Hamid Reza Faragardi is a PhD student at Mälardalen University. He received his BSc degree in Computer Engineering in 2010, followed by a MSc in Computer Engineering from University of Tehran in 2012. Hamid is a member of the Complex Real-Time Embedded Systems (CORE) at Mälardalen Real-Time Research Center (MRTC).
Hamid's research look into the intersection of resource efficiency and predictability of embedded multi-core real-time systems. A particular focus has been on mapping of hard real-time Autosar runnables on multi-core systems.
A Time-Predictable Fog-Integrated Cloud Framework: One Step Forward in the Deployment of a Smart Factory (May 2018) Hamid Reza Faragardi, Saed Dehnavi , Mehdi Kargahi , Alessandro Papadopoulos, Thomas Nolte CSI International Symposium on Real-Time and Embedded Systems and Technologies (REST'18)
An Energy-Aware Time-Predictable Cloud Data Center (Mar 2018) Hamid Reza Faragardi, Saed Dehnavi , Thomas Nolte, Mehdi Kargahi Special Issue Meta-heuristics in Cloud Computing (SPE)
A Resource Efficient Framework to Run Automotive Embedded Software on Multi-core ECUs (Jan 2018) Hamid Reza Faragardi, Björn Lisper, Kristian Sandström, Thomas Nolte Journal of Systems and Software (JSS)
A Cost Efficient Design of a Multi-Sink Multi-Controller WSN in a Smart Factory (Dec 2017) Hamid Reza Faragardi, Hossein Fotouhi, Thomas Nolte, Rahim Rahmani The 19th IEEE International Conference on Hight Performance and Communications (HPCC'17)
EAICA: An Energy-aware Resource Provisioning Algorithm for Real-Time Cloud Services (Sep 2016) Hamid Reza Faragardi, Aboozar Rajabi , Kristian Sandström, Thomas Nolte 21st IEEE Conference on Emerging Technologies and Factory Automation (ETFA'16)
Towards Energy-Aware Placement of Real-Time Virtual Machines in a Cloud Data Center (Aug 2015) Nima Khalilzad, Hamid Reza Faragardi, Thomas Nolte IEEE International Symposium on High Performance and Smart Computing (HPSC'15)
|Cloud and the Industrial Internet of Things Initiative||active|
|Energy-aware and environment-friendly Cloud computing systems||available|
|Software Partitioning and Synthetic Load Generation Framework for Multicore in Industrial Control Systems||selected|