Multiagent Architecture for Distributed Adaptive Scheduling of Reconfigurable Real-Time Tasks With Energy Harvesting Constraints

Housseyni, Wiem; Mosbahi, Olfa; Khalgui, Mohamed; Li, Zhiwu; Yin, Li

doi:10.1109/access.2017.2781459

Cited by 24 publications

(11 citation statements)

References 34 publications

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“…Furthermore, system has natural parallelism and the overall optimization task should be divided into many subtasks to be executed in different membranes [24], [25]. The task allocation method in this paper can be described in the following way.…”

Section: A Optimization Model and Strategy 1) Some Definitionsmentioning

confidence: 99%

Dynamic Placement Optimization for Bio-Inspired Self-Repairing Hardware

Liu

Xiao

et al. 2020

IEEE Access

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Bio-inspired self-repairing hardware is a distributed self-adaptive system, characterized by powerful fault-tolerant ability and environment adaptivity. However, it suffers from some difficulties such as large resource consumption and degraded circuit performances. From the viewpoint of cybernetics and computer science, the cellular differentiation and substitution process of bio-inspired self-repairing hardware can be converted into dynamic placement problems on a reconfigurable system. Current systems can only generate some predefined fault-free placements from a finite number of initial placements. The aim of this paper was to achieve high-quality placements from arbitrary initial placements. Based on P systems, an analysis has been made on the limitations of current systems and an improved computing system has been developed to achieve the ergodic property. Its computational power has been verified by a constructive proof. Moreover, centering on the problem how to improve the placement quality on a distributed platform, the optimization model, task allocation, optimization strategy, and membrane optimization algorithm have been designed and developed. The optimization performances were verified and the calculation amount was exhibited by experiments. Finally, it indicated by comparison that the proposed approach would reduce the resource consumption and maintain good circuit performances. INDEX TERMS Bio-inspired self-repairing hardware, dynamic placement optimization, P systems, computing model, membrane optimization algorithms.

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Section: A Optimization Model and Strategy 1) Some Definitionsmentioning

confidence: 99%

Dynamic Placement Optimization for Bio-Inspired Self-Repairing Hardware

Liu

Xiao

et al. 2020

IEEE Access

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“…However, they accept tasks without any priority test, which eliminates the highest priority tasks by lowest priority ones. Also, in [32] and [33], the scheduling is performed without any feasibility test in energy, which leads to wasted energy.…”

Section: Related Workmentioning

confidence: 99%

On Feasibility of Multichannel Reconfigurable Wireless Sensor Networks Under Real-Time and Energy Constraints

Aissa

Bachir

Khalgui

et al. 2019

IEEE Trans. Syst. Man Cybern, Syst.

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This paper deals with the medium between two reconfigurable sensor nodes characterized by radio interfaces that support multiple channels for exchanging real-time messages under energy constraints, these constraints are violated if the consumed energy in transmission is higher than the remaining quantity of energy. A reconfiguration, i.e., any addition or removal of tasks in devices and consequently of messages on the medium, can cause the violation of real-time or energy constraints at run-time. To achieve a feasible scheduling in time (i.e., message deadlines will be respected) and energy (i.e., there is available energy) on the medium, we propose new dynamic solutions: Balance, Dilute, and a combination of them to manage any addition or removal of messages. The proposed approach utilizes the energy harvesting techniques and PowerControl algorithm to reduce the nonharvested consumed energy. The proposed strategies achieve significant improvement over existing methods and provide the highest percentage of adding messages, with a lower average in response time and energy consumption. They reach a percentage of success in adding the highest priority messages while meeting deadlines up to 85%. This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.

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“….N ]) can be specified in two forms: (i) In the first form, a task is a black computation software component with a worst case execution time C i , period P i , relative deadline D i , and worst case energy consumption En i , Pre i is the pre-condition to be considered before executing T i , Post i is the pre-condition to be considered after executing T i , Sec i is the security mechanism related to T i [33], and (ii) In the second form, T i is direct acyclic graph G i = (Op i ,Tr i ) such that: (ii.a) Op i is a set of functional operations with precedence constraints to encode the task. We denote by ON i the number of operations encoding A transition tr i,a,b that links two operations o i,a and o i,b in G i is characterized by a firing probability prob i,a,b , an interarrival period p i,a,b which is the portion of time that must expire after executing o i,a and before triggering the operation o i,b [35]. We denote by Cpath i a sequence of operations in the critical path of G i that encodes the biggest execution times of operations.…”

Section: A System Formalizationmentioning

confidence: 99%

“…If we consider a renewable energy source to supply the energy storage, then the incoming power received by the different storage units is supposed a constant along time. Let Ph(t) be the charging rate delivered at time t by the energy source and E(t) be the total delivered energy over [0, t] given by [35]…”

Section: A System Formalizationmentioning

confidence: 99%

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On Reconfiguration Theory of Discrete-Event Systems: From Initial Specification Until Final Deployment

Khalgui

Mosbahi

2019

IEEE Access

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This paper presents an overview on different research activities that we did in the recent decade for developing reconfigurable discrete-event systems (RDESs) from initial high-level specification according to user requirements until final low-level deployment in target hardware components. A reconfiguration is any run-time scenario that adapts the system's behavior to any evolution in the related environment by adding or removing services or also configuring their parameters, i.e., their frequency, execution time, or also their location in the considered hardware architecture. Since the development of distributed RDESs under functional and extra-functional constraints is required by experts, we propose a complete methodology that deals first with their initial design with a new general profile named R-UML extending unified modeling language (UML) or also with new specific technology-oriented profiles, the validation of the related models with a new language R-OCL extending object constraint language (OCL), before their transformation to formal formalisms, such as Petri nets, timed automata, or B method for simulation or also formal verification of different properties. The checked models are transformed into OS reconfigurable tasks in the operational level, before applying a co-design methodology under functional, real-time, memory, and energy constraints for minimizing redundancies in tasks and for optimizing the composition of software and hardware parts together. We describe technology-oriented solutions for the scheduling of distributed RDESs by parameterizing tasks and their exchanged messages between multi-speed networked processors. We finish with the real low-level deployment on target hardware devices before applying useful software and hardware tests for checking the delivered system quality. These contributions, published in different journal and conference papers, are applied to different applications in medicine, wireless sensor networks, transportation systems, manufacturing industry, smart grids and microgrids, embedded technologies, and so on. We find significant gains in terms of the system reactivity and flexibility under related constraints. INDEX TERMS Discrete-event system, modeling and verification, design and validation, implementation and scheduling, real-time and energy aware scheduling, test and simulation, co-design and deployment, application. I. INTRODUCTION Automatic flexibility becomes a required criterion for dynamic system adaptability to any possible evolution in related environment. A reconfigurable system is considered in this paper as a discrete-event system (RDES) that adapts its behavior to recover faults or also improve required performance at run-time. It is a set of wired/wireless networked devices that run independent/dependent control software tasks exchanging messages on the network [1], [2]. These tasks and consequently the related messages should be executed under functional, memory, real-time, and energy constraints described in user requirements [3]-[5]. A reconfi...

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Multiagent Architecture for Distributed Adaptive Scheduling of Reconfigurable Real-Time Tasks With Energy Harvesting Constraints

Cited by 24 publications

References 34 publications

Dynamic Placement Optimization for Bio-Inspired Self-Repairing Hardware

Dynamic Placement Optimization for Bio-Inspired Self-Repairing Hardware

On Feasibility of Multichannel Reconfigurable Wireless Sensor Networks Under Real-Time and Energy Constraints

On Reconfiguration Theory of Discrete-Event Systems: From Initial Specification Until Final Deployment

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