This research aims at designing and building a scheduling framework for distributed computing systems with the primary objectives of providing fast response times to the users, delivering high system throughout and accommodating maximum number of applications into the systems. The author claims that the above mentioned objectives are the most important objectives for scheduling in recent distributed computing systems, especially Grid computing environments. In order to achieve the objectives of the scheduling framework, the scheduler employs arbitration of application-level schedules and preemption of executing jobs under certain conditions. In application-level scheduling, the user develops a schedule for his application using an execution model that simulates the execution behavior of the application. Since application-level scheduling can seriously impede the performance of the system, the scheduling framework developed in this research arbitrates between different application-level schedules corresponding to different applications to provide fair system usage for all applications and balance the interests of different applications. In this sense, the scheduling framework is not a classical scheduling system, but a meta-scheduling system that interacts with the application-level schedulers. Due to the large system dynamics involved in Grid computing systems, the ability to preempt executing jobs becomes a necessity. The meta-scheduler described in this dissertation employs well defined scheduling policies to preempt and migrate executing applications. In order to provide the users with the capability to make their applications preemptible, a user-level checkpointing library called SRS (Stop-Restart Software) was also developed by this research. The SRS library is different from many user-level checkpointing libraries since it allows reconfiguration of applications between migrations. This reconfiguration can be achieved by changing the processor configuration and/or data distribution. The experimental results provided in this dissertation demonstrates the utility of the metascheduling framework for distributed computing systems. And lastly, the metascheduling framework was put to practical use by building a Grid computing system called GradSolve. GradSolve is a flexible system and it allows the application library writers to upload applications with different capabilities into the system. GradSolve is also unique with respect to maintaining traces of the execution of the applications and using the traces for subsequent executions of the application.

This volume presents meta-heuristics approaches for Grid scheduling problems. It brings new ideas, analysis, implementations and evaluation of meta-heuristic techniques for Grid scheduling, which make this volume novel in several aspects.

Next-generation distributed systems will support continuous media (digital audio and video) in the same hardware/software framework as other data. Many applications that use continous media (CM) have end- to-end performance requirements such as minimum throughput or maximum delay. To reliably support these requirements, system components such as CPU schedulers, networks, and file systems must offer realtime semantics. A meta-scheduler coordinates these components, negotiating end-to-end guarantees on behalf of clients. The CM-resource model, described in this paper, provides a basis for such a meta- scheduler. The model defines a workload parameterization, an abstract interface to resources, and an end-to-end algorithm for negotiated reservation of multiple resources, and an end-to-end algorithm for negotiated reservation of multiple resources; the division of delay is based on an economic model. Clients make reservations for worst-case workload and resources offer hard delay bounds. However, system components may "work ahead" within limits, increasing the responsiveness of bursty non-realtime workload.

This book approaches the grid computing with a perspective on the latest achievements in the field, providing an insight into the current research trends and advances, and presenting a large range of innovative research papers. The topics covered in this book include resource and data management, grid architectures and development, and grid-enabled applications. New ideas employing heuristic methods from swarm intelligence or genetic algorithm and quantum encryption are considered in order to explain two main aspects of grid computing: resource management and data management. The book addresses also some aspects of grid computing that regard architecture and development, and includes a diverse range of applications for grid computing, including possible human grid computing system, simulation of the fusion reaction, ubiquitous healthcare service provisioning and complex water systems.

This book focuses on the future directions of the static scheduling and dynamic load balancing methods in parallel and distributed systems. It provides an overview and a detailed discussion of a wide range of topics from theoretical background to practical, state-of-the-art scheduling and load balancing techniques.