A novel distributed architecture for IoT image processing using low-cost devices and open internet standards

Industry 4.0 can be defined as the integration of computers and automation to current industrial processes, with addition of smart and autonomous systems leveraged by machine learning techniques. In this scenario, a compact, dependable and fast controller is desired, featuring low energy consumption, easily programming and maintenance, with no mobile parts. Nowadays, computing power in single board computers, e.g. the Raspberry Pi among others, has been increased at a very important rate. In just three generations, Pi computers offer almost a two-fold speed gain, when compared to first models. Its design, an underlying video driver with general capabilities of regular OSes, makes them quite suitable to build image processing systems at very low cost, with no mobile parts and low energy consumption. However, designing such a system for industrial image processing is a tough challenge, since it implies to integrate cameras, image processing libraries, database servers and application software with graphical user interface, in an already resource constrained device. This work presents a new architecture for this kind of systems, by means of open internet standards, using a self-contained, high performance web server to publish a RESTful API and a set of web pages that use latest HTML5 capabilities to manage USB webcams and system data. This proposal also integrates OpenCV as a compiled script on client-side using the new WASM paradigm, with an optimized storage for images using -industry-standard RDBMS and a modular design that can target Windows and Linux as well.Sociedad Argentina de Informática e Investigación Operativ

Predictive Reliability and Fault Management in Exascale Systems: State of the Art and Perspectives

© ACM, 2020. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Computing Surveys, Vol. 53, No. 5, Article 95. Publication date: September 2020. https://doi.org/10.1145/3403956[EN] Performance and power constraints come together with Complementary Metal Oxide Semiconductor technology scaling in future Exascale systems. Technology scaling makes each individual transistor more prone to faults and, due to the exponential increase in the number of devices per chip, to higher system fault rates. Consequently, High-performance Computing (HPC) systems need to integrate prediction, detection, and recovery mechanisms to cope with faults efficiently. This article reviews fault detection, fault prediction, and recovery techniques in HPC systems, from electronics to system level. We analyze their strengths and limitations. Finally, we identify the promising paths to meet the reliability levels of Exascale systems.This work has received funding from the European Union's Horizon 2020 (H2020) research and innovation program under the FET-HPC Grant Agreement No. 801137 (RECIPE). Jaume Abella was also partially supported by the Ministry of Economy and Competitiveness of Spain under Contract No. TIN2015-65316-P and under Ramon y Cajal Postdoctoral Fellowship No. RYC-2013-14717, as well as by the HiPEAC Network of Excellence. Ramon Canal is partially supported by the Generalitat de Catalunya under Contract No. 2017SGR0962.Canal, R.; Hernández Luz, C.; Tornero-Gavilá, R.; Cilardo, A.; Massari, G.; Reghenzani, F.; Fornaciari, W.... (2020). Predictive Reliability and Fault Management in Exascale Systems: State of the Art and Perspectives. ACM Computing Surveys. 53(5):1-32. https://doi.org/10.1145/3403956S132535Abella, J., Hernandez, C., Quinones, E., Cazorla, F. J., Conmy, P. R., Azkarate-askasua, M., … Vardanega, T. (2015). WCET analysis methods: Pitfalls and challenges on their trustworthiness. 10th IEEE International Symposium on Industrial Embedded Systems (SIES). doi:10.1109/sies.2015.7185039E. Agullo L. Giraud A. Guermouche J. Roman and M. Zounon. 