Active Monitoring in Grid Environments Using Mobile Agent Technology (original) (raw)
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Lecture Notes in Computer Science, 2000
We present the design and implementation of an infrastructure that enables monitoring of resources, services, and applications in a computational grid and provides a toolkit to help manage these entities when faults occur. This infrastructure builds on three basic monitoring components: sensors to perform measurements, actuators to perform actions, and an event service to communicate events between remote processes. We describe how we apply our infrastructure to support a grid service and an application: (1) the Globus Metacomputing Directory Service; and (2) a long-running and coarse-grained parameter study application. We use these application to show that our monitoring infrastructure is highly modular, conveniently retargettable, and extensible.
c ○ 2007 SWPS AN AGENT-BASED APPROACH TO GRID SERVICE MONITORING
2011
Abstract. The centralised management of distributed computing infrastructures presents a number of considerable challenges, not least of which is the effective monitoring of physical resources and middleware components to provide an accurate operational picture for use by administrative or management staff. The detection and presentation of real-time information pertaining to the performance and availability of computing resources is a difficult yet critical activity. This architecture is intended to enhance the service monitoring experience of a Grid operations team. We have designed and implemented an extensible agent-based architecture capable of detecting and aggregating status information using low-level sensors, functionality tests and existing information systems. To date it has been successfully deployed across eighteen Grid-Ireland sites.
Mobile Agent based Automated Deployment of Resource Monitoring Service in Grid
This paper discusses a novel approach for automated deployment of resource monitoring service for job submission in grid environment. Grid computing is used for solving large scale problems which are complex. Monitoring becomes a crucial model in Grid which is used for scheduling, fault detection, accounting, etc. Job monitoring is required because user has no direct control over the job when it is submitted to a remote node. Monitoring requires services to be deployed on all the nodes in a grid environment to predict the resource availability. It is difficult to deploy the monitoring service manually in geographically distributed nodes. Hence a need for automated deployment arises. Our approach takes less time to deploy when compared to the manual deployment. The mobile agents do the automated deployment with minimum deployment time which utilizes minimum bandwidth in turn reduces the network load.
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Grid computing is a technology for distributed computing. To manage a large scale of Grid resources for dynamic access, resource management is a key component. In this paper, a Grid Resource Information Monitoring (GRIM) prototype is introduced. To support the constantly changing resource states in the GRIM prototype, the push-based data delivery protocol named Grid Resource Information Retrieving (GRIR) is provided.There is a trade-off between information fidelity and updating transmission cost. The more frequent the reporting is, the more precise the information will be. But there will be more overheads. The offset-sensitive mechanism, the time-sensitive mechanism, and the hybrid mechanism in GRIR are used to achieve a high degree of data accuracy while decreasing the cost of updating messages. Experimental results show that the proposal alleviates both the update transmission cost and the loss of data accuracy compared to prior methods.
The MonALISA (Monitoring Agents in A Large Integrated Services Architecture) system provides a distributed service architecture which is used to collect and process monitoring information. While its initial target field of application is networks and Grid systems supporting data processing and analysis for global high energy and nuclear physics collaborations, MonALISA is broadly applicable to many fields of "data intensive" science, and to the monitoring and management of major research and education networks. MonALISA is based on a scalable Dynamic Distributed Services Architecture), and is implemented in Java using JINI and WSDL technologies. The scalability of the system derives from the use of a multi threaded engine to host a variety of loosely coupled self-describing dynamic services, the ability of each service to register itself and then to be discovered and used by any other services, or clients that require such information. The framework integrates many existing monitoring tools and procedures to collect parameters describing computational nodes, applications and network performance. Specialized mobile agents are used in the MonALISA framework to perform global optimization tasks or help and improve the operation of large distributed system by performing supervising tasks for different applications or real time parameters. MonALISA is currently running around the clock monitoring several Grids and distributed applications on around 160 sites.
A Monitoring Sensor Management System for Grid Environments
2000
Large distributed systems such as Computational Grids require a large amount of monitoring data be collected for a variety of tasks such as fault detection, performance analysis, performance tuning, performance prediction, and scheduling. Ensuring that all necessary monitoring is turned on and that data is being collected can be a very tedious and error-prone task. We have developed an agent-based system to automate the execution of monitoring sensors and the collection of event data.
A Scalable and Performant Grid Monitoring Framework
Distributed resource property repositories and state monitoring systems are critical components of any Grid Management Architecture, providing Grid scheduler, job/execution manager and state estimation components with accurate information about network, computational and storage resource properties and status. Without an upto-date information and monitoring service, intelligent scheduling decision making would be a nearimpossible task. In this paper we describe a scalable, portable and non-intrusive Grid Information and Monitoring framework. We compare it to wellknown Grid information & monitoring systems, and measure its performance to the Globus 2.2 Monitoring and Discovery Service (MDS) and the Globus 3.2 web services Information Service (WS-IS) performance.
Agent-Based Resource Management for Grid Computing
2nd IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID'02), 2002
Resource management is an important component of a grid computing infrastructure. The scalability and adaptability of such systems are two key challenges that must be addressed. In this work an agent-based resource management system, ARMS, is implemented for grid computing. ARMS utilises the performance prediction techniques of the PACE toolkit to provide quantitative data regarding the performance of complex applications running on a local grid resource. At the meta-level, a hierarchy of homogeneous agents are used to provide a scalable and adaptable abstraction of the system architecture. Each agent is able to cooperate with other agents and thereby provide service advertisement and discovery for the scheduling of applications that need to utilise grid resources. A case study with corresponding experimental results is included to demonstrate the efficiency of the resource management and scheduling system.
A Hierarchical Agent Framework for Tuning Application Performance in Grid Environment
2007
Performance analysis on a conventional distributed system consisting of a static (usually homogeneous) pool of computational resources comprises various techniques related to instrumentation, data collection, measurement, analysis and visualization. However, in a heterogeneous, dynamic environment, like grid, post-mortem analysis is of no use and data needs to be collected and analyzed in real time. Novel techniques are also required for dynamically tuning the application's performance and resource brokering in order to maintain the desired QoS. The objective of this paper is to present an agent framework for performance monitoring and analysis of applications running in grid environment and implementation of local tuning techniques for improving their performances. Results of a preliminary implementation are also presented in order to demonstrate the effectiveness of the agent framework.