Dynamic slack reclamation with procrastination scheduling in real-time embedded systems

Jejurikar, Ravindra; Gupta, Ratneshwer

doi:10.1109/dac.2005.193783

Cited by 36 publications

(76 citation statements)

References 7 publications

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“…However, when QoS constraints are imposed on the system, the utilization based condition in Jejurikar et al (2005) to compute the procrastination interval Z i is not applicable to our system model. Fortunately, with Y * i computed in the previous section, the future mandatory jobs can be procrastinated easily, which is stated in the following theorem (the proof is provided in Appendix C):…”

Section: Execution Of the Mandatory Jobsmentioning

confidence: 98%

“…In Jejurikar et al (2005), the slack time can be reclaimed to facilitate the dynamic task procrastination. The main idea is: assuming at time t, the processor is in sleeping state and the mandatory jobs J i arrives with an amount of available slack S i (t) with priorities higher than or equal to it, then the maximal between S i (t) and its static procrastination interval Z i can be used to procrastinate the execution of J i .…”

Section: Dynamic Procrastinationmentioning

confidence: 99%

“…The task set (τ 1 = (10, 10, 2, 1, 1); τ 2 = (40, 40, 28, 1, 1); τ 3 = (40, 40, 4, 1, 1)) schedule with actual execution times and slack times; (b) procrastinating the future jobs with the maximal available slack time will cause job J 12 to miss its deadline; (c) the idle intervals generated with the procrastination algorithm in Jejurikar et al (2005); (d) all the idles interval generated with our procrastination Algorithm 1 can be shut down and no mandatory job in E + will miss its deadline.…”

Section: Fig 5 (A)mentioning

confidence: 99%

“…6.2 Delaying the mandatory jobs-static procrastination Jejurikar et al (2005) proposed a procrastination scheduling algorithm (see Jejurikar et al 2005 for details) to delay the execution of jobs for hard real-time systems. However, when QoS constraints are imposed on the system, the utilization based condition in Jejurikar et al (2005) to compute the procrastination interval Z i is not applicable to our system model.…”

Section: Execution Of the Mandatory Jobsmentioning

confidence: 99%

“…The energy saving achievable solely via voltage scaling is becoming severely limited. To obtain the maximal overall energy reduction, some researchers have also proposed more advanced scheduling techniques (Jejurikar et al 2005;Zhu and Mueller 2007;Chen and Kuo 2007;Niu and Quan 2004;Yuan and Qu 2007;Thiele 2008, 2010;Quan and Zhang 2009) to reduce dynamic power and leakage power simultaneously. Most of them have targeted the hard real-time systems, i.e., the systems requiring that all the task instances meet their deadlines.…”

Section: Introductionmentioning

confidence: 98%

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Energy efficient scheduling for real-time embedded systems with QoS guarantee

Niu

2011

Real-Time Syst

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While the dynamic voltage scaling (DVS) techniques are efficient in reducing the dynamic energy consumption for the processor, varying voltage alone becomes less effective for the overall energy reduction as the static power is growing rapidly. On the other hand, Quality of Service (QoS) is also a primary concern in the development of today's pervasive computing systems. In this paper, we propose a dynamic approach to minimize the overall energy consumption for soft real-time systems while ensuring the QoS-guarantee. The QoS requirements are deterministically quantified with the window-constraints, which require that at least m out of each non-overlapped window of k consecutive jobs of a task meet their deadlines. Necessary and sufficient conditions for checking the feasibility of task sets with arbitrary service times and periods are developed to ensure that the window-constraints can be guaranteed in the worst case. And efficient scheduling techniques based on pattern variation and dynamic slack reclaiming extensions are proposed to combine the task procrastination and dynamic slowdown to minimize the energy consumption. In contrast to the previous leakage-aware dynamic reclaiming work which never scales the job speed below the critical speed, we will show that it can be more energy efficient to reclaim the slack with speed lower than the critical speed when necessary. Through extensive simulations, our experiment results demonstrate that the proposed techniques significantly outperformed the previous research in both overall and idle energy reduction.

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Section: Execution Of the Mandatory Jobsmentioning

confidence: 98%

Section: Dynamic Procrastinationmentioning

confidence: 99%

Section: Fig 5 (A)mentioning

confidence: 99%

Section: Execution Of the Mandatory Jobsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

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Energy efficient scheduling for real-time embedded systems with QoS guarantee

Niu

2011

Real-Time Syst

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Energy-Aware Scheduling with Dynamic Voltage Scaling

Wang

Mishra

Ranka

2012

Dynamic Reconfiguration in Real-Time Systems

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System and Application Scenarios for Disaster Management Processes, the Rainfall-Runoff Model Case Study

Portero

Kuchař

Vavrik

et al. 2014

Computer Information Systems and Industrial Management

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Part 5: Industrial Management and Other ApplicationsInternational audienceIn the future, the silicon technology will continue to reduce following the Moore’s law. Device variability is going to increase due to a loss in controllability during silicon chip fabrication. Then, the mean time between failures is also going to decrease. The current methodologies based on error detection and thread re-execution (roll back) can not be enough, when the number of errors increases and arrives to a specific threshold. This dynamic scenario can be very negative if we are executing programs in HPC systems where a correct, accurate and time constrained solution is expected. The objective of this paper is to describe and analyse the needs and constraints of different applications studied in disaster management processes. These applications fall mainly in the domains of the High Performance Computing (HPC). Even if this domain can have differences in terms of computation needs, system form factor and power consumption, it nevertheless shares some commonalities

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Dynamic slack reclamation with procrastination scheduling in real-time embedded systems

Cited by 36 publications

References 7 publications

Energy efficient scheduling for real-time embedded systems with QoS guarantee

Energy efficient scheduling for real-time embedded systems with QoS guarantee

Energy-Aware Scheduling with Dynamic Voltage Scaling

System and Application Scenarios for Disaster Management Processes, the Rainfall-Runoff Model Case Study

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