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

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...

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Veröffentlicht in:EURASIP journal on wireless communications and networking Jg. 2019; H. 1; S. 1 - 19
Hauptverfasser: Ma, Xiaojin, Gao, Honghao, Xu, Huahu, Bian, Minjie
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 08.11.2019
Springer Nature B.V
SpringerOpen
Schlagworte:
Algorithms
Application scheduling
Cloud computing
Communications Engineering
Completion time
Computer simulation
Computing costs
Crossovers
Deadlines
Dependence
Engineering
Information Systems Applications (incl.Internet)
Internet of Things
IoT
Multi-modal Sensor Data Fusion in Internet of Things
Multiple constraints
Mutation
Networks
Optimization
Quality of service architectures
Signal,Image and Speech Processing
Task scheduling
Virtual environments
Virtualization
Workflow
Application scheduling
Multiple constraints
Virtualization
IoT
ISSN:1687-1499, 1687-1472, 1687-1499
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Zusammenfassung: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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ISSN:1687-1499
1687-1472
1687-1499
DOI:10.1186/s13638-019-1557-3