Mariam Kiran is a research scientist with shared positions with Energy Sciences Network and the Scientific Data Management (SDM) group in Computational Research Division. Her work specifically concentrates on using advanced software and machine learning techniques to advance system architectures, particularly high-speed networks such as DOE networks.
Her current work is exploring reinforcement learning, unsupervised clustering and classification techniques to optimally control distributed network resources, improving high-speed big data transfers for exascale science applications and optimize how current network infrastructure is utilized. Kiran is the recipient of the DOE ASCR Early Career Award in 2017. Before joining LBNL in 2016, Kiran held positions as a lecturer and research fellow at the Universities of Sheffield and Leeds in the UK. She earned her undergrad and PhD degree in software engineering and computer science from the University of Sheffield, UK in 2011.
Please visit Google Scholar for an updated list of publications.
More research project details here: HomePage.
- DAPHNE: Deep and Autonomic High-Performance Networks
- Panorama 360: Performance Data Capture and Analysis for End-to-end Scientific Workflows
M Kiran, A Chhabra, “Understanding flows in high-speed scientific networks: A Netflow data study”, Future Generation Computer Systems, February 1, 2019, 94:72-79,
F Alali, N Hanford, E Pouyoul, R Kettimuthu, M Kiran, B Mack-Crane, “Calibers: A bandwidth calendaring paradigm for science workflows”, Future Generation Computer Systems, December 1, 2018, 89:736-745,
M Gribaudo, M Iacono, Mariam Kiran, “A performance modeling framework for lambda architecture based applications”, Future Generation Computer Systems, November 9, 2018, 86:1032-1041,
M. Gribaudo, M. Iacono, M. Kiran, “A performance modeling framework for lambda architecture based applications”, Future Generation Computer Systems, August 30, 2017,
M Kiran, E Pouyoul, A Mercian, B Tierney, C Guok, I Monga, “Enabling intent to configure scientific networks for high performance demands”, Future Generation Computer Systems, August 2, 2017,
Tatyana Eftonova, Mariam Kiran, Mike Stannett, “Long-term Macroeconomic Dynamics of Competition in the Russian Economy using Agent-based Modelling”, International Journal of System Dynamics Applications (IJSDA) 6 (1), 1-20, 2017, January 1, 2017,
Mariam Kiran, Anthony Simons, “Testing Software Services in Cloud Ecosystems”, International Journal of Cloud Applications and Computing (IJCAC) 6 (1), 42-58 2016, July 1, 2016,
M Kiran, G Katsaros, J Guitart, J L Prieto, “Methodology for Information Management and Data Assessment in Cloud Environments”, International Journal of Grid and High Performance Computing (IJGHPC), 6(4), 46-71, June 1, 2015,
M Kiran, “Modelling Cities as a Collection of TeraSystems–Computational Challenges in Multi-Agent Approach”, Procedia Computer Science 52, 974-979, 2015, June 1, 2015,
K. Djemame, B Barnitzke, M Corrales, M Kiran, M Jiang, D Armstrong, N Forgo, I Nwankwo, “Legal issues in clouds: towards a risk inventory”, Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 371, 1983, The Royal Society, June 1, 2014,
M Holcombe, S Chin, S Cincotti, M Raberto, A Teglio, S Coakley, C Deissenberg, S vander Hoog, C Greenough, H Dawid, M Neugart, S Gemkow, P Harting, M Kiran, D Worth, “Large-scale modelling of economic systems”, Complex Systems 22 (2) 8 2013, June 1, 2013,
M.Holcombe, S.Adra, M.Bicak, S.Chin, S.Coakley, A.I.Graham, J.Green, C.Greenough, D.Jackson, M.Kiran, S.MacNeil, A.Maleki-Dizaji, P.McMinn, M.Pogson, R.Poole, E.Qwarnstrom, F.Ratnieks, M.D.Rolfe, R.Smallwood, T.Sun and D.Worth, “Modelling complex biological systems using an agent-based approach,”, Integrative Biology, 2012, June 1, 2012,
M.Kiran, M.Bicak, S.Maleki-Dizaji, M.Holcombe, “FLAME: A Platform for High Performance Computing of Complex Systems, Applied for Three Case Studies”, Acta Physica Polonica B, Proceedings Supplement, DOI:10.5506/APhysPolBSupp.4.201, PACS numbers: 07.05.Tp, vol 4, no 2, 2011 (Polish Journal), January 1, 2012,
Mariam Kiran, Simon Coakley, Neil Walkinshaw, Phil McMinn, Mike Holcombe, “Validation and discovery from computational biology models”, Biosystems, September 1, 2009,
Amel Bennaceur, Ampaeli Cano, Lilia Georgieva, Mariam Kiran, Maria Salama, Poonam Yadav, “Issues in Gender Diversity and Equality in the UK”, Proceedings of the 1st International Workshop on Gender Equality in Software Engineering, ACM, July 13, 2018,
S Khan, T Yairi, M Kiran, “Towards a Cloud-based Machine Learning for Health Monitoring and Fault Diagnosis”, Asia Pacific Conference of the Prognostics and Health Management Society 2017, August 1, 2017,
A Mercian, M Kiran, E Pouyoul, B Tierney, I Monga, “INDIRA:‘Application Intent’ network assistant to configure SDN-based high performance scientific networks”, Optical Fiber Communication Conference, July 1, 2017,
M Usman, A Iqbal, M Kiran, “A Bandwidth Friendly Architecture for Cloud Gaming”, 31st International Conference on Information Networking (ICOIN 2017), December 1, 2016,
M Kiran, E Pouyoul, A Mercian, B Tierney, C Guok, I Monga, “Enabling Intent to Configure Scientific Networks for High Performance Demands”, 3nd International Workshop on Innovating the Network for Data Intensive Science (INDIS) 2016, SC16., November 10, 2016,
- Download File: indis-final-2016.pdf (pdf: 745 KB)
B Mohammed, M Kiran, IU Awan, KM Maiyama, “Optimising Fault Tolerance in Real-Time Cloud Computing IaaS Environment”, Future Internet of Things and Cloud (FiCloud), 2016 IEEE 4th International, 2016, September 15, 2016,
M Kiran, “Women in HPC: Changing the Face of HPC”, SC15: HPC transforms, 2015, Austin Texas, November 15, 2015,
M Kiran, “Multiple platforms: Issues of porting Agent-Based Simulation from Grids to Graphics cards”, Workshop on Portability Among HPC Architectures for Scientific Applications, SC15: HPC transforms, 2015, Austin Texas., November 15, 2015,
P Yadav, M Kiran, A Bennaceur, L Georgieva, M Salama and A E Cano, “Jack of all Trades versus Master of one”, Grace Hopper 2015 Conference, November, 2015, November 1, 2015,
Mariam Kiran, Peter Murphy, Inder Monga, Jon Dugan, Sartaj Baveja, “Lambda Architecture for Cost-effective Batch and Speed Big Data processing”, First Workshop on Data-Centric Infrastructure for Big Data Science (DIBS), October 29, 2015,
- Download File: DIBS-Final-Paper-2015.pdf (pdf: 532 KB)
This paper presents an implementation of the lambda architecture design pattern to construct a data-handling backend on Amazon EC2, providing high throughput, dense and intense data demand delivered as services, minimizing the cost of the network maintenance. This paper combines ideas from database management, cost models, query management and cloud computing to present a general architecture that could be applied in any given scenario where affordable online data processing of Big Datasets is needed. The results are presented with a case study of processing router sensor data on the current ESnet network data as a working example of the approach. The results showcase a reduction in cost and argue benefits for performing online analysis and anomaly detection for sensor data