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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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Process of assigning tasks' speeds.
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f2-sensors-12-11334: Process of assigning tasks' speeds.

Mentions: Aiming at the energy-saving problem under the HoW task model, we present an energy-saving scheduling algorithm—the HTDVS algorithm. The HTDVS algorithm includes two phases, as shown in Figure 2. During design phase, it first calculates the Time Scaling Factors for a Single task/subtask Except Non-preemptive subtasks (TSFS-EN) factors of tasks (TSFS-EN Calculation) from a given task set TS. After that, it sets the slowdown factors of subtasks using hierarchical method (Slowdown Factor Setting). Finally, it marks the slack time restriction points of tasks and the reserved time requirements (Mark Jump Points and Reserved Slack Time). During the running phase, it reclaims and reuses the slack time of runtime subtasks according to the energy-saving goals of subtasks, dynamically sets the voltages of subtasks in order to save energy further, and sets the self-suspension time of HRCTs in order to reduce the response time jitters.


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)

Process of assigning tasks' speeds.
© Copyright Policy
Related In: Results  -  Collection

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

f2-sensors-12-11334: Process of assigning tasks' speeds.
Mentions: Aiming at the energy-saving problem under the HoW task model, we present an energy-saving scheduling algorithm—the HTDVS algorithm. The HTDVS algorithm includes two phases, as shown in Figure 2. During design phase, it first calculates the Time Scaling Factors for a Single task/subtask Except Non-preemptive subtasks (TSFS-EN) factors of tasks (TSFS-EN Calculation) from a given task set TS. After that, it sets the slowdown factors of subtasks using hierarchical method (Slowdown Factor Setting). Finally, it marks the slack time restriction points of tasks and the reserved time requirements (Mark Jump Points and Reserved Slack Time). During the running phase, it reclaims and reuses the slack time of runtime subtasks according to the energy-saving goals of subtasks, dynamically sets the voltages of subtasks in order to save energy further, and sets the self-suspension time of HRCTs in order to reduce the response time jitters.

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