5 Search Results for "Saifullah, Abusayeed"

Document
DOI: 10.4230/LIPIcs.ECRTS.2023.8
Precise Scheduling of DAG Tasks with Dynamic Power Management

Authors: Ashikahmed Bhuiyan, Mohammad Pivezhandi, Zhishan Guo, Jing Li, Venkata Prashant Modekurthy, and Abusayeed Saifullah

Published in: LIPIcs, Volume 262, 35th Euromicro Conference on Real-Time Systems (ECRTS 2023)

Abstract
The rigid timing requirement of real-time applications biases the analysis to focus on the worst-case performances. Such a focus cannot provide enough information to optimize the system’s typical resource and energy consumption. In this work, we study the real-time scheduling of parallel tasks on a multi-speed heterogeneous platform while minimizing their typical-case CPU energy consumption. Dynamic power management (DPM) policy is integrated to determine the minimum number of cores required for each task while guaranteeing worst-case execution requirements (under all circumstances). A Hungarian Algorithm-based task partitioning technique is proposed for clustered multi-core platforms, where all cores within the same cluster run at the same speed at any time, while different clusters may run at different speeds. To our knowledge, this is the first work aiming to minimize typical-case CPU energy consumption (while ensuring the worst-case timing correctness for all tasks under any execution condition) through DPM for parallel tasks in a clustered platform. We demonstrate the effectiveness of the proposed approach with existing power management techniques using experimental results and simulations. The experimental results conducted on the Intel Xeon 2680 v3 12-core platform show around 7%-30% additional energy savings.
Cite as

Ashikahmed Bhuiyan, Mohammad Pivezhandi, Zhishan Guo, Jing Li, Venkata Prashant Modekurthy, and Abusayeed Saifullah. Precise Scheduling of DAG Tasks with Dynamic Power Management. In 35th Euromicro Conference on Real-Time Systems (ECRTS 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 262, pp. 8:1-8:24, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2023)

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@InProceedings{bhuiyan_et_al:LIPIcs.ECRTS.2023.8,
  author =	{Bhuiyan, Ashikahmed and Pivezhandi, Mohammad and Guo, Zhishan and Li, Jing and Modekurthy, Venkata Prashant and Saifullah, Abusayeed},
  title =	{{Precise Scheduling of DAG Tasks with Dynamic Power Management}},
  booktitle =	{35th Euromicro Conference on Real-Time Systems (ECRTS 2023)},
  pages =	{8:1--8:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-280-8},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{262},
  editor =	{Papadopoulos, Alessandro V.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2023.8},
  URN =		{urn:nbn:de:0030-drops-180372},
  doi =		{10.4230/LIPIcs.ECRTS.2023.8},
  annote =	{Keywords: Parallel task, mixed-criticality scheduling, energy minimization, dynamic power management, cluster-based platform}
}
Document
DOI: 10.4230/LIPIcs.ECRTS.2020.2
CPU Energy-Aware Parallel Real-Time Scheduling

Authors: Abusayeed Saifullah, Sezana Fahmida, Venkata P. Modekurthy, Nathan Fisher, and Zhishan Guo

Published in: LIPIcs, Volume 165, 32nd Euromicro Conference on Real-Time Systems (ECRTS 2020)

Abstract
Both energy-efficiency and real-time performance are critical requirements in many embedded systems applications such as self-driving car, robotic system, disaster response, and security/safety control. These systems entail a myriad of real-time tasks, where each task itself is a parallel task that can utilize multiple computing units at the same time. Driven by the increasing demand for parallel tasks, multi-core embedded processors are inevitably evolving to many-core. Existing work on real-time parallel tasks mostly focused on real-time scheduling without addressing energy consumption. In this paper, we address hard real-time scheduling of parallel tasks while minimizing their CPU energy consumption on multicore embedded systems. Each task is represented as a directed acyclic graph (DAG) with nodes indicating different threads of execution and edges indicating their dependencies. Our technique is to determine the execution speeds of the nodes of the DAGs to minimize the overall energy consumption while meeting all task deadlines. It incorporates a frequency optimization engine and the dynamic voltage and frequency scaling (DVFS) scheme into the classical real-time scheduling policies (both federated and global) and makes them energy-aware. The contributions of this paper thus include the first energy-aware online federated scheduling and also the first energy-aware global scheduling of DAGs. Evaluation using synthetic workload through simulation shows that our energy-aware real-time scheduling policies can achieve up to 68% energy-saving compared to classical (energy-unaware) policies. We have also performed a proof of concept system evaluation using physical hardware demonstrating the energy efficiency through our proposed approach.
Cite as

