Network and Parallel Computing

NAND flash memory has gained widespread acceptance in storage systems because of its superior write/read performance, shockresistance and low-power consumption. I/O scheduling for Solid State Drives (SSDs) has received much attention in recent years for its ability to take advantage of the rich parallelism within SSDs. However, most state-of-the-art I/O scheduling algorithms are oblivious to the increasingly significant inter-block variation introduced by the advanced technology scaling. This paper proposes a variation-aware I/O scheduler by exploiting the speed variation among blocks to minimize the access conflict latency of I/O requests. The proposed VIOS schedules I/O requests into a hierarchical-batch structured queue to preferentially exploit channel-level parallelism, followed by chip-level parallelism. Moreover, conflict write requests are allocated to faster blocks to reduce access conflict of waiting requests. Experimental results shows that VIOS reduces write latency significantly compared to state-of-the-art I/O schedulers while attaining high read efficiency.

[1]  Tsuhan Chen,et al.  Compression with mosaic prediction for image-based rendering applications , 2000, 2000 IEEE International Conference on Multimedia and Expo. ICME2000. Proceedings. Latest Advances in the Fast Changing World of Multimedia (Cat. No.00TH8532).

[2]  Madjid Merabti,et al.  Secure rendering process in cloud computing , 2013, 2013 Eleventh Annual Conference on Privacy, Security and Trust.

[3]  Jens H. Krüger,et al.  Ieee Transactions on Visualization and Computer Graphics 1 Hybrid Rendering with Scheduling under Uncertainty , 2022 .

[4]  Xiaobo Sharon Hu,et al.  Signature-based workload estimation for mobile 3D graphics , 2006, 2006 43rd ACM/IEEE Design Automation Conference.

[5]  Shoaib Amin Banday,et al.  Improving the performance of hierarchical scheduling for rendering , 2014, 2014 Innovative Applications of Computational Intelligence on Power, Energy and Controls with their impact on Humanity (CIPECH).

[6]  Frank Dürr,et al.  Concepts for execution time prediction of 3D GPU rendering , 2014, Proceedings of the 9th IEEE International Symposium on Industrial Embedded Systems (SIES 2014).

[7]  D. Prusa,et al.  Parallel Turing Machines on a Two-Dimensional Tape , 2008 .

[8]  Anastasios Doulamis,et al.  Non-linear prediction of rendering workload for grid infrastructure , 2004 .

[9]  A. Turing On Computable Numbers, with an Application to the Entscheidungsproblem. , 1937 .

[10]  Edward A. Lee The problem with threads , 2006, Computer.

[11]  Bob Iannucci Toward a dataflow/von Neumann hybrid architecture , 1988, [1988] The 15th Annual International Symposium on Computer Architecture. Conference Proceedings.

[12]  Konstantinos Tserpes,et al.  Computational workload prediction for grid oriented industrial applications: the case of 3D-image rendering , 2005, CCGrid 2005. IEEE International Symposium on Cluster Computing and the Grid, 2005..

[13]  伊藤 孝夫 Synchronized alternation and parallelism for three-dimensional automata , 2008 .

[14]  John McCarthy,et al.  LISP 1.5 Programmer's Manual , 1962 .

[15]  Emmanouel A. Varvarigos,et al.  Adjusted fair scheduling and non-linear workload prediction for QoS guarantees in grid computing , 2007, Comput. Commun..

[16]  Niklaus Wirth,et al.  Modula: A language for modular multiprogramming , 1977, Softw. Pract. Exp..

[17]  Satoshi Goto,et al.  Combined hole-filling with spatial and temporal prediction , 2013, 2013 IEEE International Conference on Image Processing.

[18]  Aiguo Song,et al.  A Time Series Based Solution for the Difference Rate Sampling between Haptic Rendering and Visual Display , 2006, 2006 IEEE International Conference on Robotics and Biomimetics.

[19]  Zhou Yi-qi,et al.  Contact Elements Prediction Based Haptic Rendering Method for Collaborative Virtual Assembly System , 2009, 2009 WRI Global Congress on Intelligent Systems.

[20]  Leslie G. Valiant,et al.  A bridging model for parallel computation , 1990, CACM.

[21]  Amit Kumar Yadav,et al.  Solar energy potential assessment of western Himalayan Indian state of Himachal Pradesh using J48 algorithm of WEKA in ANN based prediction model , 2015 .

[22]  Fabiana Piccoli,et al.  Estimation of Volume Rendering Efficiency with GPU in a Parallel Distributed Environment , 2013, ICCS.

[23]  Olga Sourina,et al.  A Prediction Method Using Interpolation for Smooth Six-DOF Haptic Rendering in Multirate Simulation , 2013, 2013 International Conference on Cyberworlds.

[24]  John von Neumann,et al.  First draft of a report on the EDVAC , 1993, IEEE Annals of the History of Computing.

[25]  Hee Yong Youn,et al.  Prediction-Based Dynamic Load Balancing Using Agent Migration for Multi-agent System , 2010, 2010 IEEE 12th International Conference on High Performance Computing and Communications (HPCC).

[26]  Rishi Khan,et al.  Position paper: Towards a codelet-based runtime for exascale computing , 2012 .

[27]  Sartaj Sahni,et al.  A framework for rendering high resolution synthetic aperture radar images on heterogeneous architectures , 2014, 2014 IEEE Symposium on Computers and Communications (ISCC).

[28]  John F. Wakerly,et al.  The programming language PASCAL , 1979, Microprocessors and microsystems.

[29]  Zhang Xiaohong,et al.  A History-Based Motion Prediction Method for Mouse-Based Navigation in 3D Digital City , 2008, 2008 Seventh International Conference on Grid and Cooperative Computing.

[30]  A. Abdel-Hamid,et al.  Prediction-based Prefetching for Remote Rendering Streaming in Mobile Virtual Environments , 2007, 2007 IEEE International Symposium on Signal Processing and Information Technology.

[31]  Michael Wimmer,et al.  Rendering Time Estimation for Real-Time Rendering , 2003, Rendering Techniques.

[32]  Doo Yong Lee,et al.  Real-time haptic rendering using multi-rate output-estimation with ARMAX model , 2007, 2007 International Conference on Control, Automation and Systems.

[33]  Leslie Lamport,et al.  Time, clocks, and the ordering of events in a distributed system , 1978, CACM.

[34]  Makoto Sakamoto,et al.  Parallel Turing machines on four-dimensional input tapes , 2010, Artificial Life and Robotics.

[35]  Yong Liu,et al.  Proxy Position Prediction Based Continuous Local Patch for Smooth Haptic Rendering , 2012, J. Comput. Inf. Sci. Eng..

[36]  Konstantinos Dolkas,et al.  A combined fuzzy-neural network model for non-linear prediction of 3-D rendering workload in Grid computing , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[37]  Alexei Sourin,et al.  Grid-based computer animation rendering , 2006, GRAPHITE '06.