Scheduling algorithms to reduce the static energy consumption of real-time systems

Legout, Vincent; Jan, Mathieu; Pautet, Laurent

doi:10.1007/s11241-014-9207-7

Cited by 20 publications

(23 citation statements)

References 43 publications

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“…7,9 During these periods, a processor is said to be in the passive-idle state. The active-idle periods have been exploited by researchers to make the processor switch to low-power states such as standby, dormant, or shutdown.…”

Section: Computation Energy Modelmentioning

confidence: 99%

“…9 The energy consumed during passive-idle states is much lower than that in active-idle state. 9 The energy consumed during passive-idle states is much lower than that in active-idle state.…”

Section: Computation Energy Modelmentioning

confidence: 99%

“…6,7 Dynamic power management (DPM) and dynamic voltage and frequency scaling (DVFS) are 2 widely adopted and efficient techniques to deal with optimum energy efficiency. In former technique, the basic idea is to put the system components (processors, hard drives, network cards, etc) to one of the low-power states for underused time (scheduling slots/idle time slots) to reduce static power consumption, 4,8,9 while, in the latter, processor's operating voltage V and frequency f are scaled down for scheduling slots leading to reduce computation (dynamic) energy consumption. [10][11][12][13] Apart from computation energy, network's communication energy is also important particularly for communication intensive graphs 14,15 and thereby needs to be handled explicitly.…”

Section: Introductionmentioning

confidence: 99%

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Duplication‐controlled static energy‐efficient scheduling on multiprocessor computing system

Kaur

Bansal

2017

Concurrency and Computation

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Energy-efficient scheduling is a step towards meeting green computing requirements. The work in this direction mainly aims at reducing dynamic energy consumption that includes clock gating, cache subbanking, and dynamic voltage and frequency scaling of underlying processors. However, the emergence of fast and compact transistor sizes has exponentially added onto the processor static power consumption, which has not been paid much attention. This article proposes a duplication-controlled static energy-efficient scheduling (C-SEED) algorithm for scheduling precedence constrained applications on parallel computing systems. The C-SEED algorithm couples adaptive threshold-based duplication with system level dynamic power management technique to achieve its objectives. Dynamic power management works by selectively putting the energy-consuming resources to efficient low-power states for idle times to reduce energy consumption. Efficacy of the proposed algorithm is analyzed and compared against other relevant works on the basis of makespan and total energy (dynamic + static + communication)consumption. The extensive simulation results carried over large set of random and regular task graphs show that the proposed C-SEED algorithm has potential to reduce energy consumption as well as makespan.KEYWORDS duplication-based scheduling, dynamic power management, list-based scheduling, multiprocessor scheduling, static power consumption

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Section: Computation Energy Modelmentioning

confidence: 99%

Section: Computation Energy Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Duplication‐controlled static energy‐efficient scheduling on multiprocessor computing system

Kaur

Bansal

2017

Concurrency and Computation

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“…Dynamic Voltage Scaling (DVS) techniques, known as one of the most effective approaches in reducing the power consumption of real-time systems, have been adopted by many previous researches to slow down the system when the workload is low [3]. With DVS techniques, the real-time scheduler with energy constraints should be able to guarantee the timing constraints of all tasks in the system and to select the speed of task execution so that the energy consumption of the system is minimized, or equivalently [4].…”

Section: Introductionmentioning

confidence: 99%

“…They proposed a polynomial time 2-approximation algorithm if the overheads in turning on/off a processor are negligible. Legout et al [3] explored the trade-off between consumption reduction and deadline misses of the low-criticality tasks. They applied a mixed integer linear program to compute schedules that optimize the most efficient low-power states.…”

Section: Introductionmentioning

confidence: 99%

Node Scaling Scheduling of Real-Time Tasks in a Power-Aware Datacenter

Teng

2015

2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium

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Power consumption of datacenters, especially of large-scale systems, has a growing concern nowadays due to economic and ecological reasons. In this paper, we propose a power-aware mechanism based on DVS that takes into account energy conservation of homogenous clusters and schedulability of deadline-constrained tasks. Although power-aware real-time scheduling heuristics have been intensively studied in the past decade years, they lack a theoretical guideline. From a theoretical perspective, we prove the necessary and sufficient conditions to test whether real-time tasks can be scheduled over a homogenous cluster, and deduce a lower bound for power reduction while guaranteeing the deadlines not violated. Besides, we develop the most power-efficient speed which can minimize the energy consumption. This speed is constant, only depending on the type of processor, and partitions an effective zone for speed scaling in a power-aware cluster. From a practical perspective, we propose a Node Scaling (NS) algorithm applying with other existing heuristics, which can further reduce the power consumption by extending the number of nodes and slowing the processor speed without missing any deadline constraints. Case studies are carried on Intel PXA270 processors. The simulation results show NS is effective for power reduction on the basis of EDF-FF.

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Enhancing timeliness and saving power in real-time databases

Kang

2018

Real-Time Syst

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Scheduling algorithms to reduce the static energy consumption of real-time systems

Cited by 20 publications

References 43 publications

Duplication‐controlled static energy‐efficient scheduling on multiprocessor computing system

Duplication‐controlled static energy‐efficient scheduling on multiprocessor computing system

Node Scaling Scheduling of Real-Time Tasks in a Power-Aware Datacenter

Enhancing timeliness and saving power in real-time databases

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