2013. Towards resilient parallel linear Krylov solvers: Recover-restart strategies. INRIA Research Report RR-8324. E. Agullo L. Giraud A. Guermouche J. Roman and M. Zounon. 2013. Towards resilient parallel linear Krylov solvers: Recover-restart strategies. INRIA Research Report RR-8324.Agullo, E., Giraud, L., Salas, P., & Zounon, M. (2016). Interpolation-Restart Strategies for Resilient Eigensolvers. SIAM Journal on Scientific Computing, 38(5), C560-C583. doi:10.1137/15m1042115Al-Qawasmeh, A. M., Pasricha, S., Maciejewski, A. A., & Siegel, H. J. (2015). Power and Thermal-Aware Workload Allocation in Heterogeneous Data Centers. IEEE Transactions on Computers, 64(2), 477-491. doi:10.1109/tc.2013.116ARM. 2017. ARM Reliability Availability and Serviceability (RAS) Specification—ARMv8 for the ARMv8-A Architecture Profile. White paper. Retrieved from https://developer.arm.com/docs/ddi0587/latest. ARM. 2017. ARM Reliability Availability and Serviceability (RAS) Specification—ARMv8 for the ARMv8-A Architecture Profile. White paper. Retrieved from https://developer.arm.com/docs/ddi0587/latest.Avizienis, A., Laprie, J.-C., Randell, B., & Landwehr, C. (2004). Basic concepts and taxonomy of dependable and secure computing. IEEE Transactions on Dependable and Secure Computing, 1(1), 11-33. doi:10.1109/tdsc.2004.2Bautista-Gomez, L., Zyulkyarov, F., Unsal, O., & McIntosh-Smith, S. (2016). Unprotected Computing: A Large-Scale Study of DRAM Raw Error Rate on a Supercomputer. SC16: International Conference for High Performance Computing, Networking, Storage and Analysis. doi:10.1109/sc.2016.54Berrocal, E., Bautista-Gomez, L., Di, S., Lan, Z., & Cappello, F. (2017). Toward General Software Level Silent Data Corruption Detection for Parallel Applications. IEEE Transactions on Parallel and Distributed Systems, 28(12), 3642-3655. doi:10.1109/tpds.2017.2735971M.-A. Breuer and A. D. Friedman. 1976. Diagnosis 8 Reliable Design of Digital Systems. Springer. M.-A. Breuer and A. D. Friedman. 1976. Diagnosis 8 Reliable Design of Digital Systems. Springer.P. Bridges K. Ferreira M. Heroux and M. Hoemmen. 2012. Fault-tolerant linear solvers via selective reliability. ArXiv e-prints June 2012. arXiv:1206.1390 [math.NA]. P. Bridges K. Ferreira M. Heroux and M. Hoemmen. 2012. Fault-tolerant linear solvers via selective reliability. ArXiv e-prints June 2012. arXiv:1206.1390 [math.NA].F. Cappello A. Geist W. Gropp S. Kale B. Kramer and M. Snir. 2014. Toward exascale resilience: 2014 update. Supercomput. Front. Innovat. 1 1 (2014). http://superfri.org/superfri/article/view/14. F. Cappello A. Geist W. Gropp S. Kale B. Kramer and M. Snir. 2014. Toward exascale resilience: 2014 update. Supercomput. Front. Innovat. 1 1 (2014). http://superfri.org/superfri/article/view/14.F. J. Cazorla L. Kosmidis E. Mezzetti C. Hernandez J. Abella and T. Vardanega. 2019. Probabilistic worst-case timing analysis: Taxonomy and comprehensive survey. ACM Comput. Surv. 52 1 Article 14 (Feb. 2019) 35 pages. DOI:https://doi.org/10.1145/3301283 F. J. Cazorla L. Kosmidis E. Mezzetti C. Hernandez J. Abella and T. Vardanega. 2019. Probabilistic worst-case timing analysis: Taxonomy and comprehensive survey. ACM Comput. Surv. 52 1 Article 14 (Feb. 2019) 35 pages. DOI:https://doi.org/10.1145/3301283Chan, C. S., Pan, B., Gross, K., Vaidyanathan, K., & Rosing, T. Š. (2014). Correcting vibration-induced performance degradation in enterprise servers. ACM SIGMETRICS Performance Evaluation Review, 41(3), 83-88. doi:10.1145/2567529.2567555Chantem, T., Hu, X. S., & Dick, R. P. (2011). Temperature-Aware Scheduling and Assignment for Hard Real-Time Applications on MPSoCs. