Task duplication-based workflow scheduling for heterogeneous cloud environment

2016 ◽  
Author(s):  
Indrajeet Gupta ◽  
Madhu Sudan Kumar ◽  
Prasanta K. Jana
Keyword(s):  
Workflow Scheduling ◽  
Cloud Environment ◽  
Task Duplication ◽  
Heterogeneous Cloud

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.

A Review of PSO-Based Task and Workflow Scheduling Analysis in Cloud Environment

2021 ◽  
Author(s):  
Arti Ranjan ◽  
Inderpreet Kaur ◽  
Anika Bhardwaj ◽  
Vishnu Sharma
Keyword(s):  
Workflow Scheduling ◽  
Cloud Environment ◽  
Scheduling Analysis

Exploration of Secured Workflow Scheduling Models in Cloud Environment: A Survey

2018 ◽  
Author(s):  
Akindipe Olusegun Francis ◽  
Bugingo Emmanuel ◽  
Defu Zhang ◽  
Wei Zheng ◽  
Yingsheng Qin ◽  
...  
Keyword(s):  
Workflow Scheduling ◽  
Cloud Environment ◽  
Scheduling Models

Research on Energy-Saving Algorithms with Migration Energy Consumption in Heterogeneous Cloud Environment

2014 ◽  
Vol 986-987 ◽  
pp. 1383-1386
Author(s):  
Zhen Xing Yang ◽  
He Guo ◽  
Yu Long Yu ◽  
Yu Xin Wang
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Energy Saving ◽  
Large Scale ◽  
Electrical Energy ◽  
Cloud Environment ◽  
Migration Energy ◽  
Live Migration ◽  
Large Scale Data ◽  
Heterogeneous Cloud

Cloud computing is a new emerging paradigm which delivers an infrastructure, platform and software as services in a pay-as-you-go model. However, with the development of cloud computing, the large-scale data centers consume huge amounts of electrical energy resulting in high operational costs and environment problem. Nevertheless, existing energy-saving algorithms based on live migration don’t consider the migration energy consumption, and most of which are designed for homogeneous cloud environment. In this paper, we take the first step to model energy consumption in heterogeneous cloud environment with migration energy consumption. Based on this energy model, we design energy-saving Best fit decreasing (ESBFD) algorithm and energy-saving first fit decreasing (ESFFD) algorithm. We further provide results of several experiments using traces from PlanetLab in CloudSim. The experiments show that the proposed algorithms can effectively reduce the energy consumption of data center in the heterogeneous cloud environment compared to existing algorithms like NEA, DVFS, ST (Single Threshold) and DT (Double Threshold).

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