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.
Power-aware Allocation of Fault-tolerant Multi-rate AUTOSAR Applications (Dec 2018) Nesredin Mahmud, Guillermo Rodriguez-Navas, Hamid Reza Faragardi, Saad Mubeen, Cristina Seceleanu 25th Asia-Pacific Software Engineering Conference (APSEC'18)
An efficient placement of sinks and SDN controller nodes for optimizing the design cost of industrial IoT systems (Jun 2018) Hamid Reza Faragardi, Maryam Vahabi, Hossein Fotouhi, Thomas Nolte, Thomas Fahringer Special Issue Meta-heuristics in Cloud Computing (SPE)
An analytical model for deploying mobile sinks in industrial Internet of Things (Jun 2018) Maryam Vahabi, Hamid Reza Faragardi, Hossein Fotouhi IEEE Wireless Communications and Networking Conference (WCNC'18)
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)
|Cloud and the Industrial Internet of Things Initiative||active|
|Future factories in the Cloud||active|
|Energy-aware and environment-friendly Cloud computing systems||available|
|Software Partitioning and Synthetic Load Generation Framework for Multicore in Industrial Control Systems||selected|
|Optimizing Energy Consumption of Real-Time Cloud Computing Systems||finished|