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

Lipsa, Swati; Dash, Ranjan Kumar; Ivković, Nikola; Cengiz, Korhan

doi:10.1109/access.2023.3255781

Cited by 8 publications

(4 citation statements)

References 44 publications

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“…Study in Ref. 37 presents a parallel algorithm for task scheduling, where both the priority assignment to tasks and the construction of the heap are concurrently executed. Authors in Ref.…”

Section: Related Workmentioning

confidence: 99%

Multi-objective and multi constrained task scheduling framework for computational grids

Hegde,

Srinivas,

Rajan

et al. 2024

Sci Rep

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Grid computing emerged as a powerful computing domain for running large-scale parallel applications. Scheduling computationally intensive parallel applications such as scientific, commercial etc., computational grids is a NP-complete problem. Many researchers have proposed several task scheduling algorithms on grids based on formulating and solving it as an optimization problem with different objective functions such as makespan, cost, energy etc. Further to address the requirements/demands/needs of the users (lesser cost, lower latency etc.) and grid service providers (high utilization and high profitability), a task scheduler needs to be designed based on solving a multi-objective optimization problem due to several trade-offs among the objective functions. In this direction, we propose an efficient multi-objective task scheduling framework to schedule computationally intensive tasks on heterogeneous grid networks. This framework minimizes turnaround time, communication, and execution costs while maximizing grid utilization. We evaluated the performance of our proposed algorithm through experiments conducted on standard, random, and scientific task graphs using the GridSim simulator.

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“…Study in Ref. 37 presents a parallel algorithm for task scheduling, where both the priority assignment to tasks and the construction of the heap are concurrently executed. Authors in Ref.…”

Section: Related Workmentioning

confidence: 99%

Multi-objective and multi constrained task scheduling framework for computational grids

Hegde,

Srinivas,

Rajan

et al. 2024

Sci Rep

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“…A Need Based lining model was produced for task booking in [11]. In any case, the planned model has not been applied in a powerful distributed computing climate.…”

Section: Related Workmentioning

confidence: 99%

Multicriteria generalized regressive neural federated learning for cloud computing task scheduling and resource allocation

George,

B K,

P Vijayan

2024

E3S Web of Conf.

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Cloud computing has arisen as a shrewd and well known worldview for people and associations to work with the entrance and use of registering assets through the web.With the rapid growth of cloud computing technology, efficiently running big data applications within minimal time has become a significant challenge. In this dynamic and scalable environment, effective resource allocation and task scheduling of big data applications play pivotal roles in optimizing performance, enhancing efficiency, and ensuring cost-effectiveness. In environments involving remote computing, task scheduling is a crucial consideration. In order to effectively accomplish resource-optimal task scheduling and minimize overall task execution time, a novel technique called Multicriteria Generalized Regressive Neural Federated Learning (MGRNFL) is developed to address the particular issues in cloud systems. Tasks from several users arrive at the cloud server at the start of the procedure. The cloud server's job scheduler then uses Multicriteria Federated Learning to carry out resource-optimal task scheduling. A decentralized machine learning technique called federated learning (FL) enables model training across several tasks that are gathered from cloud computing customers. This decentralized approach primarily focuses on learning from datasets to obtain a global model by aggregating the results of local models. The proposed techniques involve two different steps: local training models and global aggregation models. In the local training model, the task scheduler determines the resource-optimal virtual machine in the cloud server using a Generalized Regression Neural Network (GRNN) based on multicriteria functions of the virtual machine, such as energy, memory, CPU, and bandwidth. Based on these objective functions, resource-efficient virtual machines are determined to schedule multiple user tasks. The locally updated models are then combined and fed into the global aggregation model. Calculated within the global aggregation model is the weighted total of locally updated findings. The algorithm iterates through this process till the maximum number of times. In order to schedule incoming tasks, the resource-optimal virtual machine is found. Various quantitative criteria are used for the experimental evaluation, including makespan, throughput in relation to the number of tasks, and task scheduling efficiency.

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“…This section, focus on SI algorithms. The study revealed that SI algorithms were used to solve several optimization issues, like cloud scheduling [43,44].The following are the primary causes:  Environments for cloud computing are distributed and dynamic. Because the SI algorithms are distributed and decentralized, they can be used to solve real-time cloud scheduling issues.…”

Section: Literature Reviewmentioning

confidence: 99%

A meta-heuristic clustered grey wolf optimization algorithm for cloud resource scheduling

2023

IJATEE

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Introduction 1.1Background Cloud computing is an example of distributed computing and is one of the most widespread techniques nowadays. It enables the internet-based sharing of resources such as storage, servers, databases, and numerous application services. With the assistance of service providers, anyone can access these underfunded services [1]. Cloud service providers (CSPs), who run data centers (DCs) with powerful servers and infrastructure, offer these services. Software as a service (SaaS), platform as a service (PaaS), and infrastructure as a service (IaaS) are the three types of cloud service models [2]. *Author for correspondence CSPs supply virtualized computing resources over the internet in the IaaS model. Amazon web services (AWS), microsoft azure, and google cloud platform (GCP) are well-known IaaS providers. PaaS offers a platform for creating, deploying, and managing applications that include infrastructure, development tools, and services. While the cloud provider takes care of the maintenance and the underlying infrastructure, users may concentrate on creating and deploying applications. Heroku, google app engine, and microsoft azure app service are a few PaaS platform examples. Email services like Gmail, productivity suites like Microsoft 365, and customer relationship management tools like Sales-force are examples of SaaS applications. The shared use of virtual resources, especially IaaS, is the main focus of this study.

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

Cited by 8 publications

References 44 publications

Multi-objective and multi constrained task scheduling framework for computational grids

Multi-objective and multi constrained task scheduling framework for computational grids

Multicriteria generalized regressive neural federated learning for cloud computing task scheduling and resource allocation

A meta-heuristic clustered grey wolf optimization algorithm for cloud resource scheduling

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