Search

This paper studies the complexity of distributed construction of purely additive spanners in the CONGEST model. We describe algorithms for building such spanners in several cases. Because of the need to simultaneously make decisions at far apart locations, the algorithms use additional mechanisms compared to their sequential counterparts. We complement our algorithms with a lower bound on the number of rounds required for computing pairwise spanners. The standard reductions from set-disjointness and equality seem unsuitable for this task because no specific edge needs to be removed from the graph. Instead, to obtain our lower bound, we define a new communication complexity problem that reduces to computing a sparse spanner, and prove a lower bound on its communication complexity. This technique significantly extends the current toolbox used for obtaining lower bounds for the CONGEST model, and we believe it may find additional applications. [ABSTRACT FROM AUTHOR]

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]

It is anticipated that a large amount of multicast traffic needs to be supported in future communication networks. The network coding technique proposed recently is promising for establishing multicast connections with a significantly lower bandwidth requirement than that of traditional Steiner-tree-based multicast connections. How to design multicast network topologies with the consideration of efficiently supporting multicast by the network coding technique becomes an important issue now. It is notable, however, that the conventional algorithms for network topology design are mainly unicast-oriented, and they cannot be adopted directly for the efficient topology design of network-coding-based multicast networks by simply treating each multicast as multiple unicasts. In this paper, we consider for the first time the novel topology design problem of network-coding-based multicast networks. Based on the characteristics of multicast and network coding, we first formulate this problem as a mixed-integer nonlinear programming problem, which is NP-hard, and then propose two heuristic algorithms for it. The effectiveness of our heuristics is verified through simulation and comparison with the exhaustive search method. We demonstrate in this paper that, in the topology design of multicast networks, adopting the network coding technique to support multicast transmissions can significantly reduce the overall topology cost as compared to conventional unicast-oriented design and the Steiner-tree-based design. [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]

One of the main issues in distributed computing systems is to determine an optimal task-scheduling method. In this paper, we present an effective algorithm to schedule a directed acyclic graph onto a heterogeneous distributed system, called the Earliest Starting and Finishing time Duplication based (ESFD) algorithm. Our proposed algorithm considers all parameters associated with the processors, the task, and its immediate predecessors to perform an effective task allocation. The ESFD algorithm is a three-phase algorithm with a dynamic phase to assign a priority to each task. Simulation results on both random graphs and real application graphs show that the network storage link, speedup, and efficiency generated by ESFD algorithm are better than those generated by the well-known Heterogeneous Critical Parent Trees, Heterogeneous Earliest Finish Time, and Levelized Heavily Communicating Node First algorithms. [ABSTRACT FROM PUBLISHER]

In order to maximize the efficiency of a complex distributed system such as a grid, a proper scheduling algorithm is necessary. The scheduling algorithm is responsible for allocating the available system resources to the existing jobs. In this paper, we consider scheduling gangs in a multi-cluster system in the presence of critical jobs which arrive in a sporadic manner. We examine an adaptive grid scheduler with periodic feedback which allocates the existing jobs to the available clusters. A migration scheme is implemented in order to alleviate the impact of critical sporadic jobs on the performance of gang scheduling. A simulation model is used to provide results on the performance of the system. [ABSTRACT FROM PUBLISHER]

Abstract: This paper studies a distributed discrete-time coordinated tracking problem where a team of vehicles communicating with their local neighbors at discrete-time instants tracks a time-varying reference state available to only a subset of the team members. We propose a PD-like discrete-time consensus algorithm to address the problem under a fixed communication graph. We then study the condition on the communication graph, the sampling period, and the control gain to ensure stability and give the quantitative bound of the tracking errors. It is shown that the ultimate bound of the tracking errors is proportional to the sampling period. The benefit of the proposed PD-like discrete-time consensus algorithm is also demonstrated through comparison with an existing P-like discrete-time consensus algorithm. Simulation results are presented as a proof of concept. [Copyright &y& Elsevier]

