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Mariam Kiran
University of Bradford
Bradford, United Kingdom
 

Mariam Kiran is a lecturer in the Department of Electrical Engineering and Computing at the University of Bradford (UK) and a research fellow at the University of Sheffield. She is a member of the British Computing Society and the Women’s Engineering Society. She joined Bradford after several years of research and teaching at the University of Sheffield and the University of Leeds (UK).

She is actively involved in various research activities in cloud computing; agent-based modeling, particularly using FLAME (an agent-based modeling framework for complex systems); developing testing and verification techniques; using game theory, multi-objective optimization, and agent learning; and studying economic, sociologic, and biologic modeling using various computational techniques. She is particularly focused on web services, brokering models, trust, risk, security, and optimization aspects in the cloud, as well as developing cloud applications. She holds a PhD in co-evolutionary learning and optimization in agents focusing on economic market models.

Kiran has branched out of university research and has applied her ideas to various industry applications, distributing most of her software as open-source. She is involved in research on smart cities, and is a consultant on EU projects for Telsec Corporation. She has been a reviewer for Computing Reviews since 2012.


     

On the robustness of domain-independent planning engines: the impact of poorly-engineered knowledge
Vallati M., Chrpa L.  K-CAP 2019 (Proceedings of the 10th International Conference on Knowledge Capture, Marina Del Rey, CA,  Nov 19-21, 2019) 197-204, 2019. Type: Proceedings

Planning engines are important not only to help us plan for what may fail or succeed in the real world, but also to provide what-if scenarios to build better robust engines. This paper talks about various ideas to help describe engines and their d...

 

 Zooming in on wide-area latencies to a global cloud provider
Jin Y., Renganathan S., Ananthanarayanan G., Jiang J., Padmanabhan V., Schroder M., Calder M., Krishnamurthy A.  SIGCOMM 2019 (Proceedings of the ACM Special Interest Group on Data Communication, Beijing, China,  Aug 19-23, 2019) 104-116, 2019. Type: Proceedings

The authors measure wide area network (WAN) latency from the viewpoint of a large cloud provider, Azure, by tracking the round-trip time (RTT) of transmission control protocol (TCP) connections. Presenting their tool BlameIt, the authors aim to fi...

 

The datacenter as a computer: designing warehouse-scale machines (3rd ed.)
Barroso L., Hölzle U., Ranganathan P., Szefer J.,  Morgan&Claypool Publishers, San Rafael, CA, 2019. 207 pp. Type: Book (978-1-681734-33-0)

As machine learning becomes more popular and more responsible for managing our everyday lives, using it to manage data centers, to control the mammoth tasks engineers have to deal with, is only a natural evolution....

 

Adaptive reallocation of cybersecurity analysts to sensors for balancing risk between sensors
Shah A., Ganesan R., Jajodia S., Cam H.  Service Oriented Computing and Applications 12(2): 123-135, 2018. Type: Article

Using alerts for cybersecurity is one of the most pressing challenges, both in networks and in general within the computing world. Although there has been a surge of monitoring techniques to catch attacks and secure systems, there are always unana...

 

Forward delay-based packet scheduling algorithm for multipath TCP
Le T., Bui L.  Mobile Networks and Applications 23(1): 4-12, 2018. Type: Article

Multipath TCP (MPTCP) allows packets to be transmitted over multiple paths, “and hence utilizes the network resources more effectively than the traditional single-path [transmission control protocol, TCP].” However, even with paralleli...

 
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