21260780
An efficient incremental algorithm for identifying consistent checkpoints
In a distributed system, identifying consistent checkpoints is essential for error recovery and debugging. We design an efficient incremental algorithm capable of identifying all the consistent and removable checkpoints each time a new checkpoint is reported. By doing so, the required memory space can be minimized by removing those removables. While minimizing the memory space, the algorithm requires only O(p/sup 2/M) time in total, where p is the number of processes and M is the number of checkpoints.
2039675
On Detection of Bounded Global Predicates
Distributed programs often follow some bounded global predicates, for example, the total number of certain tokens is always the same or bounded in a range. In order to detect bounded global predicates, we can first derive the minimum and maximum global snapshots and then check if the minimum and maximum are out of the range. Recently, Chase and Garg proposed an efficient method to derive the minimum global snapshot by reducing this problem to a maximum network flow problem. A restriction of this method is that all message values (e.g., the token number in messages) must be zero and all process state values (e.g., the token number in processes) must be non-negative. In this paper, we propose an elegant technique, called normalization. By using this technique, we can remove the above restriction and also derive the minimum and maximum global snapshots at the same time.
7800069
Detection of summative global predicates
In distributed programs, we usually keep some global predicates from being satisfied to make it easy to run the programs correctly. A common type of global predicates are: the total number of certain tokens in the whole distributed system is always the same or in specific ranges. In this paper, we call this summative predicates, classified into the following four: (1) at some global state of the system, N/spl ne/K, (2) NK (or N/spl ges/K), and (4) N=K, where N is the total number of tokens and K is a constant. This paper investigates the methods of detecting various summative global predicates. The first class of summative predicates are trivial to detect by simply checking each message. For the second class of summative predicates, B. Groselj (1993) and V.K. Garg (1995) solved the problem by reducing the problem to a maximum network flow problem. In this paper, we propose an elegant technique, called normalization, to allow the second and third classes of summative predicates to be solved by also reducing the problem to a maximum network flow problem. For the fourth class of summative predicates, we prove that it is an NP-complete problem.
5417653
On the Web Data Extraction Model
205888059
On the Time Complexity of Minimum and Maximum Global Snapshot Problems
Abstract Deriving the minimum and maximum global snapshots is very useful for some error detection problems in distributed programs. Several researchers, e.g., Groselj, Chen and Wu, have shown that the minimum and maximum global snapshot problems are linear-time reducible to the maximum constant-ratio network flow (MCNF) problem, here defined as the wellknown maximum network flow problem with m = Θ(n), where m is the number of edges and n is the number of vertices in the given flow network. In this paper we show in a reverse way that the MCNF problem is also linear-time reducible to these global snapshot problems. Thus, we can conclude that the global snapshot problems are “as difficult as” the MCNF problem in terms of time complexity.
2568892
Traffic Adaptive MAC Protocol for Wireless Sensor Network
In this paper, we propose TA-MAC, a traffic load adaptive Medium Access Control protocol for wireless sensor network. TA-MAC modified the contention window mechanism of S-MAC. It adjusts the initial contention window according to the current traffic load to reduce the collision probability and employs a fast backoff scheme to reduce the time for idle listening during backoff procedure, which both reduce the energy consumption. Simulation results show that TA-MAC outperforms S-MAC.
5569010
A Fast Efficient Parallel Hough Transform Algorithm on LARPBS
A parallel algorithm for Hough transform on a linear array with reconfigurable pipeline bus system (LARPBS) is presented. Suppose the number of θ-values to be considered is m, for an image with n × n pixels, the algorithm can complete Hough transform in O(1) time using mn2 processors and achieve optimal speed and efficiency. We also illustrate how to partition data and perform the algorithm on a LARPBS with fewer than mn2 processors, and hence show that the algorithm is highly scalable.
17553402
Efficient Parallel Algorithms for Euclidean Distance Transform
The Euclidean Distance Transform (EDT) converts a binary image into one where each pixel has a value equal to its distance to the nearest foreground pixel. Two parallel algorithms for EDT transform on linear array with reconfigurable pipeline bus system (LARPBS) are presented. For an image with n×n pixels, the first algorithm can complete EDT transform in ) log log log log log log ( n n n O time using n processors. The second algorithm can compute the EDT in O( log n loglog n) time using n n log log 2 processors.
8972258
Designing oscillators in synthetic gene networks based on multi-scale dynamics
Multistability, oscillations, and switching exist at various levels of biological processes and organizations and have been investigated on the basis of many theoretical models, such as circadian oscillations with the period protein (PER) and the timeless protein (TIM) in Drosophila, and multistable dynamics regulated by transcriptional factors. Considerable experimental evidence suggests that cellular processes are intrinsically rhythmic or periodic. Various periodic oscillations with different time scales ranging from less than a second to more than a year, which may allow for living organisms to adapt their behaviors to a periodically varying environment, have also been observed experimentally. On the other hand, in synthetic gene networks, both toggle switch and repressilator have been theoretically proposed and further confirmed by experiments. All of these works stress the importance of feedback regulation of transcriptional factors, which is a key in giving rise to oscillatory or multistable dynamical behaviors exhibited by biological genetic systems. In addition, it should be noted that many periodic behaviors do not simply oscillate smoothly; rather, they change rapidly or jump at certain states. In gene expression systems, many different time scales characterize the gene regulatory processes. For instance, the transcription and translation processes generally evolve on a time scale that is much slower than that of phosphorylation, dimerization or binding reactions of transcription factors. In genetic networks, the time scale for expression of some genes is much slower than that of others, depending on the length of the genes. We aim to design robust periodic oscillators in synthetic gene-protein systems by simple nonlinear models and to analyze the basic mechanism of limit cycles with jumping behaviors or relaxation oscillations by exploiting multiple time-scale properties [1, 2]. We show that periodic oscillations are mainly generated by nonlinear feedback loops in gene regulatory systems and the jumping dynamics caused by time scale differences among biochemical reactions. Moreover, effects of time delay are also examined. We show that time delay generally enlarges the stability region of oscillations, thereby making the oscillations more sustainable despite parameter changes or noise [1, 2]. The dynamics of the proposed models is robust in terms of stability and period length to the parameter perturbations or environment variations. Although we mainly analyze some specific models, the mechanisms identified in this work are likely to apply to a variety of genetic regulatory systems. These simple models may actually act as basic building block in synthetic gene-protein networks, such as genetic oscillators or switches because the dynamics is robust for parameter perturbations or environment variations. Several examples are also provided to demonstrate implementation of synthetic oscillators by using genes of the /spl lambda/ phage bacteria.
27714693
Efficient Parallel Processing for K-Nearest-Neighbor Search in Spatial Databases
Even though the problem of k nearest neighbor (kNN) query is well-studied in serial environment, there is little prior work on parallel kNN search processing in parallel one. In this paper, we present the first Best-First based Parallel kNN (BFPkNN) query algorithm in a multi-disk setting, for efficient handling of kNN retrieval with arbitrary values of k by parallelization. The core of our method is to access more entries from multiple disks simultaneously and enable several effective pruning heuristics to discard non-qualifying entries. Extensive experiments with real and synthetic datasets confirm that BFPkNN significantly outperforms its competitors in both efficiency and scalability.