Fluctuation-Aware and Predictive Workflow Scheduling in Cost-Effective Infrastructure-as-a-Service Clouds

Cloud computing is used to deliver IT resources over the internet. Due to the popularity of cloud computing, nowadays, most of the scientific workflows are shifted towards this environment. There are lots of algorithms has been proposed in the literature to schedule scientific workflows in the cloud, but their execution cost is very high as well as they are not meeting the user-defined deadline constraint. This paper focuses on satisfying the userdefined deadline of a scientific workflow while minimizing the total execution cost. So, to achieve this, we have proposed a Cost-Effective under Deadline (CEuD) constraint workflow scheduling algorithm. The proposed CEuD algorithm considers all the essential features of Cloud and resolves the major issues such as performance variation, and acquisition delay. We have compared the proposed CEuD algorithm with the existing literature algorithms for scientific workflows (i.e., Montage, Epigenomics, and CyberShake) and getting better results for minimizing the overall execution cost of the workflow while satisfying the user-defined deadline.

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Software-Defined Cloud Infrastructure

Handbook of Research on Cloud and Fog Computing Infrastructures for Data Science - Advances in Computer and Electrical Engineering ◽

10.4018/978-1-5225-5972-6.ch006 ◽

2018 ◽

pp. 108-123

Author(s):

R. Mohanasundaram ◽

A. Jayanthiladevi ◽

Keerthana G.

Keyword(s):

Cloud Computing ◽

General Population ◽

Everyday Life ◽

Cost Effective ◽

Software As A Service ◽

The Internet ◽

Cloud Infrastructure ◽

Infrastructure As A Service ◽

Platform As A Service ◽

The World

Cloud computing suggests that the applications conveyed as services over the internet and frameworks programming in the server that give various services and offers in “pay as you go” trend which means pay only for what you use. The information and services are managed as software as a service (SaaS). Some sellers utilize terms, for example, IaaS (infrastructure as a service) and PaaS (platform as a service). The purpose of cloud computing is quickly expanding in everyday life. Today the use of cloud computing is widespread to the point that it is being utilized even in the medicinal services industry. As the development of cloud computing in healthcare is happening at a fast rate, we can expect a noteworthy piece of the healthcare administrations to move onto the Cloud and along these lines more focus is laid on giving cost-effective and efficient services to the general population all around the world. Cloud these days are turning into the new building pieces of significant organizations spread the world over. They offer assistance in servicing to offer different frameworks. Cloud computing has enhanced its technique and technologies in a better way to provide better services. Existing e-healthcare has many difficulties from advancement to usage. In this chapter, the authors discuss how cloud computing is utilized and the services provided by the Cloud and their models and its infrastructure.

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Reactive Workflow Scheduling in Fluctuant Infrastructure-as-a-Service Clouds Using Deep Reinforcement Learning

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering - Collaborative Computing: Networking, Applications and Worksharing ◽

10.1007/978-3-030-67540-0_17 ◽

2021 ◽

pp. 285-304

Author(s):

Qinglan Peng ◽

Wanbo Zheng ◽

Yunni Xia ◽

Chunrong Wu ◽

Yin Li ◽

...

Keyword(s):

Reinforcement Learning ◽

Workflow Scheduling ◽

Infrastructure As A Service

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AI-Based Enhanced Time Cost-Effective Cloud Workflow Scheduling

Internet of Things - Artificial Intelligence for Cloud and Edge Computing ◽

10.1007/978-3-030-80821-1_13 ◽

2022 ◽

pp. 277-297

Author(s):

V. Lakshmi Narasimhan ◽

V. S. Jithin ◽

M. Ananya ◽

Jonathan Oluranti

Keyword(s):

Cost Effective ◽

Workflow Scheduling ◽

Time Cost

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Dynamic Fault-Tolerant Workflow Scheduling with Hybrid Spatial-Temporal Re-Execution in Clouds

Information ◽

10.3390/info10050169 ◽

2019 ◽

Vol 10 (5) ◽

pp. 169 ◽

Cited By ~ 2

Author(s):

Na Wu ◽

Decheng Zuo ◽

Zhan Zhang

Keyword(s):

Large Scale ◽

Fault Tolerant ◽

Critical Path ◽

High Reliability ◽

Low Cost ◽

Cost Effective ◽

Scientific Workflow ◽

Workflow Scheduling ◽

Workflow Execution ◽

Computing Environments

Improving reliability is one of the major concerns of scientific workflow scheduling in clouds. The ever-growing computational complexity and data size of workflows present challenges to fault-tolerant workflow scheduling. Therefore, it is essential to design a cost-effective fault-tolerant scheduling approach for large-scale workflows. In this paper, we propose a dynamic fault-tolerant workflow scheduling (DFTWS) approach with hybrid spatial and temporal re-execution schemes. First, DFTWS calculates the time attributes of tasks and identifies the critical path of workflow in advance. Then, DFTWS assigns appropriate virtual machine (VM) for each task according to the task urgency and budget quota in the phase of initial resource allocation. Finally, DFTWS performs online scheduling, which makes real-time fault-tolerant decisions based on failure type and task criticality throughout workflow execution. The proposed algorithm is evaluated on real-world workflows. Furthermore, the factors that affect the performance of DFTWS are analyzed. The experimental results demonstrate that DFTWS achieves a trade-off between high reliability and low cost objectives in cloud computing environments.

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