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 19(10), 1884-1897. doi:10.1109/tvlsi.2010.2058873Chen, M. Y., Kiciman, E., Fratkin, E., Fox, A., & Brewer, E. (s. f.). Pinpoint: problem determination in large, dynamic Internet services. Proceedings International Conference on Dependable Systems and Networks. doi:10.1109/dsn.2002.1029005Chen, Z. (2011). Algorithm-based recovery for iterative methods without checkpointing. Proceedings of the 20th international symposium on High performance distributed computing - HPDC ’11. doi:10.1145/1996130.1996142Chen, Z. (2013). Online-ABFT. Proceedings of the 18th ACM SIGPLAN symposium on Principles and practice of parallel programming - PPoPP ’13. doi:10.1145/2442516.2442533Coskun, A. K., Rosing, T. S., Mihic, K., De Micheli, G., & Leblebici, Y. (2006). Analysis and Optimization of MPSoC Reliability. Journal of Low Power Electronics, 2(1), 56-69. doi:10.1166/jolpe.2006.007G. Da Costa A. Oleksiak W. Piatek J. Salom and L. Sisó. 2015. Minimization of costs and energy consumption in a data center by a workload-based capacity management. In Energy Efficient Data Centers S. Klingert M. Chinnici and M. Rey Porto (Eds.). Springer International Publishing Cham 102--119. G. Da Costa A. Oleksiak W. Piatek J. Salom and L. Sisó. 2015. Minimization of costs and energy consumption in a data center by a workload-based capacity management. In Energy Efficient Data Centers S. Klingert M. Chinnici and M. Rey Porto (Eds.). Springer International Publishing Cham 102--119.Cupertino, L., Da Costa, G., Oleksiak, A., Pia¸tek, W., Pierson, J.-M., Salom, J., … Zilio, T. (2015). Energy-efficient, thermal-aware modeling and simulation of data centers: The CoolEmAll approach and evaluation results. Ad Hoc Networks, 25, 535-553. doi:10.1016/j.adhoc.2014.11.002Dally, W. J. (1991). Express cubes: improving the performance of k-ary n-cube interconnection networks. IEEE Transactions on Computers, 40(9), 1016-1023. doi:10.1109/12.83652Dauwe, D., Pasricha, S., Maciejewski, A. A., & Siegel, H. J. (2018). Resilience-Aware Resource Management for Exascale Computing Systems. IEEE Transactions on Sustainable Computing, 3(4), 332-345. doi:10.1109/tsusc.2018.2797890R. I. Davis and A. Burns. 2011. A survey of hard real-time scheduling for multiprocessor systems. ACM Comput. Surv. 43 4 Article 35 (Oct. 2011) 44 pages. DOI:https://doi.org/10.1145/1978802.1978814 R. I. Davis and A. Burns. 2011. A survey of hard real-time scheduling for multiprocessor systems. ACM Comput. Surv. 43 4 Article 35 (Oct. 2011) 44 pages. DOI:https://doi.org/10.1145/1978802.1978814Di, S., & Cappello, F. (2016). Adaptive Impact-Driven Detection of Silent Data Corruption for HPC Applications. IEEE Transactions on Parallel and Distributed Systems, 27(10), 2809-2823. doi:10.1109/tpds.2016.2517639Di, S., Guo, H., Gupta, R., Pershey, E. R., Snir, M., & Cappello, F. (2019). Exploring Properties and Correlations of Fatal Events in a Large-Scale HPC System. IEEE Transactions on Parallel and Distributed Systems, 30(2), 361-374. doi:10.1109/tpds.2018.2864184Di, S., Robert, Y., Vivien, F., & Cappello, F. (2017). Toward an Optimal Online Checkpoint Solution under a Two-Level HPC Checkpoint Model. IEEE Transactions on Parallel and Distributed Systems, 28(1), 244-259. doi:10.1109/tpds.2016.2546248J. Dongarra T. Herault and Y. Robert. 2015. Fault Tolerance Techniques for High-Performance Computing. Springer. J. Dongarra T. Herault and Y. Robert. 2015. Fault Tolerance Techniques for High-Performance Computing. Springer.DOWNING, S., & SOCIE, D. (1982). Simple rainflow counting algorithms. International Journal of Fatigue, 4(1), 31-40. doi:10.1016/0142-1123(82)90018-4Eghbalkhah, B., Kamal, M., Afzali-Kusha, H., Afzali-Kusha, A., Ghaznavi-Ghoushchi, M. B., & Pedram, M. (2015). Workload and temperature dependent evaluation of BTI-induced lifetime degradation in digital circuits. Microelectronics Reliability, 55(8), 1152-1162. doi:10.1016/j.microrel.2015.06.004Gottscho, M., Shoaib, M., Govindan, S., Sharma, B., Wang, D., & Gupta, P. (2017). Measuring the Impact of Memory Errors on Application  Performance. IEEE Computer Architecture Letters, 16(1), 51-55. doi:10.1109/lca.2016.2599513Greenberg, A., Hamilton, J. R., Jain, N., Kandula, S., Kim, C., Lahiri, P., … Sengupta, S. (2011). VL2. Communications of the ACM, 54(3), 