Jayadev Misra

Bio: Jayadev Misra is an academic researcher from University of Texas at Austin. The author has contributed to research in topics: Concurrency & Deadlock. The author has an hindex of 40, co-authored 115 publications receiving 8583 citations.

Distributed Simulation: A Case Study in Design and Verification of Distributed Programs

Abstract: The problem of system simulation is typically solved in a sequential manner due to the wide and intensive sharing of variables by all parts of the system. We propose a distributed solution where processes communicate only through messages with their neighbors; there are no shared variables and there is no central process for message routing or process scheduling. Deadlock is avoided in this system despite the absence of global control. Each process in the solution requires only a limited amount of memory. The correctness of a distributed system is proven by proving the correctness of each of its component processes and then using inductive arguments. The proposed solution has been empirically found to be efficient in preliminary studies. The paper presents formal, detailed proofs of correctness.

Distributed discrete-event simulation

Abstract: Traditional discrete-event simulations employ an inherently sequential algorithm. In practice, simulations of large systems are limited by this sequentiality, because only a modest number of events can be simulated. Distributed discrete-event simulation (carried out on a network of processors with asynchronous message-communicating capabilities) is proposed as an alternative; it may provide better performance by partitioning the simulation among the component processors. The basic distributed simulation scheme, which uses time encoding, is described. Its major shortcoming is a possibility of deadlock. Several techniques for deadlock avoidance and deadlock detection are suggested. The focus of this work is on the theory of distributed discrete-event simulation.

Asynchronous distributed simulation via a sequence of parallel computations

Abstract: An approach to carrying out asynchronous, distributed simulation on multiprocessor messagepassing architectures is presented. This scheme differs from other distributed simulation schemes because (1) the amount of memory required by all processors together is bounded and is no more than the amount required in sequential simulation and (2) the multiprocessor network is allowed to deadlock, the deadlock is detected, and then the deadlock is broken. Proofs for the correctness of this approach are outlined.

Proofs of Networks of Processes

Abstract: We present a proof method for networks of processes in which component processes communicate exclusively through messages We show how to construct proofs of invariant properties which hold at all times during network computation, and terminal properties which hold upon termination of network computation, if network computation terminates The proof method is based upon specifying a process by a pair of assertions, analogous to pre-and post-conditions in sequential program proving The correctness of network specification is proven by applying inference rules to the specifications of component processes Several examples are proved using this technique

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

Abstract: From the Publisher: Probabilistic Reasoning in Intelligent Systems is a complete andaccessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic. The author distinguishes syntactic and semantic approaches to uncertainty—and offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognition—in short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information. Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.

Distributed algorithms

Abstract: In Distributed Algorithms, Nancy Lynch provides a blueprint for designing, implementing, and analyzing distributed algorithms. She directs her book at a wide audience, including students, programmers, system designers, and researchers. Distributed Algorithms contains the most significant algorithms and impossibility results in the area, all in a simple automata-theoretic setting. The algorithms are proved correct, and their complexity is analyzed according to precisely defined complexity measures. The problems covered include resource allocation, communication, consensus among distributed processes, data consistency, deadlock detection, leader election, global snapshots, and many others. The material is organized according to the system model-first by the timing model and then by the interprocess communication mechanism. The material on system models is isolated in separate chapters for easy reference. The presentation is completely rigorous, yet is intuitive enough for immediate comprehension. This book familiarizes readers with important problems, algorithms, and impossibility results in the area: readers can then recognize the problems when they arise in practice, apply the algorithms to solve them, and use the impossibility results to determine whether problems are unsolvable. The book also provides readers with the basic mathematical tools for designing new algorithms and proving new impossibility results. In addition, it teaches readers how to reason carefully about distributed algorithms-to model them formally, devise precise specifications for their required behavior, prove their correctness, and evaluate their performance with realistic measures. Table of Contents 1 Introduction 2 Modelling I; Synchronous Network Model 3 Leader Election in a Synchronous Ring 4 Algorithms in General Synchronous Networks 5 Distributed Consensus with Link Failures 6 Distributed Consensus with Process Failures 7 More Consensus Problems 8 Modelling II: Asynchronous System Model 9 Modelling III: Asynchronous Shared Memory Model 10 Mutual Exclusion 11 Resource Allocation 12 Consensus 13 Atomic Objects 14 Modelling IV: Asynchronous Network Model 15 Basic Asynchronous Network Algorithms 16 Synchronizers 17 Shared Memory versus Networks 18 Logical Time 19 Global Snapshots and Stable Properties 20 Network Resource Allocation 21 Asynchronous Networks with Process Failures 22 Data Link Protocols 23 Partially Synchronous System Models 24 Mutual Exclusion with Partial Synchrony 25 Consensus with Partial Synchrony

Linearizability: a correctness condition for concurrent objects

Abstract: A concurrent object is a data object shared by concurrent processes. Linearizability is a correctness condition for concurrent objects that exploits the semantics of abstract data types. It permits a high degree of concurrency, yet it permits programmers to specify and reason about concurrent objects using known techniques from the sequential domain. Linearizability provides the illusion that each operation applied by concurrent processes takes effect instantaneously at some point between its invocation and its response, implying that the meaning of a concurrent object's operations can be given by pre- and post-conditions. This paper defines linearizability, compares it to other correctness conditions, presents and demonstrates a method for proving the correctness of implementations, and shows how to reason about concurrent objects, given they are linearizable.