater Wireless Networks,” Proc. of the ACM International Workshop on Underwater Networks (WUWNet), 2017.

W. Sun, and Z.-H. Wang. “Modeling and Prediction of Large-Scale Temporal Variation in Underwater Acoustic Channels,” Proc. of the MTS/IEEE OCEANS Conference, 2016.

W. Sun, C. Wang, Z.-H. Wang, and M. Song. “Experimental Comparison Between Under-Ice and Open-Water Acoustic Channels,” Proc. of the ACM International Workshop on Underwater Networks (WUWNet), 2015.

W. Sun, C. Wang, Z.-H. Wang, and M. Song. “Estimation of the Under-Ice Acoustic Field in AUV Communication Networks,” Proc. of the ACM International Workshop on Underwater Networks (WUWNet), 2017.

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Understanding and Mitigating Triboelectric Artifacts in Wearable Electronics by Synergic Approaches


Ye Sun, Assistant Professor, Mechanical Engineering—Engineering Mechanics

Shiyan Hu, Adjunct Professor, Electrical and Computer Engineering

Sponsor: National Science Foundation

Amount of Support: $330,504

Duration of Support: 3 years

Abstract: Electrophysiological measurement is a well-accepted tool and standard for health monitoring and well-being management. A great number of electrophysiological measurement devices have been developed including clinical equipment, research products, and consumer electronics. However, until now, it is still challenging to secure long-term stable and accurate signal acquisition, especially in wearable condition, not only for medical application in hospital settings, but also for daily well-being management. Motion-induced artifacts widely exist in electrophysiological recording regardless of electrodes (wet, dry, or noncontact). These artifacts are one of the major impediments against the acceptance of wearable devices and capacitive electrodes in clinical diagnosis. This project is to provide new strategies to mitigate motion-induced artifacts in wearable electronics and design accurate wearable electronics for daily monitoring and disease diagnosis. The PIs will disseminate the research products to both students and the research community. New course materials will be developed for undergraduate and graduate education. Undergraduate and graduate students involved in the research program will obtain diverse knowledge in hardware design and data analytics. For K-12 students, the PIs will provide an integrated research and educational experience through the programs of Engineering Exploration Day for Girls and the Summer Youth Program at Michigan Technological University. A research demo and hands-on experience for triboelectric generation in textile materials will be developed and provided to K-12 students.

The research goal of this proposal is to understand the fundamental mechanism of triboelectric artifacts in wearable devices and provide synergistic solutions to mitigating the artifacts. Three approaches are proposed to achieve the goal: 1) understanding the mechanism of triboelectric charge generation in wearable condition by physical modeling and experimental validation; 2) guided by the understanding, developing tribomaterial-based sensors to manipulate triboelectric charges for artifact removal; 3) leveraging the proposed new tribomaterial-based sensors and statistical data analytics for true electrophysiological signal estimation. If successful, the synergic knowledge produced by the project will not only help improve the traditional bioinstrumentation in the medical society, but also benefit industrial community of consumer wearable electronics.

Li, Xian and Sun, Ye. “WearETE: A Scalable Wearable E-Textile Triboelectric Energy Harvesting System for Human Motion Scavenging,” Sensors, v.17, 2017. doi:10.3390/s17112649

Huang, Hui and Hu, Shiyan and Sun, Ye. “Energy-efficient ECG compression in wearable body sensor network by leveraging empirical mode decomposition,” 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), 2018. doi:10.1109/bhi.2018.8333391

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Self-Interference Modeling in Active Phased Arrays


Timothy Havens, PI, William and Gloria Jackson Associate Professor of Computer Systems
Director, Institute of Computing and Cybersystems

Timothy Schultz, Co-PI, University Professor, Electrical and Computer Engineering

Sponsor: Massachusetts Institute of Technology, Lincoln Laboratory

Amount of Support: $15,000

Abstract: The latest research in phased array systems has focused on accommodating multiple functions—radar, communications, electronic warfare—simultaneously on a single array. However, the work has not thoroughly addressed whether or not the partitioning of the antennas and signal generation in the array could be optimized to maximize the performance of the different functions on the array. This work explores these questions.

