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Author: B. Clifford Neuman Publisher: ISBN: Category : Computer networks Languages : en Pages : 16
Book Description
Abstract: "Multiprocessor systems should exist in the larger context of distributed systems, allowing multiprocessor resources to be shared by those that need them. Unfortunately, typical multiprocessor resource management techniques do not scale to large networks. The Prospero Resource Manager (PRM) is a scalable resource allocation system that supports the allocation of processing resources in large networks and multiprocessor systems. To manage resources in such distributed parallel systems, PRM employs three types of managers: system managers, job managers, and node managers. There exist multiple independent instances of each type of manager, reducing bottlenecks. The complexity of each manager is further reduced because each is designed to utilize information at an appropriate level of abstraction."
Author: B. Clifford Neuman Publisher: ISBN: Category : Computer networks Languages : en Pages : 16
Book Description
Abstract: "Multiprocessor systems should exist in the larger context of distributed systems, allowing multiprocessor resources to be shared by those that need them. Unfortunately, typical multiprocessor resource management techniques do not scale to large networks. The Prospero Resource Manager (PRM) is a scalable resource allocation system that supports the allocation of processing resources in large networks and multiprocessor systems. To manage resources in such distributed parallel systems, PRM employs three types of managers: system managers, job managers, and node managers. There exist multiple independent instances of each type of manager, reducing bottlenecks. The complexity of each manager is further reduced because each is designed to utilize information at an appropriate level of abstraction."
Author: Shikharesh Majumdar Publisher: John Wiley & Sons ISBN: 1119912954 Category : Computers Languages : en Pages : 324
Book Description
Comprehensive guide to the principles, algorithms, and techniques underlying resource management for clouds, big data, and sensor-based systems Resource Management on Distributed Systems provides helpful guidance by describing algorithms and techniques for managing resources on parallel and distributed systems, including grids, clouds, and parallel processing-based platforms for big data analytics. The book focuses on four general principles of resource management and their impact on system performance, energy usage, and cost, including end-of-chapter exercises. The text includes chapters on sensors, autoscaling on clouds, complex event processing for streaming data, and data filtering techniques for big data systems. The book also covers results of applying the discussed techniques on simulated as well as real systems (including clouds and big data processing platforms), and techniques for handling errors associated with user predicted task execution times. Written by a highly qualified academic with significant research experience in the field, Resource Management on Distributed Systems includes information on sample topics such as: Attributes of parallel/distributed applications that have an intimate relationship with system behavior and performance, plus their related performance metrics. Handling a lack of a prior knowledge of local operating systems on individual nodes in a large system. Detection and management of complex events (that correspond to the occurrence of multiple raw events) on a platform for streaming analytics. Techniques for reducing data latency for multiple operator-based queries in an environment processing large textual documents. With comprehensive coverage of core topics in the field, Resource Management on Distributed Systems is a comprehensive guide to resource management in a single publication and is an essential read for professionals, researchers and students working with distributed systems.
Author: Shikharesh Majumdar Publisher: John Wiley & Sons ISBN: 1119912938 Category : Computers Languages : en Pages : 324
Book Description
Comprehensive guide to the principles, algorithms, and techniques underlying resource management for clouds, big data, and sensor-based systems. Resource Management on Distributed Systems provides helpful guidance on resource management questions by describing algorithms and techniques for managing resources on parallel and distributed systems, including grids, clouds, and parallel processing-based platforms for big data analytics. The book introduces four general principles of resource management with a discussion of their impact on system performance, energy usage, and cost, and includes a set of exercises at the end of a chapter. To provide extensive coverage of the subject, the text includes chapters on sensors, autoscaling on clouds, complex event processing for streaming data, and data filtering techniques for big data systems. The book also covers results of applying the discussed techniques on simulated as well as real systems (including clouds and big data processing platforms), and techniques for handling errors associated with user predicted task execution times. Written by a highly qualified academic with significant research experience in the field, Resource Management on Distributed Systems includes information on sample topics such as: Attributes of parallel/distributed applications that have an intimate relationship with system behavior and performance, plus their related performance metrics. Handling a lack of a prior knowledge of local operating systems on individual nodes in a large system. Detection and management of complex events (that correspond to the occurrence of multiple raw events) on a platform for streaming analytics. Techniques for reducing data latency for multiple operator-based queries in an environment processing large textual documents. With comprehensive coverage of core topics in the field, Resource Management on Distributed Systems is a comprehensive guide to resource management in a single publication and is an essential read for professionals, researchers and students working with distributed systems.
Author: Zoltan Juhasz Publisher: Springer Science & Business Media ISBN: 0387230963 Category : Computers Languages : en Pages : 211
Book Description
DAPSY (Austrian-Hungarian Workshop on Distributed and Parallel Systems) is an international conference series with biannual events dedicated to all aspects of distributed and parallel computing. DAPSY started under a different name in 1992 (Sopron, Hungary) as regional meeting of Austrian and Hungarian researchers focusing on transputer-related parallel computing; a hot research topic of that time. A second workshop followed in 1994 (Budapest, Hungary). As transputers became history, the scope of the workshop widened to include parallel and distributed systems in general and the 1st DAPSYS in 1996 (Miskolc, Hungary) reflected the results of these changes. Distributed and Parallel Systems: Cluster and Grid Computing is an edited volume based on DAPSYS, 2004, the 5th Austrian-Hungarian Workshop on Distributed and Parallel Systems. The workshop was held in conjunction with EuroPVM/MPI-2004, Budapest, Hungary September 19-22, 2004.
Author: Dror G. Feitelson Publisher: Springer Science & Business Media ISBN: 3540666761 Category : Business & Economics Languages : en Pages : 243
Book Description
This book constitutes the thoroughly refereed post-workshop proceedings of the 5th International Workshop on Job Scheduling Strategies for Parallel Processing, JSSPP'99, held in San Juan, Puerto Rico, in April 1999, as a satelite meeting of IPPS/SPDP'99. The 12 revised full papers have been through an iterated reviewing process and present the state of the art in the area.
Author: International Business Machines Corporation. Research Division Publisher: ISBN: Category : Parallel programming (Computer science) Languages : en Pages : 8
Book Description
Abstract: "Large scale parallel systems are usually shared by many users with varying demands. The traditional scheme for supporting multiprogramming has been to statically divide the resources of a parallel system into independent partitions and assign one partition to each parallel job. These partitions are then fixed in size for the duration of each job, causing two severe problems: the operating system cannot dynamically reconfigure the system to increase overall throughput, and the user applications cannot resize the corresponding partitions to take advantage of different amounts of parallelism along each phase of computation. The Distributed Resource Management System (DRMS) is presented as an approach to allow dynamic scheduling of resources during the execution of parallel programs. By establishing well-defined interactions between the executing job and the parallel system, it supports dynamic reconfiguration of processor partitions, dynamic distribution and redistribution of data, communication among cooperating applications, and various other monitoring actions. The interactions occur only at specific points in the execution of the program where the aforementioned operations can be performed efficiently."
Author: B. Clifford Neuman
Author: B. Clifford Neuman
Author: Shikharesh Majumdar
Author: 
Author: Shikharesh Majumdar
Author: Zoltan Juhasz
Author: Ashfāq Aḥmad
Author: Dror G. Feitelson
Author: Anwesha Mukherjee
Author: 
Author: International Business Machines Corporation. Research Division