95-104. doi:10.1145/1897852.1897877Heroux, M. A., Bartlett, R. A., Howle, V. E., Hoekstra, R. J., Hu, J. J., Kolda, T. G., … Stanley, K. S. (2005). An overview of the Trilinos project. ACM Transactions on Mathematical Software, 31(3), 397-423. doi:10.1145/1089014.1089021Hoffmann, G. A., Trivedi, K. S., & Malek, M. (2007). A Best Practice Guide to Resource Forecasting for Computing Systems. IEEE Transactions on Reliability, 56(4), 615-628. doi:10.1109/tr.2007.909764Hsiao, M. Y., Carter, W. C., Thomas, J. W., & Stringfellow, W. R. (1981). Reliability, Availability, and Serviceability of IBM Computer Systems: A Quarter Century of Progress. IBM Journal of Research and Development, 25(5), 453-468. doi:10.1147/rd.255.0453Hughes, G. F., Murray, J. F., Kreutz-Delgado, K., & Elkan, C. (2002). Improved disk-drive failure warnings. IEEE Transactions on Reliability, 51(3), 350-357. doi:10.1109/tr.2002.802886S. Hukerikar and C. Engelmann. 2017. Resilience design patterns: A structured approach to resilience at extreme scale. Supercomput. Front. Innov. 4 3 (2017). DOI:https://doi.org/10.14529/jsfi170301 S. Hukerikar and C. Engelmann. 2017. Resilience design patterns: A structured approach to resilience at extreme scale. Supercomput. Front. Innov. 4 3 (2017). DOI:https://doi.org/10.14529/jsfi170301Hussain, H., Malik, S. U. R., Hameed, A., Khan, S. U., Bickler, G., Min-Allah, N., … Rayes, A. (2013). A survey on resource allocation in high performance distributed computing systems. Parallel Computing, 39(11), 709-736. doi:10.1016/j.parco.2013.09.009Intel Corporation. [n.d.]. Intel Xeon Processor E7 Family: Reliability Availability and Serviceability. White paper. https://www.intel.com/content/www/us/en/processors/xeon/xeon-e7-family-ras-server-paper.html. Intel Corporation. [n.d.]. Intel Xeon Processor E7 Family: Reliability Availability and Serviceability. White paper. https://www.intel.com/content/www/us/en/processors/xeon/xeon-e7-family-ras-server-paper.html.Jha, S., Formicola, V., Martino, C. D., Dalton, M., Kramer, W. T., Kalbarczyk, Z., & Iyer, R. K. (2018). Resiliency of HPC Interconnects: A Case Study of Interconnect Failures and Recovery in Blue Waters. IEEE Transactions on Dependable and Secure Computing, 15(6), 915-930. doi:10.1109/tdsc.2017.2737537Kiciman, E., & Fox, A. (2005). Detecting Application-Level Failures in Component-Based Internet Services. IEEE Transactions on Neural Networks, 16(5), 1027-1041. doi:10.1109/tnn.2005.853411Kim, T., Sun, Z., Cook, C., Zhao, H., Li, R., Wong, D., & Tan, S. X.-D. (2016). Invited - Cross-layer modeling and optimization for electromigration induced reliability. Proceedings of the 53rd Annual Design Automation Conference. doi:10.1145/2897937.2905010Kurowski, K., Oleksiak, A., Piątek, W., Piontek, T., Przybyszewski, A., & Węglarz, J. (2013). DCworms – A tool for simulation of energy efficiency in distributed computing infrastructures. Simulation Modelling Practice and Theory, 39, 135-151. doi:10.1016/j.simpat.2013.08.007Langou, J., Chen, Z., Bosilca, G., & Dongarra, J. (2008). Recovery Patterns for Iterative Methods in a Parallel Unstable Environment. SIAM Journal on Scientific Computing, 30(1), 102-116. doi:10.1137/040620394J. C. Laprie (Ed.). 1995. Dependability—Its Attributes Impairments and Means. Springer-Verlag Berlin. J. C. Laprie (Ed.). 1995. Dependability—Its Attributes Impairments and Means. Springer-Verlag Berlin.Laprie, J.-C. (s. f.). DEPENDABLE COMPUTING AND FAULT TOLERANCE : CONCEPTS AND TERMINOLOGY. Twenty-Fifth International Symposium on Fault-Tolerant Computing, 1995, ’ Highlights from Twenty-Five Years’. doi:10.1109/ftcsh.1995.532603Lasance, C. J. M. (2003). Thermally driven reliability issues in microelectronic systems: status-quo and challenges. Microelectronics Reliability, 43(12), 1969-1974. doi:10.1016/s0026-2714(03)00183-5Yinglung Liang, Yanyong Zhang, Sivasubramaniam, A., Jette, M., & Sahoo, R. (s. f.). BlueGene/L Failure Analysis and Prediction Models. International Conference on Dependable Systems and Networks (DSN’06). doi:10.1109/dsn.2006.18Lin, T.