15941649
Multitolerance in Distributed Reset
A reset of a distributed system is safe if it does not complete ``prematurely,'''' i.e., without having reset some process in the system. Safe resets are possible in the presence of certain faults, such as process fail-stops and repairs, but are not always possible in the presence of more general faults, such as arbitrary transients. In this paper, we design a bounded-memory distributed-reset program that possesses two tolerances: (1) in the presence of fail-stops and repairs, it always executes resets safely, and (2) in the presence of a finite number of transient faults, it eventually executes resets safely. Designing this multitolerance in the reset program introduces the novel concern of designing a safety detector that is itself multitolerant. A broad application of our multitolerant safety detector is to make any total program likewise multitolerant.
6279302
A line in the sand: a wireless sensor network for target detection, classification, and tracking
Intrusion detection is a surveillance problem of practical import that is well suited to wireless sensor networks. In this paper, we study the application of sensor networks to the intrusion detection problem and the related problems of classifying and tracking targets. Our approach is based on a dense, distributed, wireless network of multi-modal resource-poor sensors combined into loosely coherent sensor arrays that perform in situ detection, estimation, compression, and exfiltration. We ground our study in the context of a security scenario called "A Line in the Sand" and accordingly define the target, system, environment, and fault models. Based on the performance requirements of the scenario and the sensing, communication, energy, and computation ability of the sensor network, we explore the design space of sensors, signal processing algorithms, communications, networking, and middleware services. We introduce the influence field, which can be estimated from a network of binary sensors, as the basis for a novel classifier. A contribution of our work is that we do not assume a reliable network; on the contrary, we quantitatively analyze the effects of network unreliability on application performance. Our work includes multiple experimental deployments of over 90 sensor nodes at MacDill Air Force Base in Tampa, FL, as well as other field experiments of comparable scale. Based on these experiences, we identify a set of key lessons and articulate a few of the challenges facing extreme scaling to tens or hundreds of thousands of sensor nodes.
1711572
Designing masking fault-tolerance via nonmasking fault-tolerance
Masking fault-tolerance guarantees that programs continually satisfy their specification in the presence of faults. By way of contrast, nonmasking fault-tolerance does not guarantee as much: it merely guarantees that when faults stop occurring, program executions converge to states from where programs continually (re)satisfy their specification. In this paper, we show that a practical method to design masking fault-tolerance is to first design nonmasking fault-tolerance and to then transform the nonmasking fault-tolerant program minimally so as to achieve masking fault-tolerance. We demonstrate this method by designing novel fully distributed programs for termination detection, mutual exclusion, and leader election, that are masking tolerant of any finite number of process fail-stops and/or repairs.
12287966
Complexity issues in automated synthesis of failsafe fault-tolerance
We focus on the problem of synthesizing failsafe fault-tolerance where fault-tolerance is added to an existing (fault-intolerant) program. A failsafe fault-tolerant program satisfies its specification (including safety and liveness) in the absence of faults. However, in the presence of faults, it satisfies its safety specification. We present a somewhat unexpected result that, in general, the problem of synthesizing failsafe fault-tolerant distributed programs from their fault-intolerant version is NP-complete in the state space of the program. We also identify a class of specifications, monotonic specifications, and a class of programs, monotonic programs, for which the synthesis of failsafe fault-tolerance can be done in polynomial time (in program state space). As an illustration, we show that the monotonicity restrictions are met for commonly encountered problems, such as Byzantine agreement, distributed consensus, and atomic commitment. Furthermore, we evaluate the role of these restrictions in the complexity of synthesizing failsafe fault-tolerance. Specifically, we prove that if only one of these conditions is satisfied, the synthesis of failsafe fault-tolerance is still NP-complete. Finally, we demonstrate the application of monotonicity property in enhancing the fault-tolerance of (distributed) nonmasking fault-tolerant programs to masking.
33040079
Resettable vector clocks
Vector clocks (VC) are an inherent component of a rich class of distributed applications. In this paper, we first consider the problem of realistic implementation-more specifically, bounded-space and fault-tolerant-of applications that use vector clocks (VC). To this end, we generalize the notion of VC to resettable vector clocks (RVC), and provide a realistic implementation of RVC. Further, we identify an interface contract under which our RVC implementation can be substituted for VC in client applications, without affecting the client's correctness. This interface contract is designed for phase-based applications that provide certain communication guarantees. Based on such substitution and the use of the interface contract, we show how to transform the client so that it is itself realistically implemented. We illustrate our method in the context of Ricart-Agrawala's mutual exclusion program and Garg-Chase's predicate detection program.
52887342
Low-cost fault-tolerance in barrier synchronizations
We show how fault-tolerance can be effectively added to several types of faults in program computations that use barrier synchronization. We divide the faults that occur in practice into two classes, detectable and undetectable, and design a fully distributed program that tolerates the faults in both classes. Our program guarantees that every barrier is executed correctly even if detectable faults occur, and that eventually every barrier is executed correctly even if undetectable faults occur. Via analytical as well as simulation results we show that the cost of adding fault-tolerance is low, in part by comparing the times required by our program with that required by the corresponding fault-intolerant counterpart.
