Cooperative network supercomputing is becoming increasingly popular for harnessing the power of
the global Internet computing platform. A typical Internet supercomputer consists of a master
computer or server and a large number of computers called workers performing computation on
behalf of the master. Despite the simplicity and benefits of a single master approach as the
scale of such computing environments grows it becomes unrealistic to assume the existence of
the infallible master that is able to coordinate the activities of multitudes of workers.
Large-scale distributed systems are inherently dynamic and are subject to perturbations such
as failures of computers and network links thus it is also necessary to consider fully
distributed peer-to-peer solutions. We present a study of cooperative computing with the focus
on modeling distributed computing settings algorithmic techniques enabling one to combine
efficiency and fault-tolerance in distributed systems and the exposition of trade-offs between
efficiency and fault-tolerance for robust cooperative computing. The focus of the exposition is
on the abstract problem called Do-All and formulated in terms of a system of cooperating
processors that together need to perform a collection of tasks in the presence of adversity.
Our presentation deals with models algorithmic techniques and analysis. Our goal is to
present the most interesting approaches to algorithm design and analysis leading to many
fundamental results in cooperative distributed computing. The algorithms selected for inclusion
are among the most efficient that additionally serve as good pedagogical examples. Each chapter
concludes with exercises and bibliographic notes that include a wealth of references to related
work and relevant advanced results. Table of Contents: Introduction Distributed Cooperation
and Adversity Paradigms and Techniques Shared-Memory Algorithms Message-Passing
Algorithms The Do-All Problem in Other Settings Bibliography Authors' Biographies