-T. Y., & Siewiorek, D. P. (1990). Error log analysis: statistical modeling and heuristic trend analysis. IEEE Transactions on Reliability, 39(4), 419-432. doi:10.1109/24.58720Losada, N., González, P., Martín, M. J., Bosilca, G., Bouteiller, A., & Teranishi, K. (2020). Fault tolerance of MPI applications in exascale systems: The ULFM solution. Future Generation Computer Systems, 106, 467-481. doi:10.1016/j.future.2020.01.026Lyons, R. E., & Vanderkulk, W. (1962). The Use of Triple-Modular Redundancy to Improve Computer Reliability. IBM Journal of Research and Development, 6(2), 200-209. doi:10.1147/rd.62.0200M. Médard and S. S. Lumetta. 2003. Network Reliability and Fault Tolerance. American Cancer Society. Retrieved from arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1002/0471219282.eot281. M. Médard and S. S. Lumetta. 2003. Network Reliability and Fault Tolerance. American Cancer Society. Retrieved from arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1002/0471219282.eot281.Moody, A., Bronevetsky, G., Mohror, K., & de Supinski, B. (2010). Detailed Modeling, Design, and Evaluation of a Scalable Multi-level Checkpointing System. doi:10.2172/984082Moor Insights 8 Strategy. 2017. AMD EPYC Brings New RAS Capability. White paper. Retrieved from https://www.amd.com/system/files/2017-06/AMD-EPYC-Brings-New-RAS-Capability.pdf. Moor Insights 8 Strategy. 2017. AMD EPYC Brings New RAS Capability. White paper. Retrieved from https://www.amd.com/system/files/2017-06/AMD-EPYC-Brings-New-RAS-Capability.pdf.Mulas, F., Atienza, D., Acquaviva, A., Carta, S., Benini, L., & De Micheli, G. (2009). Thermal Balancing Policy for Multiprocessor Stream Computing Platforms. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 28(12), 1870-1882. doi:10.1109/tcad.2009.2032372Oleksiak, A., Kierzynka, M., Piatek, W., Agosta, G., Barenghi, A., Brandolese, C., … Janssen, U. (2017). M2DC – Modular Microserver DataCentre with heterogeneous hardware. Microprocessors and Microsystems, 52, 117-130. doi:10.1016/j.micpro.2017.05.019Oxley, M. A., Jonardi, E., Pasricha, S., Maciejewski, A. A., Siegel, H. J., Burns, P. J., & Koenig, G. A. (2018). Rate-based thermal, power, and co-location aware resource management for heterogeneous data centers. Journal of Parallel and Distributed Computing, 112, 126-139. doi:10.1016/j.jpdc.2017.04.015K. O’brien I. Pietri R. Reddy A. Lastovetsky and R. Sakellariou. 2017. A survey of power and energy predictive models in HPC systems and applications. ACM Comput. Surv. 50 3 Article 37 (June 2017) 38 pages. DOI:https://doi.org/10.1145/3078811 K. O’brien I. Pietri R. Reddy A. Lastovetsky and R. Sakellariou. 2017. A survey of power and energy predictive models in HPC systems and applications. ACM Comput. Surv. 50 3 Article 37 (June 2017) 38 pages. DOI:https://doi.org/10.1145/3078811Park, S.-M., & Humphrey, M. (2011). Predictable High-Performance Computing Using Feedback Control and Admission Control. IEEE Transactions on Parallel and Distributed Systems, 22(3), 396-411. doi:10.1109/tpds.2010.100Pfefferman, J. D., & Cernuschi-Frias, B. (2002). A nonparametric nonstationary procedure for failure prediction. IEEE Transactions on Reliability, 51(4), 434-442. doi:10.1109/tr.2002.804733Rangan, K. K., Wei, G.-Y., & Brooks, D. (2009). Thread motion. ACM SIGARCH Computer Architecture News, 37(3), 302-313. doi:10.1145/1555815.1555793Paolo Rech. [n.d.]. Reliability Issues in Current and Future Supercomputers. Retrieved from http://energysfe.ufsc.br/slides/Paolo-Rech-260917.pdf. Paolo Rech. [n.d.]. Reliability Issues in Current and Future Supercomputers. Retrieved from http://energysfe.ufsc.br/slides/Paolo-Rech-260917.pdf.F. Reghenzani G. Massari and W. Fornaciari. 2019. The real-time Linux kernel: A survey on PREEMPT_RT. Comput. Surveys 52 1 Article 18 (Feb. 2019) 36 pages. DOI:https://doi.org/10.1145/3297714 F. Reghenzani G. Massari and W. Fornaciari. 