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Message propagation is a primary means to locate resources in current unstructured peer-to-peer (P2P) systems and how to reduce effectively the redundant messages of propagation while keeping its high network coverage is what many researchers go in for in this field. In this paper, we propose the algorithm of LMPR, a scheme of improved MPR for message propagation in distributed unstructured P2P system. Based on analyzing the process and message redundancy of MPR, this paper proposes to add to such scheme a mechanism of specified relay-list to reduce further the number of its retransmitting of message. By attaching to each relay-peer a relay-list with peers it will relay message to, the scheme can assure that in the view of each decision-peer a broadcast message can be dispatched and only be dispatched once to every neighbor peer within 2 hops of it. Simulation results show that the scheme of LMPR has better performance than that of MPR in message redundancy, network overhead, and fluctuation brought to the system. [ABSTRACT FROM AUTHOR]

In recent years we have witnessed strong development and widespread use of powerful wirelessly connected platforms, thus the set of the related problems that need to be solved by distributed algorithms is growing rapidly. Some of them present large obstacles in harnessing the full potential of this new technology, so there is an imminent need for a fast and easy evaluation of new ideas and approaches. Simulation is a fundamental part of distributed algorithm design and evaluation process. In this paper, we present a library for event-based simulation and evaluation of distributed algorithms. This library provides a set of simple but powerful tools with a goal to ease virtual setup of a complex system such as a distributed network of communicating entities and to define, simulate, and analyze its behavior. In order to reduce a huge problem space inherent in such systems, our library is using a high level of abstraction. This is made possible by a strict and complete definition of the distributed computing environment. The library is implemented in Python whose simple and expressive syntax provides a possibility of minimal implementations and a mild learning curve. In addition to executing automated simulations or larger experiments, the library fully supports interactive mode along with a step-by-step execution, which can be a very powerful combination. [ABSTRACT FROM AUTHOR]