Optimized Round Robin Scheduling Algorithm Using Dynamic Time Quantum Approach in Cloud Computing Environment

Biswas, Dipto; Samsuddoha, Md.; Asif, Md. Rashid Al; Ahmed, Md. Manjur

doi:10.5815/ijisa.2023.01.03

Cited by 6 publications

(3 citation statements)

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“…The Test Case will use standard testing from Grafana Labs K6 [7][8] [9][10] [11][12] [13]. Based on Grafana Labs documentation, K6 is an open-source load balancer testing tool that simplifies and increases performance testing productivity for cloud technicians and engineers.…”

Section: Resultsmentioning

confidence: 99%

Round-Robin Algorithm in Load Balancing for National Data Centers

Sudiatmika,

Indrawan,

Sariyasa

2023

Knowl. Eng. Data Sci.

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The Provincial Government of Bali assumes a crucial role in administering various public service applications to meet the requirements of its community, traditional villages, and regional apparatus. Nevertheless, the escalating magnitude of traffic and uneven distribution of requests have resulted in substantial server burdens, which may jeopardize the operation of applications and heighten the likelihood of downtime. Ensuring efficient load distribution is of utmost importance in tackling these difficulties, and the Round Robin algorithm is often utilized for this purpose. However, the current body of research has not extensively examined the distinct circumstances surrounding on-premise servers in the Bali Provincial Government. The primary objective of this study is to address the significant gap in knowledge by conducting a comprehensive evaluation of the Round Robin algorithm's effectiveness in load-balancing on-premise servers inside the Bali Provincial Government. The primary objective of our study is to assess the appropriateness of the algorithm within the given context, with the ultimate goal of providing practical and implementable suggestions. The observations above can optimize system efficiency and minimize periods of inactivity, thereby enhancing the provision of vital public services across Bali. This study provides essential insights for enhancing server infrastructure and load-balancing strategies through empirical evaluation and comprehensive analysis. Its findings are valuable for the Bali Provincial Government and serve as a reference for other organizations facing challenges managing server loads. This study signifies a notable advancement in establishing reliable and practical public service applications within Bali.

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Section: Resultsmentioning

confidence: 99%

Round-Robin Algorithm in Load Balancing for National Data Centers

Sudiatmika,

Indrawan,

Sariyasa

2023

Knowl. Eng. Data Sci.

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“…Numerous conventional scheduling algorithms have been developed to address the problem of scheduling cloud workflows and standalone tasks in cloud environments. Biswas et al (2023) proposed a refined method for the scheduling of polling tasks. The scheduling efficiency is enhanced by this novel algorithm, which dynamically determines the time quantum.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Red-tailed Hawk Algorithm: Elevating Cloud Task Scheduling Efficiency

Qin,

Li,

Tong

et al. 2024

Preprint

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With the popularity of cloud computing, effective task scheduling has become the key to optimizing resource allocation, reducing operation costs, and enhancing the user experience. The complexity and dynamics of cloud computing environments require task scheduling algorithms that can flexibly respond to multiple computing demands and changing resource states. To this end, this study proposes an improved RTH algorithm, the ERTH algorithm, which aims to improve the efficiency and effectiveness of task scheduling in cloud computing environments. Evaluations in the CEC benchmark test sets show that the ERTH algorithm outperforms the traditional PSO and GWO in several performance metrics and outperforms the emerging GWCA and CSA. This result signifies a significant advancement of the ERTH algorithm in intelligent optimization. Further, we apply the ERTH algorithm to a real cloud computing environment and conduct a comparison with the original algorithm RTH, PSO, ACO, WOA, and HLBO. When dealing with cloud computing task scheduling problems, the ERTH algorithm demonstrates better task completion time, resource utilization, and system load balancing performance. Especially in high-load and complex task scenarios, the stability and scalability of the ERTH algorithm perform exceptionally well. This study not only reveals the powerful potential of the ERTH algorithm in cloud computing task scheduling but also brings new perspectives and solutions for cloud service providers in resource allocation and task scheduling strategies. The proposal and validation of the ERTH algorithm are of great significance in promoting the application of intelligent optimization algorithms in cloud computing.

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“…A dynamic round robin scheduling algorithm is proposed in [60]. Authors dynamically calculate the time quantum for each round by taking into account the differences among the maximum burst times of the three tasks in the ready queue.…”

Section: Multi-objective Scheduling Techniquesmentioning

confidence: 99%

Online Task Scheduling of Big Data Applications in the Cloud Environment

2023

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The development of big data has generated data-intensive tasks that are usually time-consuming, with a high demand on cloud data centers for hosting big data applications. It becomes necessary to consider both data and task management to find the optimal resource allocation scheme, which is a challenging research issue. In this paper, we address the problem of online task scheduling combined with data migration and replication in order to reduce the overall response time as well as ensure that the available resources are efficiently used. We introduce a new scheduling technique, named Online Task Scheduling algorithm based on Data Migration and Data Replication (OTS-DMDR). The main objective is to efficiently assign online incoming tasks to the available servers while considering the access time of the required datasets and their replicas, the execution time of the task in different machines, and the computational power of each machine. The core idea is to achieve better data locality by performing an effective data migration while handling replicas. As a result, the overall response time of the online tasks is reduced, and the throughput is improved with enhanced machine resource utilization. To validate the performance of the proposed scheduling method, we run in-depth simulations with various scenarios and the results show that our proposed strategy performs better than the other existing approaches. In fact, it reduces the response time by 78% when compared to the First Come First Served scheduler (FCFS), by 58% compared to the Delay Scheduling, and by 46% compared to the technique of Li et al. Consequently, the present OTS-DMDR method is very effective and convenient for the problem of online task scheduling.

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Optimized Round Robin Scheduling Algorithm Using Dynamic Time Quantum Approach in Cloud Computing Environment

Cited by 6 publications

References 0 publications

Round-Robin Algorithm in Load Balancing for National Data Centers

Round-Robin Algorithm in Load Balancing for National Data Centers

Enhanced Red-tailed Hawk Algorithm: Elevating Cloud Task Scheduling Efficiency

Online Task Scheduling of Big Data Applications in the Cloud Environment

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