Cost Effective Genetic Algorithm for Workflow Scheduling in Cloud Under Deadline Constraint

Meena, Jasraj; Kumar, Manoj; Vardhan, Manu

doi:10.1109/access.2016.2593903

Cited by 107 publications

(50 citation statements)

References 36 publications

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“…According to these studies, the characteristics of task allocation in the cloud have become an indispensable concern in scheduling problems. Meanwhile, the meta-heuristic GA is often applied to the problem because of its time efficiency [33], and different improvements of the GA to solve the workflow scheduling problem have been studied in [34][35][36][37]. On the basis of these studies, this paper proposes a DCGA for workflow scheduling in the cloud while considering the characteristics of the cloud, such as on-demand acquisition, dynamic extension, heterogeneity, acquisition delay, and performance variation of VMs.…”

Section: Related Workmentioning

confidence: 99%

An IoT-based task scheduling optimization scheme considering the deadline and cost-aware scientific workflow for cloud computing

Gao

et al. 2019

J Wireless Com Network

118

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Large-scale applications of Internet of things (IoT), which require considerable computing tasks and storage resources, are increasingly deployed in cloud environments. Compared with the traditional computing model, characteristics of the cloud such as pay-as-you-go, unlimited expansion, and dynamic acquisition represent different conveniences for these applications using the IoT architecture. One of the major challenges is to satisfy the quality of service requirements while assigning resources to tasks. In this paper, we propose a deadline and cost-aware scheduling algorithm that minimizes the execution cost of a workflow under deadline constraints in the infrastructure as a service (IaaS) model. Considering the virtual machine (VM) performance variation and acquisition delay, we first divide tasks into different levels according to the topological structure so that no dependency exists between tasks at the same level. Three strings are used to code the genes in the proposed algorithm to better reflect the heterogeneous and resilient characteristics of cloud environments. Then, HEFT is used to generate individuals with the minimum completion time and cost. Novel schemes are developed for crossover and mutation to increase the diversity of the solutions. Based on this process, a task scheduling method that considers cost and deadlines is proposed. Experiments on workflows that simulate the structured tasks of the IoT demonstrate that our algorithm achieves a high success rate and performs well compared to state-of-the-art algorithms.

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Section: Related Workmentioning

confidence: 99%

An IoT-based task scheduling optimization scheme considering the deadline and cost-aware scientific workflow for cloud computing

Gao

et al. 2019

J Wireless Com Network

118

View full text Add to dashboard Cite

show abstract

“…15,17,[23][24][25][26][27][28][29] The work of Verma and Kaushal 23 proposed a bi-criteria priority particle swarm optimization (BPSO) to schedule workflow application to cloud resources so as to optimize the execution cost for running the workflow and also minimize the total execution time under the given budget. 15,17,[23][24][25][26][27][28][29] The work of Verma and Kaushal 23 proposed a bi-criteria priority particle swarm optimization (BPSO) to schedule workflow application to cloud resources so as to optimize the execution cost for running the workflow and also minimize the total execution time under the given budget.…”

Section: Related Workmentioning

confidence: 99%

“…Most of the existing works have concentrated on the optimization of different constraints related to workflow scheduling by considering both at the same time or one of them, eg, see other works. 15,17,[23][24][25][26][27][28][29] The work of Verma and Kaushal 23 proposed a bi-criteria priority particle swarm optimization (BPSO) to schedule workflow application to cloud resources so as to optimize the execution cost for running the workflow and also minimize the total execution time under the given budget. The work presented by Abrishami et al 24 adopted partial critical path to IAAS cloud and proposed two algorithms: a one-phase algorithm IC-PCP and a two-phases PCP algorithm, which both have polynomial time complexity suitable for large workflows.…”

Section: Related Workmentioning

confidence: 99%

Cost optimization heuristics for deadline constrained workflow scheduling on clouds and their comparative evaluation

Bugingo

Qin

Wang

et al. 2018

Concurrency and Computation

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Nowadays, cloud service providers usually offer users virtual machines with various combinations of configurations and prices. As this new service scheme emerges, the problem of choosing the cost-minimized combination under a deadline constraint is becoming more complex for users.The complexity of determining the cost-minimized combination may be resulted from different causes: the characteristics of user applications and providers' setting on the configurations and pricing of virtual machine. In this paper, we proposed an algorithm with two variants to help the users to schedule their workflow applications on clouds so that the cost can be minimized and the deadline constraints can be satisfied. The proposed algorithm is evaluated by extensive simulation experiments with two realistic workflows.

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“…JasrajMeena et al [9] Cost Effective GA in favor of Workflow Scheduling in Cloud in Deadline Constraint…”

Section: Authormentioning

confidence: 99%

List Scheduling Algorithms Classification: An Analytical Study

Akanksha¹

2018

ijarcs

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Multiprocessor scheduling is another name of task scheduling in form of algorithms that are mostly utilized in systematic as well as engineering appliance that is also known as the issue of NP-complete. The main aim of scheduling is the reduction of execution time. The illustration of task scheduling for multiprocessor scheduling is shown by DAG (Directed Acyclic graph). The categorization for this is into Static as well as dynamic scheduling. The list task scheduling is the example of static task scheduling algorithm. Varied task scheduling algorithms, like ISH, HLFET, MCP, ETF, CNPT and DLS are reviewed in this paper. The comparison of list task scheduling isdependent on metrics, termed as SLR, load balancing, efficiency and speed up.

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Cost Effective Genetic Algorithm for Workflow Scheduling in Cloud Under Deadline Constraint

Cited by 107 publications

References 36 publications

An IoT-based task scheduling optimization scheme considering the deadline and cost-aware scientific workflow for cloud computing

An IoT-based task scheduling optimization scheme considering the deadline and cost-aware scientific workflow for cloud computing

Cost optimization heuristics for deadline constrained workflow scheduling on clouds and their comparative evaluation

List Scheduling Algorithms Classification: An Analytical Study

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