248 citations · 251 across the 5 of their papers we have counts for
7 papers
Energy and Thermal-aware Resource Management of Cloud Data Centres: A Taxonomy and Future Directions
Shashikant Ilager, Rajkumar Buyya
This paper investigates the existing resource management approaches in Cloud Data Centres for energy and thermal efficiency. It identifies the need for integrated computing and coo…
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…
Green-aware Mobile Edge Computing for IoT: Challenges, Solutions and Future Directions
Minxian Xu, Chengxi Gao, Shashikant Ilager +3
The development of Internet of Things (IoT) technology enables the rapid growth of connected smart devices and mobile applications. However, due to the constrained resources and li…
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…
Artificial Intelligence (AI)-Centric Management of Resources in Modern Distributed Computing Systems
Shashikant Ilager, Rajeev Muralidhar, Rajkumar Buyya
Contemporary Distributed Computing Systems (DCS) such as Cloud Data Centres are large scale, complex, heterogeneous, and distributed across multiple networks and geographical bound…
A Data-Driven Frequency Scaling Approach for Deadline-aware Energy Efficient Scheduling on Graphics Processing Units (GPUs)
Shashikant Ilager, Rajeev Muralidhar, Kotagiri Rammohanrao +1
Modern computing paradigms, such as cloud computing, are increasingly adopting GPUs to boost their computing capabilities primarily due to the heterogeneous nature of AI/ML/deep le…