A cloud computing scheduling and its evolutionary approaches

Abdalkafor, Ahmed Subhi; Jihad, Alaa Abdalqahar; Allawi, Emad Tariq

doi:10.11591/ijeecs.v21.i1.pp489-496

Cited by 6 publications

(6 citation statements)

References 29 publications

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“…The system service bus has multiple nodes. When integrating each node, resources and businesses are continuously shared, making the cloud scheduling system more unified [13].…”

Section: Hardware Design Of Power Marketing Meteringmentioning

confidence: 99%

Power Metering Automation System Based on Internet of Things

Dai

2022

Wireless Communications and Mobile Computing

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In order to solve the problems of poor stability and low operation efficiency of the traditional power marketing metering production automatic scheduling system, this paper proposes a power metering automation system based on the Internet of Things. In this paper, the hardware and software of the system are designed under the environment of the Internet of Things. The hardware part is mainly divided into three platforms: basic resource layer, platform business layer, and interface presentation layer. The normal operation is realized through the network topology, and the load balancing server and business subsystem are emphatically designed. The software is completed in four steps: coding, scheduling, backup, and display. The experimental results show that the integrity rate of the electric quantity data collected by this system in the experiment with the traditional system is as high as 99%. Compared with the traditional dispatching system, the power information dispatching system designed under the Internet of Things has better stability and higher operation efficiency.

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“…The system service bus has multiple nodes. When integrating each node, resources and businesses are continuously shared, making the cloud scheduling system more unified [13].…”

Section: Hardware Design Of Power Marketing Meteringmentioning

confidence: 99%

Power Metering Automation System Based on Internet of Things

Dai

2022

Wireless Communications and Mobile Computing

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“…In the context of cloud computing, scheduling is a significant challenge due to the fluctuating number of customers who want resources. The cloud provider must employ an efficient scheduling algorithm [14], [15] to meet the customer's expectations and deliver services effectively. Allocating resources in real time based on the specific types of workloads that require cloud services poses significant challenges for cloud providers.…”

Section: Introductionmentioning

confidence: 99%

Methods for optimizing the assignment of cloud computing resources and the scheduling of related tasks

Sultana,

Gulmeher,

Sarwath

2024

IJEECS

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<div align="center"><span>Efficient scheduling algorithms are necessary in the cloud paradigm to optimize service provision to clients while minimizing time duration, energy consumption, and violations of service level agreements (SLAs). Disregarding task appropriateness in resource scheduling can have a detrimental effect on the quality of service provided by cloud providers. Moreover, the utilization of resources in an ineffective manner will necessitate a substantial expenditure of energy to execute activities, leading to prolonged processing duration that adversely affect the temporal duration. Many research projects have focused on employment scheduling problems, and the algorithms used in these studies have offered answers that were deemed nearly flawless. This study presents a chaos bird swarm algorithm (Chaos BSA) approach that use machine learning to consider task priority while allocating tasks to the cloud platform. The method calculates the priorities of task virtual machines and incorporates these values into the scheduler. The scheduler will select tasks that align with the specified priorities and are compatible with the virtual machines. The implementation of the system utilized the openstack cloud platform and the cloudsim tool. The results and comparison with the baseline approach genetic algorithm (GA), ant colony optimization (ACO), and particle swarm optimization (PSO) clearly demonstrate that our Chaos BSA outperforms them by 18% in terms of efficiency.</span></div>

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“…An efficient load balancing algorithm is characterized by a decrease in the loading gap between the heaviest loaded and the lightest loaded virtual machines (VMs), with enhanced resource utilization [8]. Moreover, it reduces the average required processing time and response time, which is essential for performing the received tasks in cloud DCs [9]. In this respect, several loadbalancers have been proposed considering the aforementioned aspects [10]- [22].…”

Section: Introductionmentioning

confidence: 99%

Simulation and performance assessment of a modified throttled load balancing algorithm in cloud computing environment

Elnagar

ElKabbany

Al-Awamry

et al. 2022

IJECE

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<span lang="EN-US">Load balancing is crucial to ensure scalability, reliability, minimize response time, and processing time and maximize resource utilization in cloud computing. However, the load fluctuation accompanied with the distribution of a huge number of requests among a set of virtual machines (VMs) is challenging and needs effective and practical load balancers. In this work, a two listed throttled load balancer (TLT-LB) algorithm is proposed and further simulated using the CloudAnalyst simulator. The TLT-LB algorithm is based on the modification of the conventional TLB algorithm to improve the distribution of the tasks between different VMs. The performance of the TLT-LB algorithm compared to the TLB, round robin (RR), and active monitoring load balancer (AMLB) algorithms has been evaluated using two different configurations. Interestingly, the TLT-LB significantly balances the load between the VMs by reducing the loading gap between the heaviest loaded and the lightest loaded VMs to be 6.45% compared to 68.55% for the TLB and AMLB algorithms. Furthermore, the TLT-LB algorithm considerably reduces the average response time and processing time compared to the TLB, RR, and AMLB algorithms.</span>

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A cloud computing scheduling and its evolutionary approaches

Cited by 6 publications

References 29 publications

Power Metering Automation System Based on Internet of Things

Power Metering Automation System Based on Internet of Things

Methods for optimizing the assignment of cloud computing resources and the scheduling of related tasks

Simulation and performance assessment of a modified throttled load balancing algorithm in cloud computing environment

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