Mariam Kiran

Mariam Kiran
Research Scientist
Advanced Network Technologies Group

Biographical Sketch

Mariam’s research focuses on learning and decentralized optimization of system architectures and algorithms for high performance computing, underlying networks and Cloud infrastructures. She has been exploring various platforms such as HPC grids, GPUs, Cloud and SDN-related technologies. Her work involves optimization of QoS, performance using parallelization algorithms and software engineering principles to solve complex data intensive problems such as large-scale complex simulations. Over the years, she has been working with biologists, economists, social scientists, building tools and performing optimization of architectures for multiple problems in their domain.

Mariam joins ESnet as a Research Scientist, working on intent-based networking and engineering intelligent networks for optimizing performance and user experience. Before coming to Berkeley Lab, she worked as an Associate Professor at University of Bradford (UK), focusing on Software engineering and building her Cloud Computing research group, looking at infrastructure-related issues particularly exploring Openstack, AWS and Azure, building an in-house cloud for experimentation called BradStack. Previous to this, she held Postdoctoral research positions at University of Leeds and Sheffield working on HPC and MultiCloud optimization problems. Her work has led to multiple publications in the area of optimizing agent-based simulations over HPC and Cloud, building virtual platforms, Internet of things (IoT) and been involved in multiple EU-research projects and initiatives. She finished her PhD in Computer Science and MSc (Eng) in Software Engineering from University of Sheffield (UK) in 2010 and 2007 respectively.

Please visit Google Scholar for an updated list of publications.

More research project details here: HomePage.  

Current Research

Intent-based networking

Machine learning and applications in High Performance Networks

Decentralized deep learning


Agent-based modelling platform which allows users to create decentralized models in various fields such as biology, social networks and economics. FLAME allows programming of intelligent agents to study how complex systems evolve when decentralized learning and game theory principles apply. FLAME automates parallelization of agent code using MPI for HPC and GPU tools.

Cloud computing software which allows building clouds that can provision on demand resources based on trust, risk, energy and cost demands. Optimis was part of an EU-funded FP7 project and released software tools via Atos (Spain) and BT (UK).

  • BradStack

Cloud computing project to investigate building clouds from ground using minimum hardware resources and experimenting with Openstack solutions. This project was an internal research group project to allow PhD students to understand how clouds exist and test out their fault tolerance and performance modelling tools on a in-house cloud infrastructure.

Journal Articles

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,

Conference Papers

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,

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,

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

M Kiran, “Platform dependency and cloud use for ABM, Satellite Workshop, Computational Transparency in Modeling Complex Systems,”, Conference on Complex Systems, Arizona, USA, 2015., September 5, 2015,

P Yadav, M Kiran, A Bennaceur, L Georgieva, M Salama and A E Cano, “Impact of Gender Diversity and Equality Initiatives”, WomENcourage 2015 Conference, Uppsala, Sweden, October, 2015, September 1, 2015,

M Kiran, S Konur, M Burkitt, “PlatOpen Platform Dependency for Open ABM Complex Model Simulations, Satellite Workshop,”, Conference on Complex Systems, Arizona, USA, 2015., September 1, 2015,

Mariam Kiran, Kabiru Maiyama, Haroon Mir, Bashir Mohammad, Ashraf Al Oun, “Agent-Based Modelling as a Service on Amazon EC2: Opportunities and Challenges”, Utility and Cloud Computing (UCC), 2015 IEEE/ACM 8th International Conference on, September 1, 2015,

S Konur, M Kiran, M Gheorghe, M Burkitt, F Ipate,, “Agent-based high-performance simulation of biological systems on the GPU,”, High Performance Computing and Communications, IEEE, 2015, May 1, 2015,

A Al-Ou’n, M Kiran, DD Kouvatsos, “Using Agent-Based VM Placement Policy,”, Future Internet of Things and Cloud (FiCloud), Rome, Italy, August, 2015, April 1, 2015,

B Mohammed, M Kiran, “Analysis of Cloud Test Beds Using OpenSource Solutions,”, Future Internet of Things and Cloud (FiCloud), Rome, Italy, August, 2015, April 1, 2015,

Mariam Kiran, Anthony JH Simons, “Model-Based Testing for Composite Web Services in Cloud Brokerage Scenarios”, Advances in Service-Oriented and Cloud Computing, ESOCC, 2014, September 1, 2014,

