Energy-Aware Task Scheduling Using Hybrid Firefly-BAT (FFABAT) in Big Data

Senthilkumar, M.

doi:10.2478/cait-2018-0031

Cited by 12 publications

(3 citation statements)

References 30 publications

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“…One possible approach is using Hadoop in a MapReduce framework. To this end, Senthilkumar [ 92 ] proposed a hybrid method combining firefly [ 93 ] and bat [ 94 ] algorithms for efficient energy-aware tasks scheduling. This hybrid method performs resource selection in the map phase and task scheduling in the reduce phase.…”

Section: Application Of Si In Iot/iomtmentioning

confidence: 99%

Swarm Intelligence in Internet of Medical Things: A Review

Alizadehsani

Roshanzamir

Izadi

et al. 2023

Sensors

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Continuous advancements of technologies such as machine-to-machine interactions and big data analysis have led to the internet of things (IoT) making information sharing and smart decision-making possible using everyday devices. On the other hand, swarm intelligence (SI) algorithms seek to establish constructive interaction among agents regardless of their intelligence level. In SI algorithms, multiple individuals run simultaneously and possibly in a cooperative manner to address complex nonlinear problems. In this paper, the application of SI algorithms in IoT is investigated with a special focus on the internet of medical things (IoMT). The role of wearable devices in IoMT is briefly reviewed. Existing works on applications of SI in addressing IoMT problems are discussed. Possible problems include disease prediction, data encryption, missing values prediction, resource allocation, network routing, and hardware failure management. Finally, research perspectives and future trends are outlined.

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Section: Application Of Si In Iot/iomtmentioning

confidence: 99%

Swarm Intelligence in Internet of Medical Things: A Review

Alizadehsani

Roshanzamir

Izadi

et al. 2023

Sensors

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“…Additionally, it overcame every drawback associated with both Round robin and Priority Schedulers. After prioritizing the jobs with the aid of the k-means clustering technique, authors in [59] used the MapReduce framework to schedule tasks for big data using Hadoop. Here, the energy optimization is carried out using the FireFly Algorithm (FA) and BAT (FF-BAT) technique.…”

Section: Other Improved Algorithmsmentioning

confidence: 99%

An Overview of Hadoop Job Scheduling Algorithms for Big Data

Zameel

Zengi̇n

2022

Mugla Journal of Science and Technology

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Rapid advancements in Big data systems have occurred over the last several decades. The significant element for attaining high performance is "Job Scheduling" in Big data systems which requires more utmost attention to resolve some challenges of scheduling. To obtain higher performance when processing the big data, proper scheduling is required. Apache Hadoop is most commonly used to manage immense data volumes in an efficient way and also proficient in handling the issues associated with job scheduling. To improve performance of big data systems, we significantly analyzed various Hadoop job scheduling algorithms. To get an overall idea about the scheduling algorithm, this paper presents a rigorous background. This paper made an overview on the fundamental architecture of Hadoop Big data framework, job scheduling and its issues, then reviewed and compared the most important and fundamental Hadoop job scheduling algorithms. In addition, this paper includes a review of other improved algorithms. The primary objective is to present an overview of various scheduling algorithms to improve performance when analyzing big data. This study will also provide appropriate direction in terms of job scheduling algorithm to the researcher according to which characteristics are most significant

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“…More importantly, by not wasting any clock cycles, processor must be used at the highest rate. Such task scheduling should therefore conduct the execution of realtime tasks [1]. Real-time tasks are one in which accuracy depends not only on the precision of the result, but also on the time of completion of the task execution.…”

Section: Introductionmentioning

confidence: 99%

Performance of Dynamic Queue Based Minimal Deadline First Scheduling Algorithm In Multicore System Under Real Time And Non-Real Time Kernel Environment

Shanmugsundaram¹,

Burgul²,

Kumar³

et al. 2019

IJITEE

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Some real-time systems that need to be associated with operating system services with a hard real-time system. Since these real-time systems that need to be extremely responsive to the outside world have no simple and low-cost operating system assistance. This paper deals with the application on a Linux-based operating system of the priority-based preemptive real-time scheduling algorithm that will suffice these firm applications in real-time. Typically, the algorithms regarded for these hard real-time systems are preemptive scheduling based on priorities. Based on the priority, by meeting the deadline, this algorithm can produce a feasible schedule for the dynamic tasks to be performed on the processor. It is feasible to schedule tasks on a processor as long as preemption is permitted and tasks do not compete for resources. In this scheduling algorithm, the task in the running queue that is waiting for the execution will be placed in the priority queue that is ready to execute in the available processor. This algorithm is deployed in the Linux kernel with the patch file and the kernel is built in the multi core system to execute an application

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Energy-Aware Task Scheduling Using Hybrid Firefly-BAT (FFABAT) in Big Data

Abstract: In modern times there is an increasing trend of applications for handling

Cited by 12 publications

References 30 publications

Swarm Intelligence in Internet of Medical Things: A Review

Swarm Intelligence in Internet of Medical Things: A Review

An Overview of Hadoop Job Scheduling Algorithms for Big Data

Performance of Dynamic Queue Based Minimal Deadline First Scheduling Algorithm In Multicore System Under Real Time And Non-Real Time Kernel Environment

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