HPSOGWO: A Hybrid Algorithm for Scientific Workflow Scheduling in Cloud Computing

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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Deadline-constrained coevolutionary genetic algorithm for scientific workflow scheduling in cloud computing

Concurrency and Computation Practice and Experience ◽

10.1002/cpe.3942 ◽

2016 ◽

Vol 29 (5) ◽

pp. e3942 ◽

Cited By ~ 42

Author(s):

Li Liu ◽

Miao Zhang ◽

Rajkumar Buyya ◽

Qi Fan

Keyword(s):

Genetic Algorithm ◽

Cloud Computing ◽

Scientific Workflow ◽

Workflow Scheduling ◽

Coevolutionary Genetic Algorithm

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A GSA based hybrid algorithm for bi-objective workflow scheduling in cloud computing

Future Generation Computer Systems ◽

10.1016/j.future.2018.01.005 ◽

2018 ◽

Vol 83 ◽

pp. 14-26 ◽

Cited By ~ 41

Author(s):

Anubhav Choudhary ◽

Indrajeet Gupta ◽

Vishakha Singh ◽

Prasanta K. Jana

Keyword(s):

Cloud Computing ◽

Hybrid Algorithm ◽

Workflow Scheduling

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SWSA: A Hybrid Scientific Workflow Scheduling Algorithm based on Metaheuristic Approach in Cloud Computing Environment

Journal of Information & Knowledge Management ◽

10.1142/s0219649221500350 ◽

2021 ◽

pp. 2150035

Author(s):

Leyli Abbasi ◽

Hossien Momeni ◽

Mehdi Yaghoubi

Keyword(s):

Cloud Computing ◽

Large Scale ◽

Scheduling Algorithm ◽

Scientific Workflow ◽

Workflow Scheduling ◽

Initial Population ◽

Large Set ◽

Computing Environment ◽

Cloud Computing Environment ◽

Ranking Tasks

The cloud computing environment with a set of distributed computing resources is a suitable platform for the execution of large-scale applications. One of these applications is scientific workflow applications in which a large set of interrelated tasks are executed for a certain purpose. Scientific workflow scheduling is one of the main challenges in this area, which aims at the optimal assignment of tasks to computational resources. Given the heterogeneity of cloud computing resources, the scientific workflow scheduling is an NP-Complete problem that can be solved by heuristic methods. In this paper, an improved evolutionary algorithm called Scientific Workflow Scheduling Algorithm (SWSA) for scheduling scientific workflows in the cloud will be provided by ranking tasks and improving the initial population of tasks. The objective of this algorithm is to create a balance and an improvement in the parameters of the execution cost and workflow execution completion time. In this proposed approach, a heuristic algorithm is used to rank and generate the initial population, which increases the convergence rate. The experimental results show that SWSA is more efficient in terms of cost and execution time compared with other approaches.

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