Decomposition Based Multi-objective Workflow Scheduling for Cloud Environments

Bugingo, Emmanuel; Zheng, Wei; Zhang, Dongzhan; Qin, Yingsheng; Zhang, Defu

doi:10.1109/cbd.2019.00017

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…A novel multi-objective workflow scheduling approach in IaaS clouds [42] is proposed to optimize the cost and workflow's makespan for real-world scientific applications. The authors [43] proposed a multi-purpose workflow-scheduling algorithm based on decomposition. The proposed model uses Pareto front solutions to achieve at least as good as scheduling results instead of repeatedly implementing the single-objective scheduling algorithm with multiple constraints.…”

Section: Workflow Schedulingmentioning

confidence: 99%

Multi-Dependency and Time Based Resource Scheduling Algorithm for Scientific Applications in Cloud Computing

2021

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Workflow scheduling is one of the significant issues for scientific applications among virtual machine migration, database management, security, performance, fault tolerance, server consolidation, etc. In this paper, existing time-based scheduling algorithms, such as first come first serve (FCFS), min–min, max–min, and minimum completion time (MCT), along with dependency-based scheduling algorithm MaxChild have been considered. These time-based scheduling algorithms only compare the burst time of tasks. Based on the burst time, these schedulers, schedule the sub-tasks of the application on suitable virtual machines according to the scheduling criteria. During this process, not much attention was given to the proper utilization of the resources. A novel dependency and time-based scheduling algorithm is proposed that considers the parent to child (P2C) node dependencies, child to parent node dependencies, and the time of different tasks in the workflows. The proposed P2C algorithm emphasizes proper utilization of the resources and overcomes the limitations of these time-based schedulers. The scientific applications, such as CyberShake, Montage, Epigenomics, Inspiral, and SIPHT, are represented in terms of the workflow. The tasks can be represented as the nodes, and relationships between the tasks can be represented as the dependencies in the workflows. All the results have been validated by using the simulation-based environment created with the help of the WorkflowSim simulator for the cloud environment. It has been observed that the proposed approach outperforms the mentioned time and dependency-based scheduling algorithms in terms of the total execution time by efficiently utilizing the resources.

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Section: Workflow Schedulingmentioning

confidence: 99%

Multi-Dependency and Time Based Resource Scheduling Algorithm for Scientific Applications in Cloud Computing

2021

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Towards decomposition based multi-objective workflow scheduling for big data processing in clouds

et al. 2020

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A Survey on Multi-Objective Tasks and Workflow Scheduling Algorithms in Cloud Computing

Sissodia

Rauthan

Naithani

2022

International Journal of Cloud Applications and Computing

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The challenge in cloud services is scheduling and allocating resources due to the exponential growth in demand and diversity of cloud resources. Scheduling is to allocate tasks across cloud resources so that scheduling algorithms reduce power consumption and offer cloud providers maximum return by reducing execution time. Various QoS parameters (such as makespan, load balancing, costs, etc.) are considered for efficient scheduling to reduce workload and enhance performance. Through this framework, multi-objective scheduling is a decision-making problem with multiple attributes considering the trade-off between the conflicting and competing parameters mentioned in the SLA between users and providers. This paper summarizes various multi-objective scheduling algorithms that consider contradictory and competing parameters or constraints to be optimized simultaneously. These algorithms are finally tabulated, presenting their advantages and disadvantages with cloud simulation tools and other QoS related parameters.

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Decomposition Based Multi-objective Workflow Scheduling for Cloud Environments

Cited by 3 publications

References 17 publications

Multi-Dependency and Time Based Resource Scheduling Algorithm for Scientific Applications in Cloud Computing

Multi-Dependency and Time Based Resource Scheduling Algorithm for Scientific Applications in Cloud Computing

Towards decomposition based multi-objective workflow scheduling for big data processing in clouds

A Survey on Multi-Objective Tasks and Workflow Scheduling Algorithms in Cloud Computing

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