Multi-Agent Adaptive Architecture for Flexible Distributed Real-Time Systems

Chniter, Hamza; Li, Yuting; Khalgui, Mohamed; Koubâa, Anis; Li, Zhiwu; Jarray, Fethi

doi:10.1109/access.2018.2825023

Cited by 16 publications

(18 citation statements)

References 40 publications

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“…Such architectures represent an advantage in terms of sharing and synchronizing OS tasks, which directly influences the amount of energy consumed by the system. Some ( [28], [38]) are just based on DVFS tech- nologies which can affect the stability of the processors [8]. This is due to the problem of switching processors between different power consumption modes and scaling the speed of the processor according to the workload in a system.…”

Section: Nomenclature Mmentioning

confidence: 99%

“…This is due to the problem of switching processors between different power consumption modes and scaling the speed of the processor according to the workload in a system. Some studies ( [1], [28]) integrate multi-agent architectures requiring fairly good communications among agents, which can increase computational cost. Thus, some [29]- [32] propose models which are linked to a set of precise criteria to an explicit application framework, well-defined input parameters and predetermined environmental constraints which make its improvement difficult.…”

Section: Nomenclature Mmentioning

confidence: 99%

“…The proposed approach is compared with a set of methods reported in [1], [7], [10], [27], [28], and [38] (Table 1). Some of them are not considered to operate in reconfigurable systems.…”

Section: Nomenclature Mmentioning

confidence: 99%

“…LetV kp andV kp be respectively the normalized frequency and voltage of Core kp [28]. We suppose that task T i is executed at frequency F ikp and voltage V ikp in Core kp .…”

Section: Energy Modelmentioning

confidence: 99%

“…Fig. 7 shows a comparison of the proposed approach with those reported in [1], [27], and [28] based on the energy cost.…”

Section: B Performance Evaluationmentioning

confidence: 99%

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Improved Multi-Core Real-Time Task Scheduling of Reconfigurable Systems With Energy Constraints

et al. 2020

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This paper deals with the scheduling of real-time periodic tasks executed on heterogeneous multicore platforms. Each processor is composed of a set of multi-speed cores with limited energy resources. A reconfigurable system is sensible to unpredictable reconfiguration events from related environment, such as the activation, removal or update of tasks. The problem is to handle feasible reconfiguration scenarios under energy constraints. Since any task can finish execution before achieving its worst-case execution time (WCET), the idea is to distribute this execution on different processor cores for meeting related deadlines and reducing energy consumption. The methodology consists in using lower processor speeds first to consume less energy. If the system is still non-feasible after reconfiguration, then we adjust the task periods as a flexible solution or migrate some of them to the least loaded processors. Accordingly, an integer linear program (ILP) is formulated to encode the execution model that assigns tasks to different cores with optimal energy consumption, thereby realizing energy-efficient computing/green computing. The potency and effectiveness of the proposed approach are rated through simulation studies. By measuring the energy consumption cost, our solution offers better than 11% of gain than recently published methods and improves by 85% the overall number of adjusted periods.

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Section: Nomenclature Mmentioning

confidence: 99%

Section: Nomenclature Mmentioning

confidence: 99%

“…The proposed approach is compared with a set of methods reported in [1], [7], [10], [27], [28], and [38] (Table 1). Some of them are not considered to operate in reconfigurable systems.…”

Section: Nomenclature Mmentioning

confidence: 99%

“…LetV kp andV kp be respectively the normalized frequency and voltage of Core kp [28]. We suppose that task T i is executed at frequency F ikp and voltage V ikp in Core kp .…”

Section: Energy Modelmentioning

confidence: 99%

“…Fig. 7 shows a comparison of the proposed approach with those reported in [1], [27], and [28] based on the energy cost.…”

Section: B Performance Evaluationmentioning

confidence: 99%

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