Grid Resource Management: State of the Art and Future TrendsJarek Nabrzyski, Jennifer M. Schopf, Jan Weglarz Springer Science & Business Media, 2004 - 574 Seiten Grid Resource Management: State of the Art and Future Trends presents an overview of the state of the field and describes both the real experiences and the current research available today. Grid computing is a rapidly developing and changing field, involving the shared and coordinated use of dynamic, multi-institutional resources. Grid resource management is the process of identifying requirements, matching resources to applications, allocating those resources, and scheduling and monitoring Grid resources over time in order to run Grid applications as efficiently as possible. While Grids have become almost commonplace, the use of good Grid resource management tools is far from ubiquitous because of the many open issues of the field, including the multiple layers of schedulers, the lack of control over resources, the fact that resources are shared, and that users and administrators have conflicting performance goals. These are the issues addressed in this book, in addition to elucidating the overlap with related areas including discussions of work with peer-to-peer computing, economic approaches, and operations research. Grid Resource Management: State of the Art and Future Trends is an invaluable resource for today's user, application developer, or resource owners when working with Grid resource management systems. |
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Inhalt
THE GRID IN A NUTSHELL | 3 |
TEN ACTIONS WHEN GRID SCHEDULING | 15 |
APPLICATION REQUIREMENTS FOR RESOURCE BROKERING IN A GRID ENVIRONMENT | 25 |
ATTRIBUTES FOR COMMUNICATION BETWEEN GRID SCHEDULING INSTANCES | 41 |
SECURITY ISSUES OF GRID RESOURCE MANAGEMENT | 53 |
RESOURCE MANAGEMENT IN SUPPORT OF COLLABORATIONS | 71 |
SCHEDULING IN THE GRID APPLICATION DEVELOPMENT SOFTWARE PROJECT | 73 |
WORKFLOW MANAGEMENT IN GRIPHYN | 99 |
MULTICRITERIA ASPECTS OF GRID RESOURCE MANAGEMENT | 271 |
A METAHEURISTIC APPROACH TO SCHEDULING WORKFLOW JOBS ON A GRID | 295 |
DATACENTRIC APPROACHES FOR GRID RESOURCE MANAGEMENT | 319 |
STORAGE RESOURCE MANAGERS | 321 |
NEST A GRID ENABLED STORAGE APPLIANCE | 341 |
COMPUTATION SCHEDULING AND DATA REPLICATION ALGORITHMS FOR DATA GRIDS | 359 |
QUALITY OF SERVICE QOS | 375 |
GARA A UNIFORM QUALITY OF SERVICE ARCHITECTURE | 377 |
STATE OF THE ART GRID RESOURCE MANAGEMENT | 117 |
GRID SERVICE LEVEL AGREEMENTS | 119 |
CONDOR AND PREEMPTIVE RESUME SCHEDULING | 135 |
GRID RESOURCE MANAGEMENT IN LEGION | 145 |
GRID SCHEDULING WITH MAUISILVER | 161 |
SCHEDULING ATTRIBUTES AND PLATFORM LSF | 171 |
PBS PRO GRID COMPUTING AND SCHEDULING ATTRIBUTES | 183 |
PREDICTION AND MATCHING FOR GRID RESOURCE MANAGEMENT | 191 |
PERFORMANCE INFORMATION SERVICES FOR COMPUTATIONAL GRIDS | 193 |
USING PREDICTED VARIANCE FOR CONSERVATIVE SCHEDULING ON SHARED RESOURCES | 215 |
IMPROVING RESOURCE SELECTION AND SCHEDULING USING PREDICTIONS | 237 |
THE CLASSADS LANGUAGE | 255 |
QOSAWARE SERVICE COMPOSITION FOR LARGESCALE PEERTOPEER SYSTEMS | 395 |
RESOURCE MANAGEMENT IN PEERTOPEER ENVIRONMENTS | 411 |
A PEERTOPEER APPROACH TO RESOURCE LOCATION IN GRID ENVIRONMENTS | 413 |
RESOURCE MANAGEMENT IN THE ENTROPIA SYSTEM | 431 |
RESOURCE MANAGEMENT FOR THE TRIANA PEERTOPEER SERVICES | 451 |
GRID RESOURCE COMMERCIALIZATION | 465 |
TRADING GRID SERVICES WITHIN THE UK ESCIENCE GRID | 479 |
APPLYING ECONOMIC SCHEDULING METHODS TO GRID ENVIRONMENTS | 491 |
507 | |
567 | |
Andere Ausgaben - Alle anzeigen
Grid Resource Management: State of the Art and Future Trends Jarek Nabrzyski,Jennifer M. Schopf,Jan Weglarz Eingeschränkte Leseprobe - 2012 |
Grid Resource Management: State of the Art and Future Trends Jarek Nabrzyski,Jennifer M. Schopf,Jan Weglarz Keine Leseprobe verfügbar - 2012 |
Häufige Begriffe und Wortgruppen
able addition advance algorithm allocation allows application approach architecture attributes average bandwidth called Chapter client completion components computing Condor considered created decision defined described developed distributed domain dynamic environment evaluate example execution experience expression Figure files framework function future given Grid resource guarantees implementation important instance interface layer load machine mean measurement mechanisms methods monitor multiple NeST nodes object operating owner parameters particular peer performance Platform possible prediction present problem processors protocol queue refer remote represent request requirements reservation resource management scheduling Science selection server shared simulation single solution space specified standard step storage subjob submitted task techniques tion transfer utilization workflow
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Euro-Par 2007 Workshops: Parallel Processing: HPPC 2007, UNICORE Summit 2007 ... Luc Bougé,Franck Cappello,Jack J. Dongarra,Thomas Hérault Eingeschränkte Leseprobe - 2008 |