Adaptive Cloud Resource Scheduling Model Based on Improved Ant Colony Algorithm

Qingbin, 聂清彬 Nie; Feng, 潘峰 Pan; Jiacheng, 吴嘉诚 Wu; Yaoqin, 曹耀钦 Cao

doi:10.3788/lop57.010603

Cited by 1 publication

(1 citation statement)

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“… NZanywayingoma & Yang (2017) proposed an improved particle swarm optimization, which can allocate dynamic virtual resources and reduce the total time of task scheduling in the cloud environment. Nie, Pan & Wu (2020) improved the ability of the traditional ant colony algorithm to shorten the task completion time. It searches for the global optimal solution by increasing the load balance adaptive factor and enabling tasks to be assigned to the most appropriate cloud virtual machine.…”

Section: Introductionmentioning

confidence: 99%

A task scheduling algorithm with deadline constraints for distributed clouds in smart cities

Zhou

Liu

Gao

2023

PeerJ Computer Science

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Computing technologies and 5G are helpful for the development of smart cities. Cloud computing has become an essential smart city technology. With artificial intelligence technologies, it can be used to integrate data from various devices, such as sensors and cameras, over the network in a smart city for management of the infrastructure and processing of Internet of Things (IoT) data. Cloud computing platforms provide services to users. Task scheduling in the cloud environment is an important technology to shorten computing time and reduce user cost, and thus has many important applications. Recently, a hierarchical distributed cloud service network model for the smart city has been proposed where distributed (micro) clouds, and core clouds are considered to achieve a better network architecture. Task scheduling in the model has attracted many researchers. In this article, we study a task scheduling problem with deadline constraints in the distributed cloud model and aim to reduce the communication network’s data load and provide low-latency services from the cloud server in the local area, hence promoting the efficiency of cloud computing services for local users. To solve the task scheduling problem efficiently, we present an efficient local search algorithm to solve the problem. In the algorithm, a greedy search strategy is proposed to improve the current solutions iteratively. Moreover, randomized methods are used in selecting tasks and virtual machines for reassigning tasks. We carried out extensive computational experiments to evaluate the performance of our algorithm and compared experimental results with Swarm-based approaches, such as GA and PSO. The comparative results show that the proposed local search algorithm performs better than the comparative algorithms on the task scheduling problem.

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Section: Introductionmentioning

confidence: 99%