An Actuarial Framework of Cyber Risk Management for Power Grids

High voltage towers in the dusk of the evening


Chee-Wooi Ten, Associate Professor, Electrical and Computer Engineering

Yeonwoo Rho, Assistant Professor, Mathematical Sciences

Sponsor: National Science Foundation, CPS: Medium: Collaborative Research

Amount of Support: $348,866

Duration of Support: 3 years

Abstract: As evidenced by the recent cyberattacks against Ukrainian power grids, attack strategies have advanced and new malware agents will continue to emerge. The current measures to audit the critical cyber assets of the electric power infrastructure do not provide a quantitative guidance that can be used to address security protection improvement. Investing in cybersecurity protection is often limited to compliance enforcement based on reliability standards. Auditors and investors must understand the implications of hypothetical worst case scenarios due to cyberattacks and how they could affect the power grids. This project aims to establish an actuarial framework for strategizing technological improvements of countermeasures against emerging cyberattacks on wide-area power networks. By establishing an actuarial framework to evaluate and manage cyber risks, this project will promote a self-sustaining ecosystem for the energy infrastructure, which will eventually help to improve overall social welfare. The advances in cyber insurance will stimulate actuarial research in handling extreme cyber events. In addition, the research and practice related to cybersecurity and cyber insurance for the critical energy infrastructure will be promoted by educating the next generation of the workforce and disseminating the research results.

The objective of this project is to develop an actuarial framework of risk management for power grid cybersecurity. It involves transformative research on using insurance as a cyber risk management instrument for contemporary power grids. The generation of comprehensive vulnerabilities and reliability-based knowledge from extracted security logs and cyber-induced reliability degradation analysis can enable the establishment of risk portfolios for electric utilities to improve their preparedness in protecting the power infrastructure against cyber threats. The major thrusts of this project are: 1) developing an approach to quantifying cyber risks in power grids and determining how mitigation schemes could affect the cascading consequences to widespread instability; 2) studying comprehensively how hypothesized cyberattack scenarios would impact the grid reliability by performing a probabilistic cyber risk assessment; and 3) using the findings from the first two thrusts to construct actuarial models. Potential cyberattack-induced losses on electric utilities will be assessed, based on which insurance policies will be designed and the associated capital market will be explored.

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Developing Anisotropic Media for Transformation Optics by Using Dielectric Photonic Crystals


Elena Semouchkina, Professor, Electrical and Computer Engineering

Sponsor: National Science Foundation

Amount of Support: $337,217

Duration of Support: 3 years

Non-Technical Description: Transformation optics (TO) is based on coordinate transformations, which require proper spatial dispersions of the media parameters. Such media force electromagnetic (EM) waves, moving in the original coordinate system, to behave as if they propagate in a transformed coordinate system. Thus TO introduces a new powerful technique for designing advanced EM devices with superior functionalities. Coordinate transformations can be derived for compressing, expanding, bending, or twisting space, enabling designs of invisibility cloaks, field concentrators, perfect lenses, beam shifters, etc., that may bring advances to various areas of human life. Realization of these devices depends on the possibility of creating media with prescribed EM properties, in particular, directional refractive indices to provide wave propagation with superluminal phase velocities and high refractive indices in the normal direction to cause wave movement along curvilinear paths. Originally, artificial metamaterials (MMs) composed of tiny metallic resonators were chosen for building transformation media. However, a number of serious challenges were encountered, such as extremely narrow frequency band of operation and the high losses in metal elements. The proposed approach is to use dielectric photonic crystals to overcome these major limitations of MM media. This project will allow graduate and undergraduate students, especially women in engineering, to participate in theoretical and experimental EM research. Outreach activities include lectures and hands-on projects in several youth programs to K-12 students.

