QoS-DPSO: QoS-aware Task Scheduling for Cloud Computing System

Jing, Weipeng; Zhao, Chuanyu; Miao, Qiucheng; Song, Houbing; Chen, Guangsheng

doi:10.1007/s10922-020-09573-6

Cited by 35 publications

(14 citation statements)

References 31 publications

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“…The presented technique can be validated by utilizing the CloudSim toolkit. Jing et al [14] proposed a QoS scheduling algorithm by integrating the cloud features, later, we developed a task scheduling object to guarantee fault can be tolerated at the time of the task execution. At last, we presented a QoS-aware scheduling approach, QoS-DPSO, for satisfying the QoS requirement in CC system.…”

Section: Related Workmentioning

confidence: 99%

Gorilla Troops Optimizer Based Fault Tolerant Aware Scheduling Scheme for Cloud Environment

Muralidharan¹,

Latha²

2023

Intelligent Automation &Amp; Soft Computing

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In cloud computing (CC), resources are allocated and offered to the clients transparently in an on-demand way. Failures can happen in CC environment and the cloud resources are adaptable to fluctuations in the performance delivery. Task execution failure becomes common in the CC environment. Therefore, fault-tolerant scheduling techniques in CC environment are essential for handling performance differences, resource fluxes, and failures. Recently, several intelligent scheduling approaches have been developed for scheduling tasks in CC with no consideration of fault tolerant characteristics. With this motivation, this study focuses on the design of Gorilla Troops Optimizer Based Fault Tolerant Aware Scheduling Scheme (GTO-FTASS) in CC environment. The proposed GTO-FTASS model aims to schedule the tasks and allocate resources by considering fault tolerance into account. The GTO-FTASS algorithm is based on the social intelligence nature of gorilla troops. Besides, the GTO-FTASS model derives a fitness function involving two parameters such as expected time of completion (ETC) and failure probability of executing a task. In addition, the presented fault detector can trace the failed tasks or VMs and then schedule heal submodule in sequence with a remedial or retrieval scheduling model. The experimental validation of the GTO-FTASS model has been performed and the results are inspected under several aspects. Extensive comparative analysis reported the better outcomes of the GTO-FTASS model over the recent approaches.

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Section: Related Workmentioning

confidence: 99%

Gorilla Troops Optimizer Based Fault Tolerant Aware Scheduling Scheme for Cloud Environment

Muralidharan¹,

Latha²

2023

Intelligent Automation &Amp; Soft Computing

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“…Ref. [ 35 ] designed a method based on a traditional particle swarm algorithm and QoS-aware task scheduling in order to ensure QoS of users: deadline, scheduling, budget, and reliability. This multi-objective QoS algorithm was tested on a medical cloud platform and the experimental results proved its effectiveness when compared to other known approaches.…”

Section: Literature Reviewmentioning

confidence: 99%

QoS-Aware Algorithm Based on Task Flow Scheduling in Cloud Computing Environment

Rakrouki

Alharbe

2022

Sensors

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This paper deals with the challenging problem of scheduling users’ tasks, while taking into consideration users’ quality of service (QoS) requirements, with the objective of reducing the energy consumption of physical machines. This paper presents a model to analyze the current state of the running tasks according to the results of the QoS prediction assigned by an ARIMA prediction model optimized with Kalman filter. Then, we calculate a scheduling policy with a combined particle swarm optimization (PSO) and gravitational search algorithm (GSA) algorithms according to the QoS status analysis. Experimental results show that the proposed HPSO algorithm reduces resources consumption 16.51% more than the original hybrid algorithm, and the violation of service-level agreement (SLA) is 0.053% less when the optimized prediction model is used.

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“…They optimized reliability, Resource utilization, and latency. (Jing et al,2021) proposed a fault-tolerant scheduling algorithm while satisfying users' requirements for service quality. A QoS-aware discrete particle swarm optimization (QoS-DPSO) is proposed to optimize reliability under budget and time constraints.…”

Section: Related Workmentioning

confidence: 99%

A Deadline-Constrained Time-Cost-Effective Salp Swarm Algorithm for Resource Optimization in Cloud Computing

Jain

Sharma

2022

International Journal of Applied Metaheuristic Computing

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Nowadays, Cloud Computing has become the most attractive platform, which provides anything as a Service (XaaS). Many applications may be developed and run on the cloud without worrying about platforms. It is a big challenge to allocate optimal resources to these applications and satisfy user's quality of service requirements. Here, in this paper, a Deadline Constrained Time-Cost effective Salp Swarm Algorithm (DTC-SSA) is proposed to achieve optimized resource allocation. DTC-SSA assigns the user's task to an appropriate virtual machine (Vm) and achieves a trade-off between cost and makespan while satisfying the deadline constraints. Rigorous examination of the algorithm is conducted on the various scale and cloud resources. The proposed algorithm is compared with Particle Swarm Optimization (PSO), Grey Wolf Optimizer(GWO), Bat Algorithm(BAT), and Genetic Algorithm(GA). Simulation results prove that it outperforms others by minimizing makespan, execution cost, Response time, and improving resource utilization throughput.

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QoS-DPSO: QoS-aware Task Scheduling for Cloud Computing System

Cited by 35 publications

References 31 publications

Gorilla Troops Optimizer Based Fault Tolerant Aware Scheduling Scheme for Cloud Environment

Gorilla Troops Optimizer Based Fault Tolerant Aware Scheduling Scheme for Cloud Environment

QoS-Aware Algorithm Based on Task Flow Scheduling in Cloud Computing Environment

A Deadline-Constrained Time-Cost-Effective Salp Swarm Algorithm for Resource Optimization in Cloud Computing

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