Mobile Agent-Based Computational Steering for Distributed Simulation

2007 ◽  
Author(s):  
Yu-Cheng Chou ◽  
David Ko ◽  
Harry H. Cheng ◽  
Roger L. Davis ◽  
Bo Chen
Keyword(s):  
Mobile Agent ◽  
Large Scale ◽  
Distributed Simulation ◽  
Computation Time ◽  
Data Sets ◽  
Computational Steering ◽  
Program Code ◽  
Agent Based ◽  
Diverse Data ◽  
Computational Systems

Two challenging problems in the area of scientific computation are long computation time and large-scale, distributed, and diverse data sets. As the scale of science and engineering applications rapidly expands, these two problems become more manifest than ever. This paper presents the concept of Mobile Agent-based Computational Steering (MACS) for distributed simulation. The MACS allows users to apply new or modified algorithms to a running application by altering certain sections of the program code without the need of stopping the execution and recompiling the program code. The concept has been validated through an application for dynamic CFD data post processing. The validation results show that the MACS has a great potential to enhance productivity and data manageability of large-scale distributed computational systems.

Mobile Agent-Based Computing Resource and Usage Monitoring at Large Scale Computer Centers

2011 ◽  
Author(s):  
Zhixin Tie ◽  
David Ko ◽  
Harry H. Cheng
Keyword(s):  
Monitoring System ◽  
Mobile Agent ◽  
Computer Games ◽  
Data Exchange ◽  
Large Scale ◽  
Agent Based ◽  
Computer Center ◽  
Control Command ◽  
Computer Centers ◽  
Computer Resources

Mobile agent technology has become an important approach for the design and development of distributed systems. However, there is little research regarding the monitoring of computer resources and usage at large scale distributed computer centers. This paper presents a mobile agent-based system called the Mobile Agent Based Computer Monitoring System (MABCMS) that supports the dynamic sending and executing of control command, dynamic data exchange, and dynamic deployment of mobile code in C/C++. Based on the Mobile-C library, agents can call low level functions in binary dynamic or static libraries, and thus can monitor computer resources and usage conveniently and efficiently. Two experimental applications have been designed using the MABCMS. The experiments were conducted in a university computer center with hundreds of computer workstations and 15 server machines. The first experiment uses the MABCMS to detect improper usage of the computer workstations, such as playing computer games. The second experimental application uses the MABCMS to detect system resources such as available hard disk space. The experiments show that the mobile agent based monitoring system is an effective method for detecting and interacting with students playing computer games and a practical way to monitor computer resources in large scale distributed computer centers.

Mobile Agent-Based Collaborative Computing Framework for Handling Constraint Resources

2012 ◽  
pp. 113-134
Author(s):  
Anil Kakarla ◽  
Sanjeev Agarwal ◽  
Sanjay Kumar Madria
Keyword(s):  
Information Processing ◽  
Distributed Computing ◽  
Mobile Agent ◽  
Computational Cost ◽  
Computation Time ◽  
Distributed Network ◽  
Collaborative Computing ◽  
Agent Based ◽  
Computing Framework ◽  
Viable System

Information processing and collaborative computing using agents over a distributed network of heterogeneous platforms are important for many defense and civil applications. In this chapter, a mobile agent based collaborative and distributed computing framework for network centric information processing is presented using a military application. In this environment, the challenge is to continue processing efficiently while satisfying multiple constraints like computational cost, communication bandwidth, and energy in a distributed network. The authors use mobile agent technology for distributed computing to speed up data processing using the available systems resources in the network. The proposed framework provides a mechanism to bridge the gap between computation resources and dispersed data sources under variable bandwidth constraints. For every computation task raised in the network, a viable system that has resources and data to compute the task is identified and sent to the viable system for completion. Experimental evaluation under the real platform is reported. It shows that in spite of an increase of the communication load in comparison with other solutions the proposed framework leads to a decrease of the computation time.

Data Science

2019 ◽  
pp. 219-223
Author(s):  
Karl E. Misulis ◽  
Mark E. Frisse
Keyword(s):  
Large Scale ◽  
Data Science ◽  
Healthcare Delivery ◽  
Care Quality ◽  
Data Sets ◽  
Primary Role ◽  
Patient Encounter ◽  
Massive Data Sets ◽  
Diverse Data

Data science is the study of how analytics techniques can be applied to large and diverse data sets. This field is emerging because of the availability of massive data sets in both consumer and health sectors, new machine learning and other analytics requiring large-scale computation, and the vital need to identify risk factors, trends, and other relationships not apparent when applying traditional analytics methods to smaller structured data sets. In some organizations, the primary role of a clinical informatics professional no longer is focused on how electronic health records are used in healthcare delivery but instead is focused on how patient encounter information can be collected efficiently, aggregated with information from other encounters or sources, and analyzed to improve our understanding of how population studies can improve the care of individuals. Such an understanding is critical to improving care quality and lowering healthcare costs.