Technical Description: This project will develop a platform for engineering photonic crystal (PhC)-based media that are free from the major limitations of metamaterial media. The project aims to control wave propagation in media along orthogonal crystallographic directions and relies upon self-collimation phenomena at formulating TO-based prescriptions for refractive indices. For realizing directional dispersions of both superluminal and ordinary indices along desired axes of crystals, proper variations of their lattice parameters will be used. Accurate control of index values will be provided by building the media from crystal fragments with optimized dimensions. Microwave experiments using a parallel-plate waveguide chamber will be performed to record wave propagation and to verify computational results. Technologies developed earlier for fabricating low-loss PhCs will help to implement the practical devices. This interdisciplinary research will integrate electromagnetics, physics, optics, and materials science concepts; employ full-wave computational modeling and design; engineer complex materials architectures; and master characterization techniques for complex structures. The project will open up perspectives for TO by developing new approaches for media engineering and by solving fundamental problems, including integration of self-collimation. This research will integrate electromagnetics, physics, optics, and materials science concepts and will advance the potential of PhCs.


Semouchkina, E.. “A Road to Optical Cloaking Using Transformation Media Built from Photonic Crystals,” 1st International Conference on Optics, Photonics, and Lasers, (OPAL 2019), Barcelona, Spain, 2018.

S Jamilan, G Semouchkin. “Spatial dispersion of index components required for building invisibility cloak medium from photonic crystals,” Journal of optics, v.20, 2018. doi:https://doi.org/10.1088/2040-8986/aab25c

N. P. Gandji, G. B.. “All-dielectric metamaterials: irrelevance of negative refraction to overlapped Mie resonances,” Journal of physics. D, Applied physics, v.50, 2017. doi:https://doi.org/10.1088/1361-6463/aa89d3

Semouchkina, E.. “From microwaves to optics: all-dielectric solutions for coordinate transformation-based devices,” International Symposium NGC2017 (Nano and Giga Challenges in Electronics, Photonics and Renewable Energy), Tomsk, Russia, 2017. Citation details

Gandji, N P and Semouchkin, G B and Semouchkina, E. “All-dielectric metamaterials: irrelevance of negative refraction to overlapped Mie resonances,” Journal of Physics D: Applied Physics, v.50, 2017. doi:10.1088/1361-6463/aa89d3

Jamilan, S. and Semouchkin, G. and Gandji, N. P. and Semouchkina, E.. “Specifics of scattering and radiation from sparse and dense dielectric meta-surfaces,” Journal of Applied Physics, v.125, 2019. doi:10.1063/1.5087422

Jamilan, Saeid and Gandji, Navid Pourramzan and Semouchkin, George and Safari, Fatemeh and Semouchkina, Elena. “Scattering from Dielectric Metasurfaces in Optical and Microwave Ranges,” IEEE Photonics Journal, 2019. doi:10.1109/JPHOT.2019.2908307

Jamilan, Saeid and Semouchkina, Elena. “Employing GRIN PC-Inspired Approach for Building Invisibility Cloak Media from Photonic Crystals,” 2018 IEEE Photonics Conference (IPC), Reston, VA, 2018, 2018. doi:10.1109/IPCon.2018.8527322

Jamilan, S. and Semouchkina, E.. “Broader Analysis of Scattering from a Subwavelength Dielectric Sphere,” 2018 IEEE Photonics Conference (IPC), Reston, VA, 2018, 2018. doi:10.1109/IPCon.2018.8527193

Gandji, N. and Semouchkin, G. and Semouchkina, E.. “Electromagnetic Responses from Planar Arrays of Dielectric Nano-Disks at Overlapping Dipolar Resonances,” 2018 IEEE Research and Applications of Photonics In Defense Conference (RAPID), Miramar Beach, FL, 2018, 2018. doi:10.1109/RAPID.2018.8509022

