4,423 research outputs found

    Improving network reliability by exploiting path diversity in ad hoc networks with bursty losses

    Get PDF
    In wireless mobile ad hoc networks, end-to-end connections are often subject to failures which do not make the connection non-operational indefinitely but interrupt the communication for intermittent short periods of time. These intermittent failures usually arise from the mobility of hosts, dynamics of the wireless medium or energy-saving mechanisms, and cause bursty packet losses. Reliable communication in this kind of an environment is becoming more important with the emerging use of ad hoc networks for carrying diverse multimedia applications such as voice, video and data. In this thesis, we present a new path reliability model that captures intermittent availability of the paths, and we devise a routing strategy based on our path reliability model in order to improve the network reliability. Our routing strategy takes the advantage of path diversity in the network and uses a diversity coding scheme in order not to compromise efficiency. In diversity coding scheme, if the original information is encoded by using a (N,K) code, then it is enough for the destination to receive any K bits correctly out of N bits to successfully decode the original information. In our scheme, the original information is divided into N subpackets and subpackets are distributed among the available disjoint paths in the network. The distribution of subpackets among the diverse paths is a crucial decision. The subpackets should be distributed 'intelligently' so that the probability of successful reconstruction of the original information is maximized. Given the failure statistics of the paths, and the code rate (N, K), our strategy determines the allocation of subpackets to each path in such a manner that the probability of reconstruction of the original information at the destination is maximized. Simulation results justify the accuracy and efficiency of our approach. Additionally, simulation results show that our multipath routing strategy improves the network reliability substantially compared to the single path routing. In wireless networks, a widely used strategy is to place the nodes into a low energy consuming sleep mode in order to prolong the battery life. In this study, we also consider the cases where the intermittent availability of the nodes is due to the sleep/awake cycles of wireless nodes. A sleep/awake scheduling strategy is proposed which minimizes the packet latency while satisfying the energy saving ratio specified by the energy saving mechanism

    Locating and Protecting Facilities Subject to Random Disruptions and Attacks

    Get PDF
    Recent events such as the 2011 Tohoku earthquake and tsunami in Japan have revealed the vulnerability of networks such as supply chains to disruptive events. In particular, it has become apparent that the failure of a few elements of an infrastructure system can cause a system-wide disruption. Thus, it is important to learn more about which elements of infrastructure systems are most critical and how to protect an infrastructure system from the effects of a disruption. This dissertation seeks to enhance the understanding of how to design and protect networked infrastructure systems from disruptions by developing new mathematical models and solution techniques and using them to help decision-makers by discovering new decision-making insights. Several gaps exist in the body of knowledge concerning how to design and protect networks that are subject to disruptions. First, there is a lack of insights on how to make equitable decisions related to designing networks subject to disruptions. This is important in public-sector decision-making where it is important to generate solutions that are equitable across multiple stakeholders. Second, there is a lack of models that integrate system design and system protection decisions. These models are needed so that we can understand the benefit of integrating design and protection decisions. Finally, most of the literature makes several key assumptions: 1) protection of infrastructure elements is perfect, 2) an element is either fully protected or fully unprotected, and 3) after a disruption facilities are either completely operational or completely failed. While these may be reasonable assumptions in some contexts, there may exist contexts in which these assumptions are limiting. There are several difficulties with filling these gaps in the literature. This dissertation describes the discovery of mathematical formulations needed to fill these gaps as well as the identification of appropriate solution strategies

    CoShare: An Efficient Approach for Redundancy Allocation in NFV

    Full text link
    An appealing feature of Network Function Virtualization (NFV) is that in an NFV-based network, a network function (NF) instance may be placed at any node. On the one hand this offers great flexibility in allocation of redundant instances, but on the other hand it makes the allocation a unique and difficult challenge. One particular concern is that there is inherent correlation among nodes due to the structure of the network, thus requiring special care in this allocation. To this aim, our novel approach, called CoShare, is proposed. Firstly, its design takes into consideration the effect of network structural dependency, which might result in the unavailability of nodes of a network after failure of a node. Secondly, to efficiently make use of resources, CoShare proposes the idea of shared reservation, where multiple flows may be allowed to share the same reserved backup capacity at an NF instance. Furthermore, CoShare factors in the heterogeneity in nodes, NF instances and availability requirements of flows in the design. The results from a number of experiments conducted using realistic network topologies show that the integration of structural dependency allows meeting availability requirements for more flows compared to a baseline approach. Specifically, CoShare is able to meet diverse availability requirements in a resource-efficient manner, requiring, e.g., up to 85% in some studied cases, less resource overbuild than the baseline approach that uses the idea of dedicated reservation commonly adopted for redundancy allocation in NFV

    Flexible provisioning of Web service workflows

    No full text
    Web services promise to revolutionise the way computational resources and business processes are offered and invoked in open, distributed systems, such as the Internet. These services are described using machine-readable meta-data, which enables consumer applications to automatically discover and provision suitable services for their workflows at run-time. However, current approaches have typically assumed service descriptions are accurate and deterministic, and so have neglected to account for the fact that services in these open systems are inherently unreliable and uncertain. Specifically, network failures, software bugs and competition for services may regularly lead to execution delays or even service failures. To address this problem, the process of provisioning services needs to be performed in a more flexible manner than has so far been considered, in order to proactively deal with failures and to recover workflows that have partially failed. To this end, we devise and present a heuristic strategy that varies the provisioning of services according to their predicted performance. Using simulation, we then benchmark our algorithm and show that it leads to a 700% improvement in average utility, while successfully completing up to eight times as many workflows as approaches that do not consider service failures

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

    Get PDF
    © 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

    Research on failure free systems, Phase II FINAL report

    Get PDF
    Technique for employing redundant equipment to increase reliability of electronic digital system
    corecore