The fact that various jobs have different quality standards is a major problem with scheduling algorithms. Only a small number of algorithms are created to satisfy these various demands. For multivariate situations, n...
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This paper introduces a novel CPU scheduling algorithm for uniprocessor systems that employs a probabilistic function to enhance fair resource allocation. Unlike traditional algorithms, our approach specifically tackl...
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With the rapid development of the new power system, users tend to diversify the form of service demand, and at the same time, the voice communication system for power dispatching puts forward the requirements of more ...
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In today's world, cloud computing plays a significant role in the development of an effective computing paradigm that adds more benefits to the modern Internet of Things (IoT) frameworks. However, cloud resources ...
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In today's world, cloud computing plays a significant role in the development of an effective computing paradigm that adds more benefits to the modern Internet of Things (IoT) frameworks. However, cloud resources are considered to be dynamic and the demands necessitated for resource allocation for a certain task are different. These diverse factors may cause load and power imbalance which also affect the resource utilization and task scheduling in the cloud-based IoT environment. Recently, a bio-inspired algorithm can work effectually to solve task scheduling problems in the cloud-based IoT system. Therefore, this work focuses on efficient task scheduling and resource allocation through a novel Hybrid Bio-Inspired algorithm with the hybridized of Improvised Particle Swarm Optimization and Ant Colony Optimization. The vital objective of hybridizing these two approaches is to determine the nearest multiple sources to attain discrete and continuous solutions. Here, the task has been allocated to the virtual machine through a particle swarm and continuous resource management can be carried out by an ant colony. The performance of the proposed approach has been evaluated using the CloudSim simulator. The simulation results manifest that the proposed Hybridized algorithm efficiently scheduling the task in the cloud-based IoT environment with a lesser average response time of 2.18 sec and average waiting time of 3.6 sec as compared with existing state-of-the-art algorithms.
With the increasing growth of the Internet of Things (IoT), WBAN (wireless body area network), as a kind of IoT, has attracted much attention. However, due to the problems of existing MAC protocols like poor flexibili...
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The article deals with the subject of production scheduling in 3D printing systems. In the era of incremental technologies gaining in popularity, the creation of printer farms, it becomes very important to effectively...
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Given an instance of a scheduling problem where we want to start executing jobs as soon as possible, it is advantageous if a scheduling algorithm emits the first parts of its solution early, in particular before the a...
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scheduling Auction is a VCG auction, combined with scheduling, utilized where the number of and the kind of auctioned goods have yet to be determined. In its winner determination problem, not only the winners of the g...
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Distributed load management systems can become a crucial enabler for the widespread adoption of electric vehicles (EVs). The present paper experimentally demonstrates a priority-based scheduling algorithm that enables...
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The incorporation of photovoltaic energy storage systems has introduced new avenues for enhancing household energy supply, thereby enabling a greater integration of re-newable energy sources within residential power c...
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