Abusayeed Saifullah, Sezana Fahmida, Venkata P. Modekurthy, Nathan Fisher, and Zhishan Guo. CPU Energy-Aware Parallel Real-Time Scheduling. In 32nd Euromicro Conference on Real-Time Systems (ECRTS 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 165, pp. 2:1-2:26, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2020)

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@InProceedings{saifullah_et_al:LIPIcs.ECRTS.2020.2,
  author =	{Saifullah, Abusayeed and Fahmida, Sezana and Modekurthy, Venkata P. and Fisher, Nathan and Guo, Zhishan},
  title =	{{CPU Energy-Aware Parallel Real-Time Scheduling}},
  booktitle =	{32nd Euromicro Conference on Real-Time Systems (ECRTS 2020)},
  pages =	{2:1--2:26},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-152-8},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{165},
  editor =	{V\"{o}lp, Marcus},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2020.2},
  URN =		{urn:nbn:de:0030-drops-123655},
  doi =		{10.4230/LIPIcs.ECRTS.2020.2},
  annote =	{Keywords: Real-time scheduling, multicore, energy-efficiency, embedded systems}
}
Document
DOI: 10.4230/LITES-v006-i001-a005
Elastic Scheduling for Parallel Real-Time Systems

Authors: James Orr, Chris Gill, Kunal Agrawal, Jing Li, and Sanjoy Baruah

Published in: LITES, Volume 6, Issue 1 (2019). Leibniz Transactions on Embedded Systems, Volume 6, Issue 1

Abstract
The elastic task model was introduced by Buttazzo et al.~in order to represent recurrent real-time workloads executing upon uniprocessor platforms that are somewhat flexible with regards to timing constraints.  In this work, we propose an extension of this model and apply it to represent recurrent real-time workloads that exhibit internal parallelism and are executed on multiprocessor platforms. In our proposed extension, the elasticity coefficient - the quantitative measure of a task's elasticity that was introduced in the model proposed by Buttazzo et al. - is interpreted in the same manner as in the original (sequential) model. Hence, system developers who are familiar with the elastic task model in the uniprocessor context may use our more general model as they had previously done, now for real-time tasks whose computational demands require them to utilize more than one processor.
Cite as

James Orr, Chris Gill, Kunal Agrawal, Jing Li, and Sanjoy Baruah. Elastic Scheduling for Parallel Real-Time Systems. In LITES, Volume 6, Issue 1 (2019). Leibniz Transactions on Embedded Systems, Volume 6, Issue 1, pp. 05:1-05:14, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2019)

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@Article{orr_et_al:LITES-v006-i001-a005,
  author =	{Orr, James and Gill, Chris and Agrawal, Kunal and Li, Jing and Baruah, Sanjoy},
  title =	{{Elastic Scheduling for Parallel Real-Time Systems}},
  journal =	{Leibniz Transactions on Embedded Systems},
  pages =	{05:1--05:14},
  ISSN =	{2199-2002},
  year =	{2019},
  volume =	{6},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LITES-v006-i001-a005},
  doi =		{10.4230/LITES-v006-i001-a005},
  annote =	{Keywords: Parallel real-time tasks, multiprocessor federated scheduling, elasticity coefficient}
}
Document
DOI: 10.4230/LIPIcs.ECRTS.2017.22
Energy-Efficient Multi-Core Scheduling for Real-Time DAG Tasks

Authors: Zhishan Guo, Ashikahmed Bhuiyan, Abusayeed Saifullah, Nan Guan, and Haoyi Xiong

Published in: LIPIcs, Volume 76, 29th Euromicro Conference on Real-Time Systems (ECRTS 2017)

