We introduce an automated parameterized verification method for fault-tolerant distributed algorithms (FTDA). FTDAs are parameterized by both the number of processes and the assumed maximum number of faults. At the ce...
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ISBN:
(纸本)9781479926848
We introduce an automated parameterized verification method for fault-tolerant distributed algorithms (FTDA). FTDAs are parameterized by both the number of processes and the assumed maximum number of faults. At the center of our technique is a parametric interval abstraction (PIA) where the interval boundaries are arithmetic expressions over parameters. Using PIA for both data abstraction and a new form of counter abstraction, we reduce the parameterized problem to finite-state model checking. We demonstrate the practical feasibility of our method by verifying safety and liveness of several fault-tolerant broadcasting algorithms, and finding counter examples in the case where there are more faults than the FTDA was designed for.
The mobile agent paradigm has been adopted by several systems in the area of wireless sensor networks as it enables a flexible distribution and placement of application components on nodes, at runtime. Most agent plac...
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The mobile agent paradigm has been adopted by several systems in the area of wireless sensor networks as it enables a flexible distribution and placement of application components on nodes, at runtime. Most agent placement and migration algorithms proposed in the literature, assume that the communication rates between agents remain stable for a sufficiently long time to amortize the migration costs. Then, the problem is that frequent changes in the application-level communication may lead to several non-beneficial agent migrations, which may actually increase the total network cost, instead of decreasing it. To tackle this problem, we propose two distributed algorithms that take migration decisions in an online fashion, trying to deal with fluctuations in agent communication. The first algorithm is more of theoretical value, as it assumes infinite storage to keep information about the message exchange history of agents, while the second algorithm is a refined version that works with finite storage and limited information. We describe these algorithms in detail, and provide proofs for their competitive ratio vs. an optimal oracle. In addition, we evaluate the performance of the proposed algorithms for different parameter settings through a series of simulated experiments, also comparing their results with those achieved by an optimal static placement that is computed with full (a posteriori) knowledge of the execution scenarios. Our theoretical and experimental results are a strong indication for the robustness and effectiveness of the proposed algorithms.
distributed algorithms have hitherto been designed for networks with static hosts. A mobile host (MH) can connect to the network from different locations at different times. This paper presents an operational system m...
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distributed algorithms have hitherto been designed for networks with static hosts. A mobile host (MH) can connect to the network from different locations at different times. This paper presents an operational system model for explicitly incorporating the effects of host mobility and proposes a general principle for structuring efficient distributed algorithms in this model. This principle is used to redesign two classical algorithms for distributed mutual exclusion for the mobile environment. We then consider a problem introduced solely by host mobility viz, location management for groups of MHs, and propose the concept of group location as an efficient approach to tackle the problem. Lastly, we present a framework which enables host mobility to be decoupled from the design of a distributed algorithm per se, to varying degrees.< >
distributed systems offer many features such as resource sharing, scalability, fault tolerance and reliability. Several distributed algorithms have been proposed in literature to solve fundamental problems such as mut...
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distributed systems offer many features such as resource sharing, scalability, fault tolerance and reliability. Several distributed algorithms have been proposed in literature to solve fundamental problems such as mutual exclusion and leader election in distributed systems. When more than one algorithm is invented to solve the same problem particularly in asynchronous distributed systems, their performance is compared mostly based on the message complexity. This paper reviews the concept of message complexity and offers more clarity by studying the performance of the two most popular distributed algorithms - Ricart-Agrawala's algorithm and Raymond algorithm designed to solve the mutual exclusion problem. The paper has four main contributions (i) observes how the message complexity is understood and computed in the asynchronous distributed system so far and exposes its elusiveness; (ii) offers a more suitable definition of message complexity; (iii) briefly presents the simulator designed to study the performance of the distributed algorithms using the refined metric; and finally (iv) discusses about the simulation study to illustrate the significance and usefulness of the proposed metric.
Dynamic networks are characterized by frequent topology changes due to the unpredictable appearance and disappearance of mobile devices and/or communication links. In this paper, we propose a correct-by-construction a...
