0% Complete
Home
/
15th International Conference on Computer and Knowledge Engineering
DEW-WIN: A Dynamic Energy-aware Window-based Scheduler for Mixed-criticality Systems
Authors :
Mahin Moradiyan
1
Yasser Sedaghat
2
Pouria Hosseini
3
Yousef Rezazadeh
4
1- Dependable Distributed Embedded Systems (DDEmS) Laboratory, Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran
2- Dependable Distributed Embedded Systems (DDEmS) Laboratory, Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran
3- Dependable Distributed Embedded Systems (DDEmS) Laboratory, Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran
4- Dependable Distributed Embedded Systems (DDEmS) Laboratory, Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran
Keywords :
Mixed-criticality Systems،Scheduling،Energy-aware،Multi-core Systems،DVFS
Abstract :
Mixed-criticality Embedded systems (MCs) consolidate real-time tasks with different levels of criticality onto a shared hardware platform. one of a primary challenge in this domain is the integration of energy-aware techniques without violating the stringent timing guarantees of HI-criticality tasks. Many existing schedulers employ rigid strategies, such as permanently dropping LO-criticality tasks during a system mode switch, which degrades the overall Quality of Service (QoS) and fails to optimize power consumption dynamically. To address these limitations, this paper introduces DEW-WIN, a Dynamic Energy-aware Window-based scheduling framework for multi-core systems. DEW-WIN partitions time into fixed-size windows and adjusts the processor frequency for each window based on the energy profiles of ready tasks, enabling a fine-grained balance between performance and power usage. The algorithm's effectiveness was evaluated through a hybrid approach, combining hardware implementation on an ARM Zynq 7020 board and detailed emulation using the gem5 simulator. The results demonstrate that DEW-WIN delivers significantly improved performance over a classic mixed-criticality scheduling method. In high-pressure scenarios, DEW-WIN achieved schedulability rates as high as 97.5%, whereas the classic method dropped to as low as 35%. Furthermore, DEW-WIN maintained system stability by remaining in the normal operating mode for over 90% of the execution time, showcasing superior system reliability and greater overall energy efficiency.
Papers List
List of archived papers
Density Estimation Helps Adversarial Robustness
Afsaneh Hasanebrahimi - Bahareh Kaviani Baghbaderani - Reshad Hosseini - Ahmad Kalhor
Depression Diagnosis Using Optimization of Nonlinear EEG Features Based on Parametric Learning Tactics
Ali Asadi Zeidabadi - Melika Changizi - Mahdi Zolfagharzadeh Kermani - Sara Bargi Barkouk
A Graph-based Feature Selection using Class-Feature Association Map (CFAM)
Motahare Akhavan - Seyed Mohammad Hossein Hasheminejad
WBT-GAN:Wavelet based Generative Adversarial Network for Texture Synthesis
Sara Saberi moghadam - Reza Azmi - Maral Zarvani
A Comprehensive Approach to SMS Spam Filtering Integrating Embedded and Statistical Features
Shaghayegh Hosseinpour - Mohammad Reza Keyvanpour
Paddy Plant Stress Identification Using Few-Shot Learning Framework
Ervin Gubin Moung - Pavindrah Naidu a/l Narayanasamy Naiidu - Maisarah Mohd Sufian - Valentino Liaw - Ali Farzamnia - Lorita Angeline
Weakly Supervised Convolutional Neural Network for Automatic Gleason Grading of Prostate Cancer
Maryam Kamareh - Mohammad Sadegh Helfroush - Kamran Kazemi
An intelligent linguistic error detection approach to automated diagnosis of Dyslexia disorder in Persian speaking children
Fatemeh Asghari - Mahsa Khorasani - Mohsen Kahani - Seyed Amir Amin Yazdi - Mahdi Arkhodi Ghalenoei
Uncertainty-Aware Deep Ensembles for Confident Customer Churn Prediction with Rejection Option
Fatemeh Moradi - Mehran Tarif - Mohammadhossein Homaei
A Cloud Broker with Gap Analysis Perspective for Scheduling Multi-Workflows Across On-Demand and Reserved Resources
Negin Shafinezhad - Hamidreza Abrishami - Saeid Abrishami
more
Samin Hamayesh - Version 43.7.0