Task Scheduling Research Based on Dynamic Backup in Cloud Environment

In order to improve resource utilization in cloud environment and reduce the total task execution time, a new scheduling strategytask scheduling strategy based on dynamic backup was proposed. The cloud system scheduling model was built, according to different security requirements for tasks to users and different trust level for nodes. This model can schedule the number of tasks backup reasonably, according to the change of system trust index. The simulation result shows that, this strategy can improve the overall system efficiency effectively.

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Data-Intensive Task Scheduling for Heterogeneous Big Data Analytics in IoT System

Energies ◽

10.3390/en13174508 ◽

2020 ◽

Vol 13 (17) ◽

pp. 4508

Author(s):

Xin Li ◽

Liangyuan Wang ◽

Jemal H. Abawajy ◽

Xiaolin Qin ◽

Giovanni Pau ◽

...

Keyword(s):

Big Data ◽

Data Analysis ◽

Task Scheduling ◽

Execution Time ◽

Data Centers ◽

Big Data Analysis ◽

Data Locality ◽

Data Migration ◽

Task Execution ◽

Task Execution Time

Efficient big data analysis is critical to support applications or services in Internet of Things (IoT) system, especially for the time-intensive services. Hence, the data center may host heterogeneous big data analysis tasks for multiple IoT systems. It is a challenging problem since the data centers usually need to schedule a large number of periodic or online tasks in a short time. In this paper, we investigate the heterogeneous task scheduling problem to reduce the global task execution time, which is also an efficient method to reduce energy consumption for data centers. We establish the task execution for heterogeneous tasks respectively based on the data locality feature, which also indicate the relationship among the tasks, data blocks and servers. We propose a heterogeneous task scheduling algorithm with data migration. The core idea of the algorithm is to maximize the efficiency by comparing the cost between remote task execution and data migration, which could improve the data locality and reduce task execution time. We conduct extensive simulations and the experimental results show that our algorithm has better performance than the traditional methods, and data migration actually works to reduce th overall task execution time. The algorithm also shows acceptable fairness for the heterogeneous tasks.

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An efficient Multiobjective Dynamic Task Scheduling to Minimize Task Execution Time for Computational Grid

INTERNATIONAL JOURNAL OF ADVANCED SCIENTIFIC AND TECHNICAL RESEARCH ◽

10.26808/rs.st.i8v4.02 ◽

2018 ◽

Vol 4 (8) ◽

Author(s):

Sophiya Sheikh

Keyword(s):

Task Scheduling ◽

Execution Time ◽

Computational Grid ◽

Task Execution ◽

Dynamic Task ◽

Task Execution Time ◽

Dynamic Task Scheduling

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Coupling Factor and Cost Based Task Clustering Method to Optimize Task Clustering For Scientific Workflows in Cloud Environment

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f9288.088619 ◽

2019 ◽

Vol 8 (6) ◽

pp. 4136-4143

Keyword(s):

Execution Time ◽

Clustering Algorithm ◽

Coupling Factor ◽

Scientific Workflows ◽

Cloud Environment ◽

Task Execution ◽

Multiple Tasks ◽

Task Execution Time ◽

System Overhead ◽

Task Clustering

Scientific workflows are large scale loosely coupled submissions that are used by Computational Scientists. They are composed of multiple tasks with dependencies between them and are composed of many fine granular tasks. Task clustering is an optimization method that combines multiple tasks into a single job such that task execution time and system overhead is reduced and thus the whole performance is improved in a cloud environment. Though existing task clustering algorithms has significantly reduced the System overhead, yet dependencies among the tasks are not well-thought-out. This work examines the features of task by which the tasks can be clustered and developed proficient task clustering algorithm. In this work two task clustering ideas were proposed namely Horizontal Coupling Factor (HCF) based clustering and Horizontal Processing Cost (HPC) based Task Clustering. Next, the proposed algorithm have been evaluated and tested for various real world applications and the experiment results shows that the proposed approach suits best for data intensive and Compute intensive applications. The obtained results showed that the HCF and HPC task clustering strategies can significantly improve the performance by reducing the task execution time and inter task Communication delay

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A web browsing interface for people with severe speech and motor impairment

Journal of Enabling Technologies ◽

10.1108/jet-07-2020-0029 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Manjira Sinha ◽

Tirthankar Dasgupta

Keyword(s):

Execution Time ◽

Motor Impairment ◽

Cognitive Effort ◽

Motor Disorder ◽

Web Browsing ◽

Task Execution ◽

Content Type ◽

Task Execution Time ◽

Scanning Mechanism ◽

The Web

Purpose The Web has become an indispensable medium used by people across the world for education, information, entertainment, social interaction as well as for various daily activities involving shopping and employment-related tasks. It is therefore becoming increasingly essential that the Web must be accessible to all people to provide equal access and equal opportunity. This is specifically more important for people with various kind of disabilities. Several initiatives such as development of Web accessibility guidelines, tools and technologies have been undertaken to make the Web usable for people with different disabilities. However, only a handful of them are aimed at people with Severe Speech and Motor Impairment (SSMI). This paper aims to present a Web browsing interface for people with severe speech and motor impairment. Design/methodology/approach The browser allows easy dissemination of information through World Wide Web for people with SSMI. The browser is augmented with both automatic as well as manual scanning mechanisms through which a motor disorder person can access the browser graphical user interface (GUI). Further, the browser provides an intelligent content scanning mechanism through which the Web contents can be accessed with less time and cognitive effort. Along with the desktop version, WebSanyog is successfully ported on Android-based tablets to make the system portable. Findings The system has been exhaustively field tested by people with SSMI. The browser has been deployed at the Indian Institute of Cerebral Palsy (IICP), Kolkata. The performance of the browser has been measured in terms of three parameters: The Task execution time (TET); Error rates analysis (ER); and Overall usability score by the subject. The evaluation results suggests that the proposed Web browsing interface is effective in terms of task execution time, cognitive effort and overall user satisfaction. Originality/value The browser GUI is integrated with an automatic scanning mechanism as an alternate way to access and navigate through Web pages, instead of using keyboard and mouse. The browser provides novel content access mechanisms that makes navigating through Web page contents like links, images and embedded videos easier and faster. To facilitate text entry, the browser provides two different options, namely, the predictive virtual scanning keyboard and a novel icon-based query entry scheme that allows generating search queries through the selection of multiple icons.

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