2019. The real-time Linux kernel: A survey on PREEMPT_RT. Comput. Surveys 52 1 Article 18 (Feb. 2019) 36 pages. DOI:https://doi.org/10.1145/3297714F. Salfner M. Lenk and M. Malek. 2010. A survey of online failure prediction methods. ACM Comput. Surv. 42 3 Article 10 (March 2010) 42 pages. DOI:https://doi.org/10.1145/1670679.1670680 F. Salfner M. Lenk and M. Malek. 2010. A survey of online failure prediction methods. ACM Comput. Surv. 42 3 Article 10 (March 2010) 42 pages. DOI:https://doi.org/10.1145/1670679.1670680Salfner, F., Schieschke, M., & Malek, M. (2006). Predicting failures of computer systems: a case study for a telecommunication system. Proceedings 20th IEEE International Parallel & Distributed Processing Symposium. doi:10.1109/ipdps.2006.1639672Shi, L., Chen, H., Sun, J., & Li, K. (2012). vCUDA: GPU-Accelerated High-Performance Computing in Virtual Machines. IEEE Transactions on Computers, 61(6), 804-816. doi:10.1109/tc.2011.112D. P. Siewiorek and R. S. Swarz. 1998. Reliable Computer Systems 3rd ed. A. K. Peters Ltd. D. P. Siewiorek and R. S. Swarz. 1998. Reliable Computer Systems 3rd ed. A. K. Peters Ltd.Singh, S., & Chana, I. (2016). A Survey on Resource Scheduling in Cloud Computing: Issues and Challenges. Journal of Grid Computing, 14(2), 217-264. doi:10.1007/s10723-015-9359-2Slegel, T. J., Averill, R. M., Check, M. A., Giamei, B. C., Krumm, B. W., Krygowski, C. A., … Webb, C. F. (1999). IBM’s S/390 G5 microprocessor design. IEEE Micro, 19(2), 12-23. doi:10.1109/40.755464Sridhar, A., Sabry, M. M., & Atienza, D. (2014). A Semi-Analytical Thermal Modeling Framework for Liquid-Cooled ICs. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 33(8), 1145-1158. doi:10.1109/tcad.2014.2323194Sridharan, V., DeBardeleben, N., Blanchard, S., Ferreira, K. B., Stearley, J., Shalf, J., & Gurumurthi, S. (2015). Memory Errors in Modern Systems. ACM SIGARCH Computer Architecture News, 43(1), 297-310. doi:10.1145/2786763.2694348Stathis, J. H. (2018). The physics of NBTI: What do we really know? 2018 IEEE International Reliability Physics Symposium (IRPS). doi:10.1109/irps.2018.8353539Stellner, G. (s. f.). CoCheck: checkpointing and process migration for MPI. Proceedings of International Conference on Parallel Processing. doi:10.1109/ipps.1996.508106Stone, J. E., Gohara, D., & Shi, G. (2010). OpenCL: A Parallel Programming Standard for Heterogeneous Computing Systems. Computing in Science & Engineering, 12(3), 66-73. doi:10.1109/mcse.2010.69Subasi, O., Di, S., Bautista-Gomez, L., Balaprakash, P., Unsal, O., Labarta, J., … Cappello, F. (2018). Exploring the capabilities of support vector machines in detecting silent data corruptions. Sustainable Computing: Informatics and Systems, 19, 277-290. doi:10.1016/j.suscom.2018.01.004Tang, D., & Iyer, R. K. (1993). Dependability measurement and modeling of a multicomputer system. IEEE Transactions on Computers, 42(1), 62-75. doi:10.1109/12.192214D. Turnbull and N. Alldrin. 2003. Failure Prediction in Hardware Systems. Tech. rep. University of California San Diego CA. Retrieved from http://www.cs.ucsd.edu/ dturnbul/Papers/ServerPrediction.pdf. D. Turnbull and N. Alldrin. 2003. Failure Prediction in Hardware Systems. Tech. rep. University of California San Diego CA. Retrieved from http://www.cs.ucsd.edu/ dturnbul/Papers/ServerPrediction.pdf.Vilalta, R., Apte, C. V., Hellerstein, J. L., Ma, S., & Weiss, S. M. (2002). Predictive algorithms in the management of computer systems. IBM Systems Journal, 41(3), 461-474. doi:10.1147/sj.413.0461Vinoski, S. (2007). Reliability with Erlang. IEEE Internet Com

Power converters control for photovoltaic water pumping system