Abstract: This paper introduces a simple real-time distributed computing model for message-passing systems, which reconciles the distributed computing and the real-time systems perspective: By just replacing instantaneous computing steps with computing steps of non-zero duration, we obtain a model that both facilitates real-time scheduling analysis, and retains compatibility with classic distributed computing analysis techniques and results. We provide general simulations and validity conditions for transforming algorithms from the classic synchronous model to our real-time model and vice versa, and investigate whether/which properties of real systems are inaccurately or even wrongly captured when resorting to zero step-time models. We revisit the well-studied problem of deterministic drift- and failure-free internal clock synchronization for this purpose, and show that no clock synchronization algorithm with constant running time can achieve optimal precision in our real-time model. Since such an algorithm is known for the classic model, this is an instance of a problem where the standard distributed computing analysis gives too optimistic results. We prove that optimal precision is only achievable with algorithms that take time in our model, and establish several additional algorithms and lower bounds. [Copyright &y& Elsevier]

In this paper, we investigate the theoretical aspects of the nonuniform node distribution strategy used to mitigate the energy hole problem in wireless sensor networks (WSNs). We conclude that in a circular multihop sensor network (modeled as concentric coronas) with nonuniform node distribution and constant data reporting, the unbalanced energy depletion among all the nodes in the network is unavoidable. Even if the nodes in the inner coronas of the network have used up their energy simultaneously, the ones in the outermost corona may still have unused energy. This is due to the intrinsic many-to-one traffic pattern of WSNs. Nevertheless, nearly balanced energy depletion in the network is possible if the number of nodes increases in geometric progression from the outer coronas to the inner ones except the outermost one. Based on the analysis, we propose a novel nonuniform node distribution strategy to achieve nearly balanced energy depletion in the network. We regulate the number of nodes in each corona and derive the ratio between the node densities in the adjacent (i + 1)th and ith coronas by the strategy. Finally, we propose q-Switch Routing, a distributed shortest path routing algorithm tailored for the proposed nonuniform node distribution strategy. Extensive simulations have been performed to validate the analysis. [ABSTRACT FROM AUTHOR]

The localization of sensor nodes is a fundamental problem in sensor networks and can be implemented using powerful and expensive beacons. Beacons, the fewer the better, can acquire their position knowledge either from GPS devices or by virtue of being manually placed. In this paper, we propose a distributed method to localization of sensor nodes using a single moving beacon, where sensor nodes compute their position estimate based on the range-free technique. Two parameters are critical to the location accuracy of sensor nodes: the radio transmission range of the beacon and how often the beacon broadcasts its position. Theoretical analysis shows that these two parameters determine the upper bound of the estimation error when the traverse route of the beacon is a straight line. We extend the position estimate when the traverse route of the beacon is randomly chosen in a real-world situation, where the radio irregularity might cause a node to miss some crucial coordinate information from the beacon. We further point out that the movement pattern of the beacon plays a pivotal role in the localization task for sensors. To minimize estimation errors, sensor nodes can carry out a variety of algorithms in accordance with the movement of the beacon. Simulation results compare variants of the distributed method in a variety of testing environments. Real experiments show that the proposed method is feasible and can estimate the location of sensor nodes accurately, given a single moving beacon. [ABSTRACT FROM AUTHOR]

Abstract: Many problems that face a business decision maker are most accurately formulated as multi-objective optimization problems. However, actually solving these problems is a difficult and computationally expensive process. In this paper, we develop and use an agent-based optimization system for efficiently generating the non-dominated solution set to a non-convex multi-objective optimization problem. Through simulation, we highlight the benefits that arise from allowing collaboration among agents embodying a diverse set of algorithms. Further, we demonstrate the ability of the agent system to use large-scale parallel resources efficiently. We also propose two new metrics for quantifying the performance of multi-objective optimization systems. [Copyright &y& Elsevier]