scholarly journals Troubleshooting Customer Behaviour Against Merchants with Adaptive Multivariate Regression

Webology ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 462-474
Author(s):  
Marischa Elveny ◽  
Mahyuddin KM Nasution ◽  
Muhammad Zarlis ◽  
Syahril Efendi
Keyword(s):  
Business Intelligence ◽  
Fast Food ◽  
Multivariate Regression ◽  
Large Scale ◽  
Large Data ◽  
Data Sets ◽  
Sustainable Business ◽  
Customer Behaviour ◽  
Input Variables ◽  
Diverse Data

Business intelligence can be said to be techniques and tools as acquisition, transforming raw data into meaningful and useful information for business analysis purposes. This study aims to build business intelligence in optimizing large-scale data based on e-metrics. E-metrics are data created from electronic-based customer behavior. As more and more large data sets become available, the challenge of analyzing data sets will get bigger and bigger. Therefore, business intelligence is currently facing new challenges, but also interesting opportunities, where can describe in real time the needs of the market share. Optimization is done using adaptive multivariate regression that can be address high-dimensional data and produce accurate predictions of response variables and produce continuous models in knots based on the smallest GCV value, where large and diverse data are simplified and then modeled based on the level of behavior similarity, basic measurements of distances, attributes, times, places, and transactions between social actors. Customer purchases will represent each preferred behaviour and a formula can be used to calculate the score for each customer using 7 input variables. Adaptive multivariate regression looks for customer behaviour so as to get the results of cutting the deviation which is the determining factor for performance on the data. The results show there are strategies and information needed for a sustainable business. Where merchants who sell fast food or food stalls are more in demand by customers.

scholarly journals A Mobile Agent-Based System for Server Resource Monitoring

2013 ◽  
Vol 13 (4) ◽  
pp. 104-117 ◽  
Author(s):  
Zhixin Tie
Keyword(s):  
Monitoring System ◽  
Mobile Agent ◽  
Large Scale ◽  
Main Memory ◽  
Resource Monitoring ◽  
Agent Based ◽  
Computer Center ◽  
Hard Disk Space ◽  
Important Approach ◽  
Computer Centers

Abstract Mobile agent technology has become an important approach for the design and development of distributed systems. Currently, there is little research regarding the efficiency of mobile agent-based monitoring of the server resource. Based on the Mobile-C library, a mobile agent-based system called Mobile Agent- Based Server Resource Monitoring System (MABSRMS) is presented. In MABSRMS mobile agents can call low level functions in binary dynamic or static libraries, and thus can monitor server resource conveniently and efficiently. The experiment was conducted in a university computer center with hundreds of computer workstations and 15 server machines. The experiment uses the MABSRMS to detect system resources, such as available hard disk space, CPU usage and main memory usage. The experiment shows that the mobile agent-based monitoring system is a practical way to monitor server resources in large scale distributed computer centers.

A Grid Aware Large Scale Agent-based Simulation System

2011 ◽  
pp. 299-319 ◽  
Author(s):  
Dan Chen
Keyword(s):  
Quality Of Service ◽  
Large Scale ◽  
Simulation System ◽  
Data Sets ◽  
Distributed Data ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Geographically Distributed ◽  
Performance Results

The emergence of Grid technologies provide exciting new opportunities for large scale simulation over Internet, enabling collaboration and the use of distributed computing resources, while also facilitating access to geographically distributed data sets. This chapter presents HLA_Grid_RePast, a middleware platform for executing large scale collaborating RePast agent-based models on the Grid. The chapter also provides performance results and analysis on Quality of Service from a deployment of the system between UK and Singapore.

MACVE: A Mobile Agent Based Framework for Large-Scale Collaborative Virtual Environments

2007 ◽  
Vol 16 (3) ◽  
pp. 279-292 ◽  
Author(s):  
Liang Zhang ◽  
Qingping Lin
Keyword(s):  
Virtual Environments ◽  
Mobile Agent ◽  
Mobile Agents ◽  
Large Scale ◽  
Prototype System ◽  
Agent Based ◽  
Geographically Dispersed ◽  
Processing Power ◽  
System Task ◽  
Network Speed

The Collaborative Virtual Environment (CVE) is a promising technology which provides an online shared virtual world for geographically dispersed people to interact with each other. However, the scalability of existing CVE systems is limited due to the constraints in processing power and network speed of each participating host. In this paper, a mobile agent based framework for large-scale CVE, MACVE, is proposed to support a large number of concurrent participants in a CVE with a large amount of evolving virtual entities. In MACVE, the CVE system is decomposed into a group of collaborative mobile agents, each of which is responsible for an independent system task. These agents can migrate or clone dynamically at any suitable participating host including traditional servers and qualified user hosts to avoid the potential bottleneck, which can improve the scalability of CVE. Our prototype system has demonstrated the feasibility of the proposed framework.

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