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

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.

scholarly journals Hybrid RSO Algorithm with SFLA for Scientific Workflow Scheduling in Cloud Using Clustering Techniques

2021 ◽  
Author(s):  
KARPAGAM M
Keyword(s):  
Cloud Computing ◽  
Information Exchange ◽  
Virtual Machines ◽  
Optimal Schedule ◽  
Scientific Workflow ◽  
Workflow Scheduling ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Optimization Heuristics ◽  
Shuffled Frog Leaping

Abstract An inevitable part of the cloud computing environment is virtualization, as it can multiplex or combine many virtual machines in a single physical machine, and simultaneously an isolated environment is provided to every virtual machine. An important issue in cloud computing is workflow scheduling, which maps tasks of workflow to VMs based on various functional and non-functional requisites. Workflow scheduling is an NP-hard optimization problem and it is quite hard to achieve an optimal schedule. Metaheuristic algorithms helped in solving the problem of cloud task scheduling and this was compared to other heuristics. Reactive Search (RSO) and its structure will consist of a local heuristic based on a certain neighborhood complemented by making use of a memory-based mechanism. The Shuffled Frog Leaping Algorithm (SFLA) is based on swarm evolution that imitates information exchange divided into memeplexes when searching for food. This paper proposes a new set of optimization heuristics along with hybrid optimizations (RSO - SFLA) to solve problems in combinatorial optimization.

Workflow Scheduling for Scientific Application in Homogeneous Cloud Environment

2017 ◽  
Vol 7 (7) ◽  
pp. 137
Author(s):  
. Monika ◽  
Pardeep Kumar ◽  
Sanjay Tyagi
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Large Data ◽  
Workflow Scheduling ◽  
Cloud Environment ◽  
Computing Environment ◽  
Scientific Application ◽  
Cloud Computing Environment ◽  
Flow Of Information

In Cloud computing environment QoS i.e. Quality-of-Service and cost is the key element that to be take care of. As, today in the era of big data, the data must be handled properly while satisfying the request. In such case, while handling request of large data or for scientific applications request, flow of information must be sustained. In this paper, a brief introduction of workflow scheduling is given and also a detailed survey of various scheduling algorithms is performed using various parameter.

scholarly journals Cloud-based intelligent self-diagnosis and department recommendation service using Chinese medical BERT

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Junshu Wang ◽  
Guoming Zhang ◽  
Wei Wang ◽  
Ka Zhang ◽  
Yehua Sheng
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Medical Service ◽  
Rapid Development ◽  
Medical Knowledge ◽  
Language Models ◽  
Computing Environment ◽  
Computing Power ◽  
Cloud Computing Environment ◽  
Proposed Model

AbstractWith the rapid development of hospital informatization and Internet medical service in recent years, most hospitals have launched online hospital appointment registration systems to remove patient queues and improve the efficiency of medical services. However, most of the patients lack professional medical knowledge and have no idea of how to choose department when registering. To instruct the patients to seek medical care and register effectively, we proposed CIDRS, an intelligent self-diagnosis and department recommendation framework based on Chinese medical Bidirectional Encoder Representations from Transformers (BERT) in the cloud computing environment. We also established a Chinese BERT model (CHMBERT) trained on a large-scale Chinese medical text corpus. This model was used to optimize self-diagnosis and department recommendation tasks. To solve the limited computing power of terminals, we deployed the proposed framework in a cloud computing environment based on container and micro-service technologies. Real-world medical datasets from hospitals were used in the experiments, and results showed that the proposed model was superior to the traditional deep learning models and other pre-trained language models in terms of performance.

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