M Kiran, A Friesen, A J H Simons and W K R Schwach, “Model-based Testing in Cloud Brokerage Scenarios,”, Proc. 1st. Int. Workshop on Cloud Service Brokerage. Service-Oriented Computing, ICSOC 2013 Workshops, LNCS 8377, 2014, September 1, 2014,

U Khan, M Oriol, M Kiran, “Threat methodology for securing scalable video in the Cloud”, Internet Technology and Secured Transactions (ICITST), 2013 8th Int. Conf., Pages 428-436, IEEE, 2013, September 1, 2013,

T Kirkham, K Djemame, M Kiran, M Jiang, G Vafiadis, A Evangelinou, “Risk based SLA management in clouds: A legal perspective,”, Internet Technology and Secured Transactions, 2012, September 1, 2012,

M.Kiran, A.U.Khan, M.Jiang, K.Djemame, M.Oriol, M.Corrales,, “Managing Security Threats in Clouds”, Digital Research 2012, September 1, 2012,

A.U.Khan, M.Kiran, M.Oriol, M.Jiang, K.Djemame, “Security risks and their management in cloud computing”, CloudCom 2012: 121-128, September 1, 2012,

T.Kirkham, D.Armstrong, K.Djemame, M.Corrales, M.Kiran, I.Nwankwo, M.Jiang, N.Forgo, “Assuring Data Privacy in Cloud Transformations,”, In: TrustCom, 2012, September 1, 2012,

K.Djemame, D.Armstrong, M.Kiran, M.Jiang, “A Risk Assessment Framework and Software Toolkit for Cloud Service Ecosystems, Cloud Computing 2011,”, The Second International Conference on Cloud Computing, Grids and Virtualization, pg: 119-126, ISBN: 978-1-61208-153-3, Italy, September 1, 2011,

S.F.Adra, M.Kiran, P.McMinn, N.Walkinshaw, “A multiobjective optimisation approach for the dynamic inference and refinement of agent-based model specifications,”, IEEE Congress on Evolutionary Computation 2011: 2237-2244, New Orleans, USA, January 2, 2011,

M.Kiran, M.Jiang, D.Armstrong and K.Djemame, “Towards a Service Life Cycle-based Methodology for Risk Assessment in Cloud Computing,”, CGC 2011, International conference on Cloud and Green Computing, December, Australia, Proceedings DASC 2011: 449-456, January 2, 2011,

M.Kiran, P.Richmond, M.Holcombe, L.S.Chin, D.Worth and C.Greenough, “FLAME: Simulating Large Populations of Agents on Parallel Hardware Architectures”, AAMAS 2010: 1633-1636, Toronto, Canada, June 1, 2010,


Mariam Kiran, X-Machines for Agent-Based Modeling: FLAME Perspectives, (January 30, 2017)

Book Chapters

Mariam Kiran, “What is Modelling and Simulation: An introduction”, Encyclopedia of Computer Science and Technology, ( December 24, 2015)

Mariam Kiran, “Legal Issues Surrounding Connected Government Services: A Closer Look at G-Clouds”, Cloud Computing Technologies for Connected Government, ( October 24, 2015)

M Kiran, “A methodology for Cloud Security Risks Management”, Cloud Computing, ( October 20, 2015)


M Kiran, Invited Talk Software Engineering challenges in Smart Cities, Optics Group Arizona University, Oct 2015, December 1, 2015,

M Kiran, Platform dependency and cloud use for ABM, CCS Conference, Oct 2015, October 1, 2015,

Handling Data Challenges in the Capacity Crunch, Royal Society London, May 2015, May 1, 2015,

EPSRC Grand Engineering Challenges, EPSRC meeting in defining the engineering challenges for future UK research, November 2014, November 1, 2014,

EC consultation research directions for future European research calls 2015-2016, November 1, 2014,

Mariam Kiran, Concerns for how software is distributed through the Cloud, Consultation on Cloud Computing, EU Digital Agenda for Europe, 2014, November 1, 2014,


M Kiran, M Stanett, “Bitcoin risk analysis, NEMODE Policy Paper”, December 1, 2015,

B Mohammed, M Kiran, “Experimental Report on Setting up a Cloud Computing Environment at the University”, arXiv preprint arXiv:1412.4582 1 2014, June 1, 2014,