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

Strategic negotiation models for grid scheduling

One of the key requirements for Grid infrastructures is the ability to share resources with nontrivial qualities of service. However, resource management in a decentralized infrastructure is a complex task as it has to cope with di erent policies and objectives of the di erent resource providers and the resource users. This problem is further complicated due to the diversity of the resource types and the heterogeneity of their local resource management systems. Agreement-based resource management can be used to address these issues because in the negotiation process of creating such bilateral service level agreements (SLAs) between Grid parties, the di erent polices of the resource providers and the users will be abstracted and observed. Such negotiation processes should be automated with no or minimal human interaction, considering the potential scale of Grid systems and the amount of necessary transactions. Therefore, strategic negotiation models play important roles. In this thesis, we have made several novel research contributions which are as follows: - An agreement based resource management approach is analyzed. Requirements for the automatic negotiation problems in Grid computing are introduced. Furthermore, related work in the areas of economics and agent communities are investigated. - Several negotiation models and negotiation strategies are proposed and examined. Simulation results demonstrate that these proposed negotiation models are suitable and e ective for Grid environments. - Firstly, a strategic negotiation model using time-based negotiation strategies is proposed and evaluated using discrete event based simulation techniques. - Secondly, time-based negotiation strategies are quite limited in the dynamically changing Grid environment because they are quite simple and static; so learning based negotiation strategies are investigated and evaluated, which are quite exible and e ective in the dynamically changing Grid environment. Also we adopted negotiation strategies considering opportunistic functions for Grid scheduling. - Thirdly, it is usually necessary that resources from di erent resource providers are co-allocated to satisfy the complex requirements of the users, so a strategic negotiation model supporting co-allocation and the tradeo between "first" and "best" agreements in the Grid computing is also proposed and evaluated. - Finally, the contributions of the current research work to the WSNegotiation protocol are analyzed

Resource Management in Grids: Overview and a discussion of a possible approach for an Agent-Based Middleware

14 pagesInternational audienceResource management and job scheduling are important research issues in computational grids. When software agents are used as resource managers and brokers in the Grid a number of additional issues and possible approaches materialize. The aim of this chapter is twofold. First, we discuss traditional job scheduling in grids, and when agents are utilized as grid middleware. Second, we use this as a context for discussion of how job scheduling can be done in the agent-based system under development

Utilization of Modified CoreGRID Ontology in an Agent-based Grid Resource Management System

isbn 978-1-880843-75-8International audienceThe Agents in Grid project is devoted to the de-velopment of an agent-based intelligent high-level Grid middleware. In the proposed system, all data process-ing is ontology-driven, and initially was based on an in-house developed mini-ontology of the Grid. Our recent analysis has indicated that we should adapt and utilize the Grid ontology developed within the framework of the CoreGRID project. This note outlines how we have modified and extended the CoreGRID ontology to fulfill the needs of our approac

ALICE - ARC integration

AliEn or Alice Environment is the Grid middleware developed and used within the ALICE collaboration for storing and processing data in a distributed manner. ARC (Advanced Resource Connector) is the Grid middleware deployed across the Nordic countries and gluing together the resources within the Nordic Data Grid Facility (NDGF). In this paper we will present our approach to integrate AliEn and ARC, in the sense that ALICE data management and job processing can be carried out on the NDGF infrastructure, using the client tools available in AliEn. The inter-operation has two aspects, one is the data management part and the second the job management aspect. The first aspect was solved by using dCache across NDGF to handle data. Therefore, we will concentrate on the second part. Solving it, was somewhat cumbersome, mainly due to the different computing models employed by AliEn and ARC. AliEN uses an Agent based pull model while ARC handles jobs through the more 'traditional' push model. The solution comes as a module implementing the functionalities necessary to achieve AliEn job submission and management to ARC enabled sites

Context-Aware Self-Organized Resource Allocation In Intelligent Water Informatics

An increasing attention of intelligent water informatics has been registered in the recent years, specifically for monitoring water distribution systems. With a combination of smart sensor network technologies and water resource management systems, the intelligent water management system will be provided more easily to acquire the context information of water distribution systems, which aids to supply on a real-time monitoring/response/distribution framework through exchanging resource information in real time. In addition, endowing smart water grids with self-organizing capabilities is instrumental in helping operators cope with smart operations and maintenance. In this paper, we investigate the water resource allocation for heterogeneous smart water grids with context information. A water resource sharing algorithm is developed for efficient managing water resource in intelligent water informatics. Given the context information of water distribution grid, the reinforcement learning scheme, namely SWG-RL, is performed by virtue of two approaches: spectral clustering method and multi-agent reinforcement learning (RL). In the proposed SWG-RL scheme, the novel spectral clustering algorithm is proposed to cluster end-users into different communities with respect to the context information, and thereafter the community is modeled as an agent, which makes the online optimal decision for water resource allocation based on its interaction with the environment context dynamically. The proposed approach is tested and the numerical results show that the significant performance gain compared to conventional static schemes

An event-based resource management framework for distributed decision-making in decentralized virtual power plants

The Smart Grid incorporates advanced information and communication technologies (ICTs) in power systems, and is characterized by high penetration of distributed energy resources (DERs). Whether it is the nation-wide power grid or a single residential building, the energy management involves different types of resources that often depend on and influence each other. The concept of virtual power plant (VPP) has been proposed to represent the aggregation of energy resources in the electricity market, and distributed decision-making (DDM) plays a vital role in VPP due to its complex nature. This paper proposes a framework for managing different resource types of relevance to energy management for decentralized VPP. The framework views VPP as a hierarchical structure and abstracts energy consumption/generation as contractual resources, i.e., contractual offerings to curtail load/supply energy, from third party VPP participants for DDM. The proposed resource models, event-based approach to decision making, multi-agent system and ontology implementation of the framework are presented in detail. The effectiveness of the proposed framework is then demonstrated through an application to a simulated campus VPP with real building energy data

Grid Resource Management Model Based on ESA

提出了基于ESA的网格资源管理模型。该模型以Service-Agent为基础,结合了Agent动态自主性和服务的松散耦合优点,运用经济学的市场机制和交易理论,提出该模型的资源发现机制、分配选择机制和交易机制,以及基于议价交易机制的Bargaining_Min_min算法,实现资源的优化管理,使资源提供者和使用者满足各自利益,达到网格市场上的双赢。A service-agent grid resource management model based on economic mechanisms is proposed.Based on Service-Agent,it combines the dynamic agent with loose coupling of services.The market mechanisms and trading theories such as resource discovery mechanism,resource allocation mechanism and Bargaining_Min_min based on bargaining mechanism,are also adopted to achieve the optimal allocation of grid resources so that providers and consumers can maximize their own interests and try to get the win-win objective in grid market.“985工程”智能化科技创新平台基金资助项