Energy harvesting earliest deadline first scheduling algorithm for increasing lifetime of real time systems

Kumar, Arvind; Alam, Bashir

doi:10.11591/ijece.v9i1.pp539-545

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…Figure 4 shows the simulation result for static EDF using SimSo. The scheduler utilized 100% of CPU time compared to the standard EDF scheduler [23], as shown in Figure 5. All task is executed at 0.67 processor speed which is the lowest speed set for the simulation.…”

Section: Resultsmentioning

confidence: 99%

Dynamic frequency scheduling for CubeSat's on-board and data handling subsystem

Sabri

Ahmad

Sahibuddin

et al. 2021

IJEECS

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<p>CubeSat is a small-sized satellite that provides a cheaper option for the manufacturer to have a fully operational satellite. Due to its size, CubeSat can only generate limited power, and this will restrict its functionality. This research aims to improve CubeSat’s power consumption by implementing the dynamic voltage and frequency scaling (DVFS) technique to on-board and data handling subsystem (OBDH). DVFS will find the best operating frequency to execute all of OBDH’s task. This paper explains how we determined the task set, representing all routine tasks performed by OBDH during normal operation mode. We have simulated the task set using two DVFS algorithms, static earliest deadline first (EDF) and cycle conserving edf (CC EDF). The result shows that both scheduling algorithms give a similar result to our task set. However, when the scheduler is configured as non-preemptive, the simulator failed to schedule the critical task. It means that the system fails to work as intended. Therefore, we conclude that we need to implement mixed-criticality scheduling to prevent critical tasks from being aborted by the system.</p>

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Section: Resultsmentioning

confidence: 99%

Dynamic frequency scheduling for CubeSat's on-board and data handling subsystem

Sabri

Ahmad

Sahibuddin

et al. 2021

IJEECS

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show abstract

“…2) Time-based algorithms: EDF; This is a scheduling algorithm used in buffer scheduling and real-time systems. EDF refers to a mechanism where data packets are scheduled for transmission or processing based on their respective deadlines [50]. In the realm of buffer scheduling, EDF ensures that packets or tasks with imminent deadlines are processed ahead of those with later deadlines, that is, the packet or task with the earliest deadline is given priority and transmitted or executed first.…”

Section: Novel Simulation Environment Setupmentioning

confidence: 99%

Combining Software-Defined and Delay-Tolerant Networking Concepts With Deep Reinforcement Learning Technology to Enhance Vehicular Networks

Nakayima,

Soliman,

Ueda

et al. 2024

IEEE Open J. Veh. Technol.

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Ensuring reliable data transmission in all Vehicular Ad-hoc Network (VANET) segments is paramount in modern vehicular communications. Vehicular operations face unpredictable network conditions which affect routing protocol adaptiveness. Several solutions have addressed those challenges, but each has noted shortcomings. This work proposes a centralised-controller multi-agent (CCMA) algorithm based on Software-Defined Networking (SDN) and Delay-Tolerant Networking (DTN) principles, to enhance VANET performance using Reinforcement Learning (RL). This algorithm is trained and validated with a simulation environment modelling the network nodes, routing protocols and buffer schedules. It optimally deploys DTN routing protocols (Spray and Wait, Epidemic, and PRoPHETv2) and buffer schedules (Random, Defer, Earliest Deadline First, First In First Out, Large/smallest bundle first) based on network state information (that is; traffic pattern, buffer size variance, node and link uptime, bundle Time To Live (TTL), link loss and capacity). These are implemented in three environment types; Advanced Technological Regions, Limited Resource Regions and Opportunistic Communication Regions. The study assesses the performance of the multi-protocol approach using metrics: TTL, buffer management,link quality, delivery ratio, Latency and overhead scores for optimal network performance. Comparative analysis with single-protocol VANETs (simulated using the Opportunistic Network Environment (ONE)), demonstrate an improved performance of the proposed algorithm in all VANET scenarios.

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“…The studies rely on algorithms that demonstrate their efficiency in task scheduling. For reducing energy consumption and making a whole system live longer, Kumar and Alam [3] proposed a novel scheduling algorithm based on dynamic voltage scaling (DVS) and earliest deadline first (EDF) algorithm. Ouhame and Yadi [4] proposed a new technique to improve the data allocation system in virtual machine (VM) for cloud computing based on a modified grey wolf optimization algorithm.…”

Section: Int J Elec and Comp Eng Issn: 2088-8708mentioning

confidence: 99%

An efficient cloudlet scheduling via bin packing in cloud computing

Chraibi

Alla

Ezzati

2022

IJECE

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<p>In this ever-developing technological world, one way to manage and deliver services is through cloud computing, a massive web of heterogenous autonomous systems that comprise adaptable computational design. Cloud computing can be improved through task scheduling, albeit it being the most challenging aspect to be improved. Better task scheduling can improve response time, reduce power consumption and processing time, enhance makespan and throughput, and increase profit by reducing operating costs and raising the system reliability. This study aims to improve job scheduling by transferring the job scheduling problem into a bin packing problem. Three modifies implementations of bin packing algorithms were proposed to be used for task scheduling (MBPTS) based on the minimisation of makespan. The results, which were based on the open-source simulator CloudSim, demonstrated that the proposed MBPTS was adequate to optimise balance results, reduce waiting time and makespan, and improve the utilisation of the resource in comparison to the current scheduling algorithms such as the particle swarm optimisation (PSO) and first come first serve (FCFS).</p>

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Energy harvesting earliest deadline first scheduling algorithm for increasing lifetime of real time systems

Cited by 5 publications

References 17 publications

Dynamic frequency scheduling for CubeSat's on-board and data handling subsystem

Dynamic frequency scheduling for CubeSat's on-board and data handling subsystem

Combining Software-Defined and Delay-Tolerant Networking Concepts With Deep Reinforcement Learning Technology to Enhance Vehicular Networks

An efficient cloudlet scheduling via bin packing in cloud computing

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