7 Papers
18 Citations
Zinea Das is an academic researcher from Indian Institutes of Information Technology. The author has contributed to research in topics: Scheduling (computing) & Multi-core processor. The author has an hindex of 2, co-authored 7 publications.
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Papers
HEARS: A heterogeneous energy-aware real-time scheduler
TL;DR: Experimental results show that the proposed heuristic strategy, HEARS for DVFS enabled energy-aware scheduling of a set of periodic tasks executing on a heterogeneous multicore system having an arbitrary number of core types enables significant improvement in resource utilization.
22
EA-HRT: An Energy-Aware scheduler for Heterogeneous Real-Time systems
Sanjay Moulik,Rishabh Chaudhary,Zinea Das,Arnab Sarkar +3 more
- 01 Jan 2020
TL;DR: Experimental results show that the proposed heuristic strategy, EA-HRT, is not only able to achieve appreciable energy savings with respect to state-of-the-art but also enables significant improvement in resource utilization.
17
SPORTS: A Semi-partitioned Real-Time Scheduler for Heterogeneous Multicore Platforms
Yanshul Sharma,Zinea Das,Sanjay Moulik +2 more
- 18 Dec 2020
TL;DR: This work proposes a two-phase hierarchical resource allocation strategy called SPORTS: A semi-partitioned real-time scheduler for heterogeneous multicore platforms, for scheduling of periodic tasks with bounded number of migrations and context-switches.
2
TA-HRT: A Temperature-Aware Scheduler for Heterogeneous Real-Time Multicore Systems
Yanshul Sharma,Zinea Das,Alok Das,Sanjay Moulik +3 more
- 01 Dec 2020
TL;DR: In this paper, a heuristic strategy named TA-HRT is proposed for temperature-aware scheduling of a set of real-time periodic tasks on a heterogeneous multicore platform, which operates in three stages, namely Deadline Partitioning, Core Clustering and Temperature-Aware Task Scheduling.
SEAMERS: A Semi-partitioned Energy-Aware scheduler for heterogeneous MulticorE Real-time Systems
TL;DR: In this article, a low-overhead heuristic strategy named SEAMERS is proposed for DVFS based energy-aware scheduling for a set of real-time periodic tasks on a heterogeneous multicore platform.