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The main objective was to study the energy usage in Faculty of Engineering office, Electronic and Telecommunication Engineering, and Civil Engineering building. The study is focusing in cooling or air-conditioning system. This study involved research and analysis, where inside and outside temperature were taken using Digital Thermometer (Thermoset). The relationship between the inside and outside temperature is determined through the knowledge of heat transfer. In addition, with these data different heat loss of material construction include concrete wall, wood (wall and door) and glass window was then be determined. The result of this study determined the energy usage in building much as of the air-conditioning system. The research is suggested recommendations for optimum energy usage and saving energy cost. The relationship obtained from this study is then used as a reference guide for the energy saving

Active thermal control of power electronic modules in smart transformer applications

The Smart Transformer (ST) is a possible solution to obtain intelligent nodes in the electrical grid, which can be used for the grid management and increase the capacity for the integration of renewable energy sources. A problem for the application of the ST in the distribution grid is the expected lower reliability in comparison with the traditional transformer. To address this problem, the knowledge of power system, power electronics and reliability is combined in this work. Following the "Physics of failure" approach, the most frequently failing components are identified, their load profile in the electrical distribution grid is analyzed and finally solutions are developed to improve the reliability. The power semiconductors are found to be the most prone to fail components and most of their failure mechanisms are found to be affected by thermal cycling. For this reason, thermal stress analysis is performed for the three-stage ST. As an opportunity to increase the reliability, active thermal control is introduced, which is a software based solution for the reduction of the thermal stress during operation. The existing approaches from literature are reviewed and categorized into control of the power converter losses and the control of the device loading. For increasing the reliability by control of the power converter losses, one algorithm is introduced and validated for hard switching power converters and one algorithm is introduced for soft switching power semiconductors. Controlling the thermal stress of modular building blocks in a modular power converter, referring to power routing, is proposed. The capability of the algorithm is investigated analytically for series connected and parallel connected modular building blocks. For the validation, the influence of the power routing on the loading of the single cell is demonstrated experimentally for series connected, parallel connected and medium frequency transformer coupled cells in modular power converters

Advanced Concepts for Renewable Energy Supply of Data Centres

The rapid increase of cloud computing, high performance computing (HPC) and the vast growth in Internet and Social Media use have aroused the interest in energy consumption and the carbon footprint of Data Centres. Data Centres primarily contain electronic equipment used for data processing (servers), data storage (storage equipment), and communications (network equipment). Collectively, this equipment processes, stores, and transmits digital information and is known as information technology (IT) equipment. Advanced Concepts for Renewable Energy Supply of Data Centres introduces a number of technical solutions for the supply of power and cooling energy into Data Centres with enhanced utilisation of renewable energy sources in order to achieve low energy Data Centres. Because of the high energy density nature of these unique infrastructures, it is essential to implement energy efficiency measures and reduce consumption before introducing any renewable energy source. A holistic approach is used with the objective of integrating many technical solutions such as management of the IT (Information Technology) load, efficient electrical supply to the IT systems, Low-Ex air-conditioning systems, interaction