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Energy-efficient scheduling for hybrid tasks in control devices for the Internet of Things.

Gao Z, Wu Y, Dai G, Xia H - Sensors (Basel) (2012)

Bottom Line: Dynamic voltage scaling (DVS) has been proved to be an effective method for reducing the energy consumption of processors.HoW describes the structure of HRCTs and SRTs, and their properties, e.g., deadlines, execution time, preemption properties, and energy-saving goals, etc.HTDVS first sets the slowdown factors of subtasks while meeting the different real-time requirements of HRCTs and SRTs, and then dynamically reclaims, reserves, and reuses the slack time of the subtasks to meet their ideal energy-saving goals.

View Article: PubMed Central - PubMed

Affiliation: College of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China. gaozhigang@zju.edu.cn

ABSTRACT
In control devices for the Internet of Things (IoT), energy is one of the critical restriction factors. Dynamic voltage scaling (DVS) has been proved to be an effective method for reducing the energy consumption of processors. This paper proposes an energy-efficient scheduling algorithm for IoT control devices with hard real-time control tasks (HRCTs) and soft real-time tasks (SRTs). The main contribution of this paper includes two parts. First, it builds the Hybrid tasks with multi-subtasks of different function Weight (HoW) task model for IoT control devices. HoW describes the structure of HRCTs and SRTs, and their properties, e.g., deadlines, execution time, preemption properties, and energy-saving goals, etc. Second, it presents the Hybrid Tasks' Dynamic Voltage Scaling (HTDVS) algorithm. HTDVS first sets the slowdown factors of subtasks while meeting the different real-time requirements of HRCTs and SRTs, and then dynamically reclaims, reserves, and reuses the slack time of the subtasks to meet their ideal energy-saving goals. Experimental results show HTDVS can reduce energy consumption about 10%-80% while meeting the real-time requirements of HRCTs, HRCTs help to reduce the deadline miss ratio (DMR) of systems, and HTDVS has comparable performance with the greedy algorithm and is more favorable to keep the subtasks' ideal speeds.

No MeSH data available.


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DMR of SRTs at different task type ratio.
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f5-sensors-12-11334: DMR of SRTs at different task type ratio.

Mentions: In this experiment, after generating the task set, we randomly changed the properties of task type and made the HRCTs have the following ratio: 0, 33%, 50%, 66%, and 100%, corresponding to T0, T33, T50, T66 and T100 respectively. Under each utilization, we measured the energy consumption and the DMR of SRTs (deadline-missing task number of SRTs/total number of SRTs) at different HRCT ratio when using the HTDVS algorithm. The experiment results are shown in Figures 4 and 5 respectively.


Energy-efficient scheduling for hybrid tasks in control devices for the Internet of Things.

Gao Z, Wu Y, Dai G, Xia H - Sensors (Basel) (2012)

DMR of SRTs at different task type ratio.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472887&req=5

f5-sensors-12-11334: DMR of SRTs at different task type ratio.
Mentions: In this experiment, after generating the task set, we randomly changed the properties of task type and made the HRCTs have the following ratio: 0, 33%, 50%, 66%, and 100%, corresponding to T0, T33, T50, T66 and T100 respectively. Under each utilization, we measured the energy consumption and the DMR of SRTs (deadline-missing task number of SRTs/total number of SRTs) at different HRCT ratio when using the HTDVS algorithm. The experiment results are shown in Figures 4 and 5 respectively.

Bottom Line: Dynamic voltage scaling (DVS) has been proved to be an effective method for reducing the energy consumption of processors.HoW describes the structure of HRCTs and SRTs, and their properties, e.g., deadlines, execution time, preemption properties, and energy-saving goals, etc.HTDVS first sets the slowdown factors of subtasks while meeting the different real-time requirements of HRCTs and SRTs, and then dynamically reclaims, reserves, and reuses the slack time of the subtasks to meet their ideal energy-saving goals.

View Article: PubMed Central - PubMed

Affiliation: College of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China. gaozhigang@zju.edu.cn

ABSTRACT
In control devices for the Internet of Things (IoT), energy is one of the critical restriction factors. Dynamic voltage scaling (DVS) has been proved to be an effective method for reducing the energy consumption of processors. This paper proposes an energy-efficient scheduling algorithm for IoT control devices with hard real-time control tasks (HRCTs) and soft real-time tasks (SRTs). The main contribution of this paper includes two parts. First, it builds the Hybrid tasks with multi-subtasks of different function Weight (HoW) task model for IoT control devices. HoW describes the structure of HRCTs and SRTs, and their properties, e.g., deadlines, execution time, preemption properties, and energy-saving goals, etc. Second, it presents the Hybrid Tasks' Dynamic Voltage Scaling (HTDVS) algorithm. HTDVS first sets the slowdown factors of subtasks while meeting the different real-time requirements of HRCTs and SRTs, and then dynamically reclaims, reserves, and reuses the slack time of the subtasks to meet their ideal energy-saving goals. Experimental results show HTDVS can reduce energy consumption about 10%-80% while meeting the real-time requirements of HRCTs, HRCTs help to reduce the deadline miss ratio (DMR) of systems, and HTDVS has comparable performance with the greedy algorithm and is more favorable to keep the subtasks' ideal speeds.

No MeSH data available.


Related in: MedlinePlus