Load Balancing Scheduling Algorithm for Concurrent Workflow

Singhal, Sunita; Patel, Jemin

doi:10.4149/cai_2018_2_311

Cited by 7 publications

(10 citation statements)

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“…The obtained results of the experiment indicate that the SMPIA outperform standard algorithms including Min-Min algorithm [34] in terms of MS. Moreover, the SMPIA performs better than the algorithm developed in [4,6,20,30,35,17,31,33,36,37], in terms of ET, MS and RU, respectively.…”

Section: Introductionmentioning

confidence: 91%

“…The papers of [2,4,6,7,10,11,12,19] confirmed that the implementation of MPI applications is appropriate on the cloud. In [17], the ranking of each task in Heterogeneous Earliest Finish Time (HEFT) algorithm was performed based on the average of task connections and cost of computing between the current task and its substitution. In [20], the Eager Map algorithm for solving mapping problems was proposed in cluster nodes and cores, which was based on a Greedy heuristic in order to match application communication patterns to hardware hierarchies.…”

Section: Related Workmentioning

confidence: 99%

“…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%

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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: Introductionmentioning

confidence: 91%

Section: Related Workmentioning

confidence: 99%

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

Mokhtari

Bayat

Motameni

2021

cai

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“…Researchers were prompted to potentially contribute heuristic algorithms because of the complete NP problem [8]. In this research paper, the algorithm for heterogeneous computer models has proposed an efficient critical task prediction algorithm for distributing the load over the cloud server.…”

Section: B Contributionmentioning

confidence: 99%

“…As the Prediction based priority queue is full. Therefore, the relation between the load LEs of the request satisfies the equation (8). The loading rate of workload request is c, and lη is the PMHEFT capacity.…”

Section: Imentioning

confidence: 99%