14320923
Scalable Clustering for Large High-Dimensional Data Based on Data Summarization
Clustering large data sets with high dimensionality is a challenging data-mining task. This paper presents a framework to perform such a task efficiently. It is based on the notion of data space reduction, which finds high density areas, or dense cells, in the given feature space. The dense cells store summarized information of the data. A designated partitioning or hierarchical clustering algorithm can be used as the second step to find clusters based on the data summaries. Using Kmeans as an example, this paper presents GARDEN-Kmeans, which performs data space reduction using Gamma Region DENsity partition, and utilizes Kmeans to cluster the summarized information. The experimental study shows that GARDEN-Kmeans executes several orders of magnitude faster than basic Kmeans and the recursive bisection Kmeans algorithm of CLUTO, while producing comparable clustering quality
6063474
PAC learning with generalized samples and an application to stochastic geometry
In this paper, we introduce an extension of the standard PAC learning model which allows the use of generalized samples. We view a generalized sample as a pair consisting of a functional on the concept class together with the value obtained by the functional operating on the unknown concept. It appears that this model can be applied to a number of problems in signal processing and geometric reconstruction to provide sample size bounds under a PAC criterion. We consider a specific application of the model to a problem of curve reconstruction, and discuss some connections with a result from stochastic geometry.
7606261
Rates of convergence of nearest neighbor estimation under arbitrary sampling
Rates of convergence for nearest neighbor estimation are established in a general framework in terms of metric covering numbers of the underlying space. The first result is to find explicit finite sample upper bounds for the classical independent and identically distributed (i.i.d.) random sampling problem in a separable metric space setting. The convergence rate is a function of the covering numbers of the support of the distribution. For example, for bounded subsets of R/sup r/, the convergence rate is O(1/n/sup 2/r/). The main result is to extend the problem to allow samples drawn from a completely arbitrary random process in a separable metric space and to examine the performance in terms of the individual sample sequences. The authors show that for every sequence of samples the asymptotic time-average of nearest neighbor risks equals twice the time-average of the conditional Bayes risks of the sequence. Finite sample upper bounds under arbitrary sampling are again obtained in terms of the covering numbers of the underlying space. In particular, for bounded subsets of R/sup r/ the convergence rate of the time-averaged risk is O(1/n/sup 2/r/). The authors then establish a consistency result for k/sub n/-nearest neighbor estimation under arbitrary sampling and prove a convergence rate matching established rates for i.i.d. sampling. Finally, they show how their arbitrary sampling results lead to some classical i.i.d. sampling results and in fact extend them to stationary sampling. The framework and results are quite general while the proof techniques are surprisingly elementary. >
5710059
Universal variable-to-fixed length source codes
A universal variable-to-fixed length algorithm for binary memoryless sources which converges to the entropy of the source at the optimal rate is known. We study the problem of universal variable-to-fixed length coding for the class of Markov sources with finite alphabets. We give an upper bound on the performance of the code for large dictionary sizes and show that the code is optimal in the sense that no codes exist that have better asymptotic performance. The optimal redundancy is shown to be H log log M/log M where H is the entropy rate of the source and M is the code size. This result is analogous to Rissanen's (1984) result for fixed-to-variable length codes. We investigate the performance of a variable-to-fixed coding method which does not need to store the dictionaries, either at the coder or the decoder. We also consider the performance of both these source codes on individual sequences. For individual sequences we bound the performance in terms of the best code length achievable by a class of coders. All the codes that we consider are prefix-free and complete.
7846439
Universal lossless source coding with the Burrows Wheeler Transform
The Burrows Wheeler transform (1994) is a reversible sequence transformation used in a variety of practical lossless source-coding algorithms. In each, the BWT is followed by a lossless source code that attempts to exploit the natural ordering of the BWT coefficients. BWT-based compression schemes are widely touted as low-complexity algorithms giving lossless coding rates better than those of the Ziv-Lempel codes (commonly known as LZ'77 and LZ'78) and almost as good as those achieved by prediction by partial matching (PPM) algorithms. To date, the coding performance claims have been made primarily on the basis of experimental results. This work gives a theoretical evaluation of BWT-based coding. The main results of this theoretical evaluation include: (1) statistical characterizations of the BWT output on both finite strings and sequences of length n /spl rarr/ /spl infin/, (2) a variety of very simple new techniques for BWT-based lossless source coding, and (3) proofs of the universality and bounds on the rates of convergence of both new and existing BWT-based codes for finite-memory and stationary ergodic sources. The end result is a theoretical justification and validation of the experimentally derived conclusions: BWT-based lossless source codes achieve universal lossless coding performance that converges to the optimal coding performance more quickly than the rate of convergence observed in Ziv-Lempel style codes and, for some BWT-based codes, within a constant factor of the optimal rate of convergence for finite-memory sources.