Fatos Xhafa Livres

This book covers the latest advances in the rapid growing field of inter-cooperative collective intelligence aiming the integration and cooperation of various computational resources, networks and intelligent processing paradigms to collectively build intelligence and advanced decision support and interfaces for end-users. The book brings a comprehensive view of the state-of-the-art in the field of integration of sensor networks, IoT and Cloud computing, massive and intelligent querying and processing of data. As a result, the book presents lessons learned so far and identifies new research issues, challenges and opportunities for further research and development agendas. Emerging areas of applications are also identified and usefulness of inter-cooperative collective intelligence is envisaged.   Researchers, software developers, practitioners and students interested in the field of inter-cooperative collective intelligence will find the comprehensive coverage of this book useful for their research, academic, development and practice activity.  

Grid computing has emerged as one of the most promising computing paradigms of the new millennium! Achieving high performance Grid computing requires techniques to efficiently and adaptively allocate jobs and applications to available resources in a large scale, highly heterogenous and dynamic environment. This volume presents meta-heuristics approaches for Grid scheduling problems. Due to the complex nature of the problem, meta-heuristics are primary techniques for the design and implementation of efficient Grid schedulers. The volume brings new ideas, analysis, implementations and evaluation of meta-heuristic techniques for Grid scheduling, which make this volume novel in several aspects. The 14 chapters of this volume have identified several important formulations of the problem, which we believe will serve as a reference for the researchers in the Grid computing community. Important features include the detailed overview of the various novel metaheuristic scheduling approaches, excellent coverage of timely, advanced scheduling topics, state-of-the-art theoretical research and application developments and chapters authored by pioneers in the field. Academics, scientists as well as engineers engaged in research, development and scheduling will find the comprehensive coverage of this book invaluable.

Over recent decades, scheduling has emerged as a pivotal optimization problem and remains an active research area. It manifests in various scientific, engineering, and industrial contexts, adapting to the specific restrictions and optimization criteria of each environment. In optimization and computer science, scheduling is defined as the allocation of tasks to resources over time to achieve optimality in one or more objective criteria efficiently. In production, it refers to the planning of operations to ensure jobs progress through machines optimally according to certain criteria. While a standardized form exists for stating scheduling problems—efficiently allocating n jobs on m machines, each processing one activity at a time to optimize job completion times—scheduling encompasses a diverse array of problems. Several parameters influence the problem definition, including job characteristics (such as preemptiveness, precedence constraints, and release dates), resource environments (single or parallel machines, unrelated or identical machines), optimization criteria (minimizing tardiness, late jobs, makespan, flowtime, or maximizing resource utilization), and scheduling environments (static vs. dynamic, where job numbers and characteristics may change over time).