Resource Scheduling Algorithms in Cloud Computing: A Big Picture

Singhal, Saurabh; Sharma, Ashish

doi:10.1109/iscon52037.2021.9702313

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Resource management has always been a critical issue in cloud computing [11]. In the literature, numerous issues have been discussed for solving resource management problems (e.g., scheduling approaches [12] and allocation strategies [13]). For instance, cluster-based resource management schemes, which improve Kubernete algorithms [14]- [18], and resource allocation algorithms based on multi-objective evolutionary algorithms (MOEAs), such as particle swarm optimization (PSO) [19], simulated annealing (SA) [20], ant colony optimization (ACO) [21], and GA algorithm [22]- [24].…”

Section: A Resource Allocationmentioning

confidence: 99%

Optimal Resource Allocation Using Genetic Algorithm in Container-Based Heterogeneous Cloud

Chen,

Wen

2024

IEEE Access

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This paper tackles the complex problem of optimizing resource configuration for microservice management in heterogeneous cloud environments. To address this challenge, an enhanced framework, the multi-objective microservice allocation (MOMA) algorithm, is developed to formulate the efficient resource management of cloud microservice resources as a constrained optimization problem, guided by resource utilization and network communication overhead, which are two important factors in microservice resource allocation. The proposed framework simplifies the deployment of cloud services and streamlines workload monitoring and analysis within a diverse cloud system. A comprehensive comparison is made between the effectiveness of the proposed algorithm and existing algorithms on real-world datasets, with a focus on resource balancing, network overhead, and network reliability. Experimental results reveal that the proposed algorithm significantly enhances resource utilization, reduces network transmission overhead, and improves reliability.

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Section: A Resource Allocationmentioning

confidence: 99%

Optimal Resource Allocation Using Genetic Algorithm in Container-Based Heterogeneous Cloud

Chen,

Wen

2024

IEEE Access

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“…In a cloud computing environment, the inter-arrival time and service time of the tasks can be assumed to be exponentially distributed [48] and thus follow M/M/n queue model [49]. Let α be the mean rate of arrival and β be the mean service rate, then the probability that VMs are busy [44] is expressed as…”

Section: A Problem Formulationmentioning

confidence: 99%

Task Scheduling in Cloud Computing: A Priority-Based Heuristic Approach

et al. 2023

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In this paper, a task scheduling problem for a cloud computing environment is formulated by using the M/M/n queuing model. A priority assignment algorithm is designed to employ a new data structure named the waiting time matrix to assign priority to individual tasks upon arrival. In addition to this, the waiting queue implements a unique concept based on the principle of the Fibonacci heap for extracting the task with the highest priority. This work introduces a parallel algorithm for task scheduling in which the priority assignment to task and building of heap is executed in parallel with respect to the non-preemptive and preemptive nature of tasks. The proposed work is illustrated in a step-by-step manner with an appropriate number of tasks. The performance of the proposed model is compared in terms of overall waiting time and CPU time against some existing techniques like BATS, IDEA, and BATS+BAR to determine the efficacy of our proposed algorithms. Additionally, three distinct scenarios have been considered to demonstrate the competency of the task scheduling method in handling tasks with different priorities. Furthermore, the task scheduling algorithm is also applied in a dynamic cloud computing environment.

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