Grouped tasks scheduling algorithm based on QoS in cloud computing network

Ali, Hend Gamal El Din Hassan; Saroit, Imane Aly; Kotb, Amira

doi:10.1016/j.eij.2016.07.002

Cited by 64 publications

(12 citation statements)

References 3 publications

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“…Thus, only a few metrics are taken into consideration to improve the overall performance. This is an issue as improper Task Scheduling leads to an imbalanced load in the hosts [16], [17], [19], [20]. For example, if tasks arrive simultaneously following the FCFS algorithm's procedure, this could highly increase Makespan as the task will wait longer to finish executing.…”

Section: Problem Statementmentioning

confidence: 99%

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A Load Balancing Algorithm for the Data Centres to Optimize Cloud Computing Applications

et al. 2021

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Despite the many past research conducted in the Cloud Computing field, some challenges still exist related to workload balancing in cloud-based applications and specifically in the Infrastructure as service (IaaS) cloud model. Efficient allocation of tasks is a crucial process in cloud computing due to the restricted number of resources/virtual machines. IaaS is one of the models of this technology that handles the backend where servers, data centers, and virtual machines are managed. Cloud Service Providers should ensure high service delivery performance in such models, avoiding situations such as hosts being overloaded or underloaded as this will result in higher execution time or machine failure, etc. Task Scheduling highly contributes to load balancing, and scheduling tasks much adheres to the requirements of the Service Level Agreement (SLA), a document offered by cloud developers to users. Important SLA parameters such as Deadline are addressed in the LB algorithm. The proposed algorithm is aimed to optimize resources and improve Load Balancing in view of the Quality of Service (QoS) task parameters, the priority of VMs, and resource allocation. The proposed LB algorithm addresses the stated issues and the current research gap based on the literature's findings. Results showed that the proposed LB algorithm results in an average of 78% resource utilization compared to the existing Dynamic LBA algorithm. It also achieves good performance in terms of less Execution time and Makespan.

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Section: Problem Statementmentioning

confidence: 99%

“…A Grouped Tasks Scheduling (GTS) algorithm was introduced in [20]. It applies QoS parameters such as user type, expected priority, length, and latency of tasks.…”

Section: B Recent Literaturementioning

confidence: 99%

A Load Balancing Algorithm for the Data Centres to Optimize Cloud Computing Applications

et al. 2021

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“…Ali et al [6] suggested a clustered algorithm for software-based cloud activities. This algorithm incorporates various task attributes such as user category, task importance, task size, and task latency to calculate the task priority.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Based Security Model for Cloud based Task Scheduling

Devi¹,

Paulraj²,

Muthusenthil³

2020

KSII TIIS

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Scheduling plays a dynamic role in cloud computing in generating as well as in efficient distribution of the resources of each task. The principle goal of scheduling is to limit resource starvation and to guarantee fairness among the parties using the resources. The demand for resources fluctuates dynamically hence the prearranging of resources is a challenging task. Many task-scheduling approaches have been used in the cloud-computing environment. Security in cloud computing environment is one of the core issue in distributed computing. We have designed a deep learning-based security model for scheduling tasks in cloud computing and it has been implemented using CloudSim 3.0 simulator written in Java and verification of the results from different perspectives, such as response time with and without security factors, makespan, cost, CPU utilization, I/O utilization, Memory utilization, and execution time is compared with Round Robin (RR) and Waited Round Robin (WRR) algorithms

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“…One of the major requirement in cloud computing is to assign the job to appropriate resources while taking the QoS into consideration. [2].…”

Section: Introductionmentioning

confidence: 99%

“…In cloud computing where service and storage can be access by the client, Clients can submit their tasks into the c1oud for computation processing purpose or rather for future storage [7]. Those clients who are using this service have different requirements, this requirement knows as Quality of Service, which is a cooperative effort of service performance that defines the degree of gratification of a user for the service [2]. The dynamic nature of this environment can endure a big challenge as some assigned resources might overload during the execution that may cause the failure of the whole execution process [8].…”

Section: Introductionmentioning

confidence: 99%

OTS: An Optimal Tasks Scheduling Algorithm Based on QoS in Cloud Computing Network

Alhakimi¹

2019

IJATCSE

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Cloud Computing has emerged as a service model that offers online accessible resources to the clients. These resources contain storage, servers, and other applications and it provides security, flexibility, and scalability. In Max-Min algorithm where the large tasks have their priority to be scheduled first this leads small tasks to stay longer in the queue until all huge tasks finished their execution. This study presents an optimal tasks scheduling algorithm by enhancing Max-Min algorithm. The simulation results have proven that the Proposed Optimal Tasks Scheduling OTS completes tasks execution with less execution time and higher performance compared with Max-Min and TS algorithms. The overall results show that the performance of the proposed algorithm achieved 6% better in terms of time execution compared of both of Max-Min and TS algorithms

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Grouped tasks scheduling algorithm based on QoS in cloud computing network

Cited by 64 publications

References 3 publications

A Load Balancing Algorithm for the Data Centres to Optimize Cloud Computing Applications

A Load Balancing Algorithm for the Data Centres to Optimize Cloud Computing Applications

Deep Learning Based Security Model for Cloud based Task Scheduling

OTS: An Optimal Tasks Scheduling Algorithm Based on QoS in Cloud Computing Network

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