with district heating and cooling networks, re-use of heat, free cooling (air, seawater, groundwater), optimal use of heat and cold storage, electrical storage and integration in smart grids. This book is therefore a catalogue of advanced technical concepts that could be integrated into Data Centres portfolio in order to increase the overall efficiency and the share of renewable energies in power and cooling supply. Based on dynamic energy models implemented in TRNSYS some concepts are deeply evaluated through yearly simulations. The results of the simulation are illustrated with Sankey charts, where the energy flows per year within the subsystems of each concept for a selected scenario are shown, and graphs showing the results of parametric analysis. A set of environmental metrics (as the non-renewable primary energy) and financial metrics (CAPEX and OPEX) as well of energy efficiency metrics like the well-known PUE, are described and used to evaluate the different technical concepts

Advanced Concepts for Renewable Energy Supply of Data Centres

The rapid increase of cloud computing, high performance computing (HPC) and the vast growth in Internet and Social Media use have aroused the interest in energy consumption and the carbon footprint of Data Centres. Data Centres primarily contain electronic equipment used for data processing (servers), data storage (storage equipment), and communications (network equipment). Collectively, this equipment processes, stores, and transmits digital information and is known as information technology (IT) equipment. Advanced Concepts for Renewable Energy Supply of Data Centres introduces a number of technical solutions for the supply of power and cooling energy into Data Centres with enhanced utilisation of renewable energy sources in order to achieve low energy Data Centres. Because of the high energy density nature of these unique infrastructures, it is essential to implement energy efficiency measures and reduce consumption before introducing any renewable energy source. A holistic approach is used with the objective of integrating many technical solutions such as management of the IT (Information Technology) load, efficient electrical supply to the IT systems, Low-Ex air-conditioning systems, interaction with district heating and cooling networks, re-use of heat, free cooling (air, seawater, groundwater), optimal use of heat and cold storage, electrical storage and integration in smart grids. This book is therefore a catalogue of advanced technical concepts that could be integrated into Data Centres portfolio in order to increase the overall efficiency and the share of renewable energies in power and cooling supply. Based on dynamic energy models implemented in TRNSYS some concepts are deeply evaluated through yearly simulations. The results of the simulation are illustrated with Sankey charts, where the energy flows per year within the subsystems of each concept for a selected scenario are shown, and graphs showing the results of parametric analysis. A set of environmental metrics (as the non-renewable primary energy) and financial metrics (CAPEX and OPEX) as well of energy efficiency metrics like the well-known PUE, are described and used to evaluate the different technical concepts

Survey of FPGA applications in the period 2000 – 2015 (Technical Report)

Romoth J, Porrmann M, Rückert U. Survey of FPGA applications in the period 2000 – 2015 (Technical Report).; 2017.Since their introduction, FPGAs can be seen in more and more different fields of applications. The key advantage is the combination of software-like flexibility with the performance otherwise common to hardware. Nevertheless, every application field introduces special requirements to the used computational architecture. This paper provides an overview of the different topics FPGAs have been used for in the last 15 years of research and why they have been chosen over other processing units like e.g. CPUs