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.

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.

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.

Autonomic configuration and recovery in a mobile agent-based distributed event monitoring system

2007 ◽  
Vol 37 (5) ◽  
pp. 493-522 ◽  
Author(s):  
Anand R. Tripathi ◽  
Devdatta Kulkarni ◽  
Harsha Talkad ◽  
Muralidhar Koka ◽  
Sandeep Karanth ◽  
...  
Keyword(s):  
Monitoring System ◽  
Mobile Agent ◽  
Event Monitoring ◽  
Agent Based

scholarly journals Predicting Hard Disk Failure by Means of Automatized Labeling and Machine Learning Approach

2021 ◽  
Vol 11 (18) ◽  
pp. 8293
Author(s):  
Federico Gargiulo ◽  
Dirk Duellmann ◽  
Pasquale Arpaia ◽  
Rosario Schiano Lo Moriello
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Dominant Role ◽  
Storage System ◽  
Supervised Machine Learning ◽  
Large Set ◽  
Production Environment ◽  
Hard Drives ◽  
Computer Center ◽  
Computer Centers

Today, cloud systems provide many key services to development and production environments; reliable storage services are crucial for a multitude of applications ranging from commercial manufacturing, distribution and sales up to scientific research, which is often at the forefront of computing resource demands. In large-scale computer centers, the storage system requires particular attention and investment; usually, a large number of diverse storage devices need to be deployed in order to match the varying performance and volume requirements of changing user applications. As of today, magnetic drives still play a dominant role in terms of deployed storage volume and of service outages due to device failure. In this paper, we study methods to facilitate automated proactive disk replacement. We propose a method to identify disks with media failures in a production environment and describe an application of supervised machine learning to predict disk failures. In particular, a proper stage to automatically label (healthy/at-risk) the disks during the training and validation stage is presented along with tuning strategy to optimize the hyperparameters of the associated machine learning classifier. The approach is trained and validated against a large set of 65,000 hard drives in the CERN computer center, and the achieved results are discussed.

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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