activity
20052024
most citedDynamic Scheduling for Stochastic Edge-Cloud Computing Environments using A3C learning and Residual Recurrent Neural Networks

248 citations · 598 across the 22 of their papers we have counts for

collaborators

22 papers

cs.DC2024★ 178 cited

Modern Computing: Vision and Challenges

Sukhpal Singh Gill, Huaming Wu, Panos Patros +22

Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Int…

cs.DC2020

Thermal Prediction for Efficient Energy Management of Clouds using Machine Learning

Shashikant Ilager, Kotagiri Ramamohanarao, Rajkumar Buyya

Thermal management in the hyper-scale cloud data centers is a critical problem. Increased host temperature creates hotspots which significantly increases cooling cost and affects r…

cs.DB2020

Segmented Pairwise Distance for Time Series with Large Discontinuities

Jiabo He, Sarah Erfani, Sudanthi Wijewickrema +2

Time series with large discontinuities are common in many scenarios. However, existing distance-based algorithms (e.g., DTW and its derivative algorithms) may perform poorly in mea…

eess.IV2020★ 1 cited

Learning Non-Unique Segmentation with Reward-Penalty Dice Loss

Jiabo He, Sarah Erfani, Sudanthi Wijewickrema +2

Semantic segmentation is one of the key problems in the field of computer vision, as it enables computer image understanding. However, most research and applications of semantic se…

cs.LG2020★ 248 cited

Dynamic Scheduling for Stochastic Edge-Cloud Computing Environments using A3C learning and Residual Recurrent Neural Networks

Shreshth Tuli, Shashikant Ilager, Kotagiri Ramamohanarao +1

The ubiquitous adoption of Internet-of-Things (IoT) based applications has resulted in the emergence of the Fog computing paradigm, which allows seamlessly harnessing both mobile-e…

cs.LG2020★ 17 cited

MurTree: Optimal Classification Trees via Dynamic Programming and Search

Emir Demirović, Anna Lukina, Emmanuel Hebrard +5

Decision tree learning is a widely used approach in machine learning, favoured in applications that require concise and interpretable models. Heuristic methods are traditionally us…