Comparison of Task Scheduling Algorithms in Cloud Environment

Malik, Babur Hayat; Amir, Mehwashma; Mazhar, Bilal; Ali, Shehzad; Jalil, Rabiya; Khalid, Javaria

doi:10.14569/ijacsa.2018.090550

Cited by 16 publications

(8 citation statements)

References 13 publications

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“…Besides, the related performance parameters in SMPIA were calculated based on the mean values, as obtained after 50 times of the program execution. This paper conforms results [1,8,9,10,14,16,22,23,24,25,30,31,36] avoiding Service Level Agreements (SLA) violation and QoS dropping.…”

Section: Comparison the Smpia With Benchmarks Algorithms And Methodssupporting

confidence: 87%

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Multi-Objective Task Scheduling Using Smart MPI-Based Cloud Resources

Mokhtari

Bayat

Motameni

2021

cai

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Task Scheduling and Resource Allocation (TSRA) is the key focus of cloud computing. This paper utilizes Smart Message Passing Interface based Approach (SMPIA) and the Roulette Wheel selection method in order to determine the best Alternative Virtual Machine (AVM). To do so, the Virtual MPI Bus (VMPIB) is employed for efficient communication among Virtual Machines (VMs) using SMPIA. In this matter, SMPIA is applied on different resource allocation and task scheduling strategies. MakeSpan (MS) was chosen as an optimization factor and solutions with minimum MS value as the best task mapping performance and reduced cloud consumption. The simulation is conducted using MATLAB. The analysis proves that applying SMPIA reduced the Total Execution Time (TET) of resource allocation, maximum MS time, and increase the Resource Utilization (RU), as compared to non-SMPIA for Greedy, Max-Min, Min-Min algorithms. It is observed that SMPIA can outperform non-SMPIA. The effect of SMPIA is more * Corresponding author Multi-Objective Task Scheduling Using Smart MPI-Based Cloud Resources 105 obvious as change in the MS and the number of cloud workloads increase. Furthermore, regarding the TET and MS of the tasks, the SMPIA can significantly reduce the starvation problem as well as the lack of sufficient resources. In addition, this approach improves the system's performance more than the previous methods, what reflects effectiveness of the proposed approach concerning the Message Passing Interface (MPI) communication time in the network virtualization. The mentioned text mining work was prepared concurrently after practical evaluation.

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Section: Comparison the Smpia With Benchmarks Algorithms And Methodssupporting

confidence: 87%

“…The value 1 refers to Enable MPI while the value 0 is used to Desable MPI. This paper takes advantage the Greedy, and fixed heuristic algorithms such as Max-Min, Min-Min [14,17,23] to find the optimal solution. These algorithms were implemented in the cloud platform on the datasets of MCITI.…”

Section: Active Mpi Processmentioning

confidence: 99%

Multi-Objective Task Scheduling Using Smart MPI-Based Cloud Resources

Mokhtari

Bayat

Motameni

2021

cai

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“…The authors highlight the benefits and drawbacks of existing task scheduling approaches under different cloud environments and for various scheduling objectives. An in-depth investigation and analysis of cloud computing resource scheduling algorithms is provided by [ 37 , 38 , 39 ].…”

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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“…The distribution of virtual resources among servers is done by resource scheduling. Workflow scheduling is to scheduling workflow comprised by an entire job in an efficient order [8].…”

Section: Cloud Scheduling Algorithmsmentioning

confidence: 99%