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Dynamic networks are characterized by frequent topology changes due to the unpredictable appearance and disappearance of mobile devices and/or communication links. In this paper, we propose a correct-by-construction approach for proving distributed algorithms in a forest of spanning trees. Our approach consists in two phases. The first one aims to control the dynamic structure of the network by triggering a maintenance operation when the forest is altered. To do so, we develop a formal pattern using the Event-B method which is based on an existing model for building and maintaining a spanning forest in dynamic networks. The second phase of our approach deals with distributed algorithms which can be applied to spanning trees. We illustrate our pattern through an example of a leader election algorithm. The proof statistics show that our solution can save efforts on specifying as well as proving the correctness of distributed algorithms in a forest of spanning trees.
Bluetooth has provided many features that enable wireless ad-hoc networks, but it has also introduced many problems. The root cause of these problems lies in its communication mechanisms. We argue in this paper that t...
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Bluetooth has provided many features that enable wireless ad-hoc networks, but it has also introduced many problems. The root cause of these problems lies in its communication mechanisms. We argue in this paper that the models that have been used to study distributed algorithms on Bluetooth networks do not adequately model these networks in most cases, and were often oversimplified. This is mainly due to how the many restrictions that the Bluetooth specifications impose on such networks are taken into account, and the lack of ldquoshared knowledgerdquo of these restrictions among the researchers of this field. We give some examples to back our argument. We give also some suggestions and proposals to overcome these issues.
In this work, we consider the problem of designing adaptive distributed processing algorithms in large sensor networks that are efficient in terms of minimizing the total power spent for gathering the spatially correl...
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In this work, we consider the problem of designing adaptive distributed processing algorithms in large sensor networks that are efficient in terms of minimizing the total power spent for gathering the spatially correlated data from the sensor nodes to a sink node. We take into account both the power spent for purposes of communication as well as the power spent for local computation. Our distributed algorithms are also matched to the nature of the correlated field, namely, for piecewise smooth signals, we provide two distributed multiresolution wavelet-based algorithms, while for correlated Gaussian fields, we use distributed prediction based processing. In both cases, we provide distributed algorithms that perform network division into groups of different sizes. The distribution of the group sizes within the network is the result of an optimal trade-off between the local communication inside each group needed to perform decorrelation, the communication needed to bring the processed data (coefficients) to the sink and the local computation cost, which grows as the network becomes larger. Our experimental results show clearly that important gains in power consumption can be obtained with respect to the case of not performing any distributed decorrelating processing.
Adopting a tutorial approach, this paper surveys some control and systems theory problems that have recently gained interest in the context of multi-vehicle and sensor networks. By means of illustrative examples, we d...
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Adopting a tutorial approach, this paper surveys some control and systems theory problems that have recently gained interest in the context of multi-vehicle and sensor networks. By means of illustrative examples, we discuss some challenges in modeling of robotic networks, motion coordination algorithms, sensing and estimation tasks, and complexity of distributed algorithms.
Resource contention is widely recognized as having a major impact on the performance of distributed algorithms. Nevertheless, the metrics that are commonly used to predict their performance take little or no account o...
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Resource contention is widely recognized as having a major impact on the performance of distributed algorithms. Nevertheless, the metrics that are commonly used to predict their performance take little or no account of contention. We define two performance metrics for distributed algorithms that account for network contention as well as CPU contention. We then illustrate the use of these metrics by comparing four atomic broadcast algorithms, and show that our metrics allow for a deeper understanding of performance issues than conventional metrics.
The optimal power flow (OPF) problem, a fundamental problem in power systems, is generally nonconvex and computationally challenging for networks with an increasing number of smart devices and real-time control requir...
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ISBN:
(纸本)9781509045839
The optimal power flow (OPF) problem, a fundamental problem in power systems, is generally nonconvex and computationally challenging for networks with an increasing number of smart devices and real-time control requirements. In this paper, we first investigate a fully distributed approach by means of the augmented Lagrangian and proximal alternating minimization method to solve the nonconvex OPF problem with a convergence guarantee. Given time-critical requirements, we then extend the algorithm to a distributed parametric tracking scheme with practical warm-starting and termination strategies, which aims to provide a closed-loop sub-optimal control policy while taking into account the grid information updated at the time of decision making. The effectiveness of the proposed algorithm for real-time nonconvex OPF problems is demonstrated in numerical simulations.
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