Status based resource discovery in computational grids.

Resource discovery is an important aspect of Computational Grids. Locating resources in a Grid environment is difficult because of the geographic dispersion and dynamic nature of its resources. Issues such as large numbers of users, heterogeneous resources and dynamic status of resources over time in a large distributed network make resource discovery more difficult than the case of traditional networks. In the case of Computational Grids, additional issues such as different operating systems, different administrative domains and lack of portability between platform dependent applications make resource discovery even more difficult. Further to all these difficult issues, knowledge of the current status of the resources adds an extra challenge to the problem of finding resources in the Grids. An ideal Computational Grid environment should contain a resource discovery infrastructure that includes heterogeneous resource monitoring capabilities. These capabilities will save time and the risk of selecting inappropriate resources. In this thesis work, we propose a resource discovery infrastructure in the form of an automated status monitoring model. The model consists of two fundamental aspects, a portable data model and a set of executable monitoring components. Our approach adheres to principles of software design, is well structured and platform independent. The portable data model, which conveys the status of the resources, must be understandable by any application software, agent or scheduler on any platform. In turn, the monitors must be able to acquire necessary status information from various, diverse systems and maintain the data model. We developed appropriate interfaces that provide straightforward connectivity between our infrastructure and other Grid middleware components being developed elsewhere.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .A38. Source: Masters Abstracts International, Volume: 44-01, page: 0381. Thesis (M.Sc.)--University of Windsor (Canada), 2005

Agent-based resource management for grid computing

A computational grid is a hardware and software infrastructure that provides dependable, consistent, pervasive, and inexpensive access to high-end computational capability. An ideal grid environment should provide access to the available resources in a seamless manner. Resource management is an important infrastructural component of a grid computing environment. The overall aim of resource management is to efficiently schedule applications that need to utilise the available resources in the grid environment. Such goals within the high performance community will rely on accurate performance prediction capabilities. An existing toolkit, known as PACE (Performance Analysis and Characterisation Environment), is used to provide quantitative data concerning the performance of sophisticated applications running on high performance resources. In this thesis an ASCI (Accelerated Strategic Computing Initiative) kernel application, Sweep3D, is used to illustrate the PACE performance prediction capabilities. The validation results show that a reasonable accuracy can be obtained, cross-platform comparisons can be easily undertaken, and the process benefits from a rapid evaluation time. While extremely well-suited for managing a locally distributed multi-computer, the PACE functions do not map well onto a wide-area environment, where heterogeneity, multiple administrative domains, and communication irregularities dramatically complicate the job of resource management. Scalability and adaptability are two key challenges that must be addressed. In this thesis, an A4 (Agile Architecture and Autonomous Agents) methodology is introduced for the development of large-scale distributed software systems with highly dynamic behaviours. An agent is considered to be both a service provider and a service requestor. Agents are organised into a hierarchy with service advertisement and discovery capabilities. There are four main performance metrics for an A4 system: service discovery speed, agent system efficiency, workload balancing, and discovery success rate. Coupling the A4 methodology with PACE functions, results in an Agent-based Resource Management System (ARMS), which is implemented for grid computing. The PACE functions supply accurate performance information (e. g. execution time) as input to a local resource scheduler on the fly. At a meta-level, agents advertise their service information and cooperate with each other to discover available resources for grid-enabled applications. A Performance Monitor and Advisor (PMA) is also developed in ARMS to optimise the performance of the agent behaviours. The PMA is capable of performance modelling and simulation about the agents in ARMS and can be used to improve overall system performance. The PMA can monitor agent behaviours in ARMS and reconfigure them with optimised strategies, which include the use of ACTs (Agent Capability Tables), limited service lifetime, limited scope for service advertisement and discovery, agent mobility and service distribution, etc. The main contribution of this work is that it provides a methodology and prototype implementation of a grid Resource Management System (RMS). The system includes a number of original features that cannot be found in existing research solutions

A peer-to-peer service architecture for the Smart Grid

Short paperThe Smart Grid vision needs to address hard challenges such as interoperability, reliability and scalability before it can become fulfilled. The need to provide full interoperability between current and future energy and non-energy systems and its disparate technologies along with the problem of seamless discovery, configuration, and communication of a large variety of networked devices ranging from the resource constrained sensing devices to the large machines inside a data center requires an agnostic Service Oriented Architecture. Moreover, the sheer scale of the Smart Grid and the criticality of the communication among its subsystems for proper management, demands a scalable and reliable communication framework able to work in an heterogeneous and dynamic environment. In this position paper, we propose a generic framework, based on Web Services for interoperability, and epidemic or gossip based communication protocols for reliability and scalability, that can serve a general management substrate where several Smart Grid problems can be solved. We illustrate the flexibility of the proposed framework by showing how it can be used in two specific scenarios.Important challenges in interoperability, reliability, and scalability need to be addressed before the Smart Grid vision can be fulfilled. The sheer scale of the electric grid and the criticality of the communication among its subsystems for proper management, demands a scalable and reliable communication framework able to work in an heterogeneous and dynamic environment. Moreover, the need to provide full interoperability between diverse current and future energy and non-energy systems, along with seamless discovery and configuration of a large variety of networked devices, ranging from the resource constrained sensing devices to servers in data centers, requires an implementation-agnostic Service Oriented Architecture. In this position paper we propose that this challenge can be addressed with a generic framework that reconciles the reliability and scalability of Peer-to-Peer systems, with the industrial standard interoperability of Web Services. We illustrate the flexibility of the proposed framework by showing how it can be used in two specific scenarios

Towards a Semantic Grid Computing Platform for Disaster Management in Built Environment

Current disaster management procedures rely primarily on heuristics which result in their strategies being very cautious and sub-optimum in terms of saving life, minimising damage and returning the building to its normal function. Also effective disaster management demands decentralized, dynamic, flexible, short term and across domain resource sharing, which is not well supported by existing distributing computing infrastructres. The paper proposes a conceptual framework for emergency management in the built environment, using Semantic Grid as an integrating platform for different technologies. The framework supports a distributed network of specialists in built environment, including structural engineers, building technologists, decision analysts etc. It brings together the necessary technology threads, including the Semantic Web (to provide a framework for shared definitions of terms, resources and relationships), Web Services (to provide dynamic discovery and integration) and Grid Computing (for enhanced computational power, high speed access, collaboration and security control) to support rapid formation of virtual teams for disaster management. The proposed framework also make an extensive use of modelling and simulation (both numerical and using visualisations), data mining (to find resources in legacy data sets) and visualisation. It also include a variety of hardware instruments with access to real time data. Furthermore the whole framework is centred on collaborative working by the virtual team. Although focus of this paper is on disaster management, many aspects of the discussed Grid and Visualisation technologies will be useful for any other forms of collaboration. Conclusions are drawn about the possible future impact on the built environment

Self-organising management of Grid environments

This paper presents basic concepts, architectural principles and algorithms for efficient resource and security management in cluster computing environments and the Grid. The work presented in this paper is funded by BTExacT and the EPSRC project SO-GRM (GR/S21939)