Abstract
In this work, we study energy-aware real-time scheduling of a set of sporadic Directed Acyclic Graph (DAG) tasks with implicit deadlines. While meeting all real-time constraints, we try to identify the best task allocation and execution pattern such that the average power consumption of the whole platform is minimized. To the best of our knowledge, this is the first work that addresses the power consumption issue in scheduling multiple DAG tasks on multi-cores and allows intra-task processor sharing. We first adapt the decomposition-based framework for federated scheduling and propose an energy-sub-optimal scheduler. Then we derive an approximation algorithm to identify processors to be merged together for further improvements in energy-efficiency and to prove the bound of the approximation ratio. We perform a simulation study to demonstrate the effectiveness and efficiency of the proposed scheduling. The simulation results show that our algorithms achieve an energy saving of 27% to 41% compared to existing DAG task schedulers.
Cite as

Zhishan Guo, Ashikahmed Bhuiyan, Abusayeed Saifullah, Nan Guan, and Haoyi Xiong. Energy-Efficient Multi-Core Scheduling for Real-Time DAG Tasks. In 29th Euromicro Conference on Real-Time Systems (ECRTS 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 76, pp. 22:1-22:21, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2017)

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@InProceedings{guo_et_al:LIPIcs.ECRTS.2017.22,
  author =	{Guo, Zhishan and Bhuiyan, Ashikahmed and Saifullah, Abusayeed and Guan, Nan and Xiong, Haoyi},
  title =	{{Energy-Efficient Multi-Core Scheduling for Real-Time DAG Tasks}},
  booktitle =	{29th Euromicro Conference on Real-Time Systems (ECRTS 2017)},
  pages =	{22:1--22:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-037-8},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{76},
  editor =	{Bertogna, Marko},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2017.22},
  URN =		{urn:nbn:de:0030-drops-71675},
  doi =		{10.4230/LIPIcs.ECRTS.2017.22},
  annote =	{Keywords: Parallel task, Real-time scheduling, Energy minimization, Convex optimization}
}
Document
DOI: 10.4230/LITES-v003-i001-a004
Optimal Scheduling of Periodic Gang Tasks

Authors: Joël Goossens and Pascal Richard

Published in: LITES, Volume 3, Issue 1 (2016). Leibniz Transactions on Embedded Systems, Volume 3, Issue 1

Abstract
The gang scheduling of parallel implicit-deadline periodic task systems upon identical multiprocessor platforms is considered. In this scheduling problem, parallel tasks use several processors simultaneously. We propose two DPFAIR (deadline partitioning) algorithms that schedule all jobs in every interval of time delimited by two subsequent deadlines. These algorithms define a static schedule pattern that is stretched at run-time in every interval of the DPFAIR schedule. The first algorithm is based on linear programming and is the first one to be proved  optimal for the considered gang scheduling problem. Furthermore, it runs in polynomial time for a fixed number m of processors and an efficient implementation is fully detailed. The second algorithm is an approximation algorithm based on a fixed-priority rule that is competitive under resource augmentation analysis in order to compute an optimal schedule pattern. Precisely, its speedup factor is bounded by (2-1/m). Both algorithms are also evaluated through intensive numerical experiments.
Cite as

Joël Goossens and Pascal Richard. Optimal Scheduling of Periodic Gang Tasks. In LITES, Volume 3, Issue 1 (2016). Leibniz Transactions on Embedded Systems, Volume 3, Issue 1, pp. 04:1-04:18, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2016)

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@Article{goossens_et_al:LITES-v003-i001-a004,
  author =	{Goossens, Jo\"{e}l and Richard, Pascal},
  title =	{{Optimal Scheduling of Periodic Gang Tasks}},
  journal =	{Leibniz Transactions on Embedded Systems},
  pages =	{04:1--04:18},
  ISSN =	{2199-2002},
  year =	{2016},
  volume =	{3},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LITES-v003-i001-a004},
  doi =		{10.4230/LITES-v003-i001-a004},
  annote =	{Keywords: Real-time systems, Scheduling, Parallel tasks}
}
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