Shanika Karunasekera

Deep reinforcement learning for application scheduling in resource-constrained, multi-tenant serverless computing environments

A Mampage, S Karunasekera, R Buyya

Journal article  |  2023  |  Future Generation Computer Systems

Serverless computing has sparked a massive interest in both the cloud service providers and their clientele in recent years. This ..

Dynamic Straggler Mitigation for Large-scale Spatial Simulations

Eman Bin Khunayn, Hairuo Xie, Shanika Karunasekera, Kotagiri Ramamohanarao

Journal article  |  2023  |  ACM Transactions on Spatial Algorithms and Systems

Spatial simulations have been widely used to study real-world environments, such as transportation systems. Applications like pred..

A clustering-based topic model using word networks and word embeddings

W Mu, KH Lim, J Liu, S Karunasekera, L Falzon, A Harwood

Journal article  |  2022  |  Journal of Big Data

Online social networking services like Twitter are frequently used for discussions on numerous topics of interest, which range fro..

Concurrent optimization of safety and traffic flow using deep reinforcement learning for autonomous intersection management

L Muthugama, H Xie, E Tanin, S Karunasekera, U Gunarathna

Conference Proceedings  |  2022  |  GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems

With increasing connectivity and autonomy in traffic eco-systems, Autonomous Intersection Management (AIM) has attracted strong at..

Story points changes in agile iterative development An empirical study and a prediction approach

Jirat Pasuksmit, Patanamon Thongtanunam, Shanika Karunasekera

Journal article  |  2022  |  EMPIRICAL SOFTWARE ENGINEERING

Story Points (SP) are an effort unit that is used to represent the relative effort of a work item. In Agile software development, ..

A simulation study on prioritizing connected freight vehicles at intersections for traffic flow optimization (industrial paper)

H Xie, R Borovica-Gajic, E Tanin, S Karunasekera, U Gunarathna, G Oppy, M Sarvi

Conference Proceedings  |  2022  |  GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems

Due to the importance of road freight, there is a significant cost of delaying freight vehicles on the road. In this work, we focu..

Dynamic graph combinatorial optimization with multi-attention deep reinforcement learning

U Gunarathna, R Borovica-Gajic, S Karunasekera, E Tanin

Conference Proceedings  |  2022  |  GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems

Graph combinatorial optimization (CO) is a widely studied problem with use-cases stemming from many fields. Typically, in real-wor..

e-SMARTS: a system to simulate intelligent traffic management solutions (demo paper)

U Gunarathna, R Borovica-Gajic, S Karunasekera, E Tanin

Conference Proceedings  |  2022  |  GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems

Intelligent traffic management solutions that leverage machine learning have gained a lot of interest in recent years. These techn..

Performance and Cost-Efficient Spark Job Scheduling Based on Deep Reinforcement Learning in Cloud Computing Environments

Muhammed Tawfiqul Islam, Shanika Karunasekera, Rajkumar Buyya

Journal article  |  2022  |  IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS

Big data frameworks such as Spark and Hadoop are widely adopted to run analytics jobs in both research and industry. Cloud offers ..

Noise-Robust Learning from Multiple Unsupervised Sources of Inferred Labels

A Silva, L Luo, S Karunasekera, C Leckie

Journal article  |  2022  |  Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022

Deep Neural Networks (DNNs) generally require large-scale datasets for training. Since manually obtaining clean labels for large d..