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Scalable Spectral Sparsification of Graph Laplacians and Integrated Circuits

Circuit board

Researcher: Zhuo Feng, Associate Professor, Electrical and Computer Engineering

Sponsor: National Science Foundation: SHF: Small

Amount of Support: $450,000

Duration of Support: 3 years

Abstract: This research is motivated by investigations on scalable methods for design simplifications of nanoscale integrated circuits (ICs). This is to be achieved by extending the associated spectral graph sparsification framework to handle Laplacian-like matrices derived from general nonlinear IC modeling and simulation problems. The results from this research may prove to be key to the development of highly scalable computer-aided design algorithms for modeling, simulation, design, optimization, as well as verification of future nanoscale ICs that can easily involve multi-billions of circuit components. The algorithms and methodologies developed will be disseminated to leading technology companies that may include semiconductor and Electronic Design Automation companies as well as social and network companies, for potential industrial deployments.

Spectral graph sparsification aims to find an ultra-sparse subgraph (a.k.a. sparsifier) such that its Laplacian can well approximate the original one in terms of its eigenvalues and eigenvectors. Since spectrally similar subgraphs can approximately preserve the distances, much faster numerical and graph-based algorithms can be developed based on these “spectrally” sparsified networks. A nearly-linear complexity spectral graph sparsification algorithm is to be developed based on a spectral perturbation approach. The proposed method is highly scalable and thus can be immediately leveraged for the development of nearly-linear time sparse matrix solvers and spectral graph (data) partitioning (clustering) algorithms for large real-world graph problems in general. The results of the research may also influence a broad range of computer science and engineering problems related to complex system/network modeling, numerical linear algebra, optimization, machine learning, computational fluid dynamics, transportation and social networks, etc.

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Improving Reliability of In-Memory Storage

Electronic circuit board

Researcher: Jianhui Yue, PI, Assistant Professor, Computer Science

Sponsor: National Science Foundation, SHF: Small: Collaborative Research

Amount of Support: $192, 716

Duration of Support: 3 years

Abstract: Emerging nonvolatile memory (NVM) technologies, such as PCM, STT-RAM, and memristors, provide not only byte-addressability, low-latency reads and writes comparable to DRAM, but also persistent writes and potentially large storage capacity like an SSD. These advantages make NVM likely to be next-generation fast persistent storage for massive data, referred to as in-memory storage. Yet, NVM-based storage has two challenges: (1) Memory cells have limited write endurance (i.e., the total number of program/erase cycles per cell); (2) NVM has to remain in a consistent state in the event of a system crash or power loss. The goal of this project is to develop an efficient in-memory storage framework that addresses these two challenges. This project will take a holistic approach, spanning from low-level architecture design to high-level OS management, to optimize the reliability, performance, and manageability of in-memory storage. The technical approach will involve understanding the implication and impact of the write endurance issue when cutting-edge NVM is adopted into storage systems. The improved understanding will motivate and aid the design of cost-effective methods to improve the life-time of in-memory storage and to achieve efficient and reliable consistence maintenance.


Pai Chen, Jianhui Yue, Xiaofei Liao, Hai Jin. “Optimizing DRAM Cache by a Trade-off between Hit Rate and Hit Latency,” IEEE Transactions on Emerging Topics in Computing, 2018. doi:10.1109/TETC.2018.2800721

Chenlei Tang, Jiguang Wan, Yifeng Zhu, Zhiyuan Liu, Peng Xu, Fei Wu and Changsheng Xie. “RAFS: A RAID-Aware File System to Reduce Parity Update Overhead for SSD RAID,” Design Automation Test In Europe Conference (DATE) 2019, 2019.

Pai Chen, Jianhui Yue, Xiaofei Liao, Hai Jin. “Trade-off between Hit Rate and Hit Latency for Optimizing DRAM Cache,” IEEE Transactions on Emerging Topics in Computing, 2018.

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