Reconfiguration models and algorithms for stateful interactiveprocesses

We present new results in the area of reconfiguration of stateful interactive processes in the presence of faults. More precisely, we consider a set of servers/processes that have the same functionality, i.e., are able to perform the same tasks and provide the same set of services to their clients. In the case when several of them turn out to be faulty, we want to reconfigure the system so that the clients of the faulty servers/processes are served by some other, fault-free, servers of the system in a way that is transparent to all the system clients. We propose a novel method for reconfiguring in the presence of faults: compensation paths. Compensation paths are an efficient way of shifting spare resources from where they are available to where they are needed. We also present optimal and suboptimal simple reconfiguration algorithms of low polynomial time complexity O(nmlog(n²/m)) for the optimal and O(m) for the suboptimal algorithms, where n is the number of processes and m is the number of primary-backup relationships. The optimal algorithms compute the way to reconfigure the system whenever the reconfiguration is possible. The suboptimal algorithms may sometimes fail to reconfigure the system, although reconfiguration would be possible by using the optimal centralized algorithms. However, suboptimal algorithms have other competitive advantages over the centralized optimal algorithms with regard to time complexity and communication overhead     

The Role of Leptin in Childhood Immune Thrombocytopenia (ITP): An Anti-Inflammatory Agent?

To investigate the effect of leptin in childhood ITP, we measured plasma leptin in 39 children with acute ITP, after treatment and in remission, and in 33 healthy age/BMI-matched controls. We also cultured ITP and control peripheral blood mononuclear cells (PBMCs) with recombinant leptin to assess its direct effect on pro/anti-inflammatory cytokine gene expression. A significant increase in leptin was observed in children with active disease compared to controls. A significant inverse correlation of leptin with platelet count was also observed in children with acute ITP. Leptin remained high after treatment with IVIg, whereas steroid treatment lowered leptin below control levels. In remission, leptin was in the control range. Cytokine gene expression was significantly increased in children with acute ITP compared with controls, with highest expression for IFN-γ and IL-10. IVIg/steroid treatment significantly decreased IFN-γ and IL-10 expression. In remission, IFN-γ and IL-10 expression remained low. Addition of leptin to PBMCs isolated from patients in remission resulted in a significant increase in IL-10 gene expression compared to controls. Further experiments with purified T-cells and monocytes identified monocytes as the source of leptin-induced IL-10. We suggest that leptin acts as an active anti-inflammatory agent in childhood ITP by promoting IL-10 secretion by monocytes.     

Fault tolerant and prioritized scheduling in OGSA‐based mobile Grids

Grids and mobile Grids can form the basis and the enabling technology for pervasive and utility computing due to their ability to being open, highly heterogeneous and scalable. In this paper we present a scheme for advancing quality of service (QoS) attributes, such as fault tolerance and prioritized scheduling, in OGSA‐based mobile Grids. The fault tolerance is achieved by producing and managing sufficient replicas of tasks submitted for execution on the mobile Grid resources. We design a simple and efficient prioritization scheme, which allows the scheduling of the tasks submitted by the Grid users as distinguished priorities that can be managed and exploited as a QoS parameter by the Grid infrastructure operator. The results that are presented show the efficiency of the proposed scheme in being simple and additionally enriching with reliability and QoS features the applications that are built on the concept of mobile Grids. Copyright © 2008 John Wiley & Sons, Ltd.     

MOFA: a model for fault and availability in complex services

Asset allocation to optimize reliability and availability (R&A) has been studied in the literature. However, these concepts have not been investigated in combination with degraded performance in realistic reliability-models. Some reliability models, techniques, and tools allow modeling of degradable performance. However, they are mainly used in evaluating R&A of given systems; they do not consider the inverse problem of determining the system parameters that would maximize the R&A. This paper provides a realistic reliability model for complex services; the model can be used to study the asset allocation problem in degradable complex services. This paper uses a simple model to describe the failure characteristics of the system parts. This model provides: (1) an infrastructure for defining R&A in a flexible way, and (2) a way of defining at what level of availability it is worth offering the service, and at what level of failure of resources the system should be declared faulty. This model is then used to `allocate optimally the available assets' to `implement a service that is tailored to the availability needs of the user and the situation'. The model is applied to a realistic example of a complex service, viz. a multimedia communication service     

Robust Sequential Data Modeling Using an Outlier Tolerant Hidden Markov Model

Hidden Markov (chain) models using finite Gaussian mixture models as their hidden state distributions have been successfully applied in sequential data modeling and classification applications. Nevertheless, Gaussian mixture models are well known to be highly intolerant to the presence of untypical data within the fitting data sets used for their estimation. Finite Student's t-mixture models have recently emerged as a heavier-tailed, robust alternative to Gaussian mixture models, overcoming these hurdles. To exploit these merits of Student's t-mixture models in the context of a sequential data modeling setting, we introduce, in this paper, a novel hidden Markov model where the hidden state distributions are considered to be finite mixtures of multivariate Student's t-densities. We derive an algorithm for the model parameters estimation under a maximum likelihood framework, assuming full, diagonal, and factor-analyzed covariance matrices. The advantages of the proposed model over conventional approaches are experimentally demonstrated through a series of sequential data modeling applications.     

STAVIES: a system for information extraction from unknown Web data sources through automatic Web wrapper generation using clustering techniques

A fully automated wrapper for information extraction from Web pages is presented. The motivation behind such systems lies in the emerging need for going beyond the concept of "human browsing". The World Wide Web is today the main "all kind of information" repository and has been so far very successful in disseminating information to humans. By automating the process of information retrieval, further utilization by targeted applications is enabled. The key idea in our novel system is to exploit the format of the Web pages to discover the underlying structure in order to finally infer and extract pieces of information from the Web page. Our system first identifies the section of the Web page that contains the information to be extracted and then extracts it by using clustering techniques and other tools of statistical origin. STAVIES can operate without human intervention and does not require any training. The main innovation and contribution of the proposed system consists of introducing a signal-wise treatment of the tag structural hierarchy and using hierarchical clustering techniques to segment the Web pages. The importance of such a treatment is significant since it permits abstracting away from the raw tag-manipulating approach. Experimental results and comparisons with other state of the art systems are presented and discussed in the paper, indicating the high performance of the proposed algorithm.     

Module replication for fault-tolerant real-time distributed systems

This paper considers the problem of replicating and scheduling periodic tasks in a multiprocessor system, under timing and dependency constraints. The objective is to maximize the probability of successful completion (logically correct execution, within the time constraints) of all the tasks in the system. The authors assume a precedence graph that is general with chain, AND, OR and loop subgraphs. To achieve high probability of successful completion of the tasks in the system, several modules (that constitute the tasks) are chosen for replication and executed, regardless of whether a failure actually occurs or not. The replicated modules are chosen in an optimal way, and are added to the set of the executable tasks only if that increases the probability of successful completion. The failure model of the modules in the system is general and realistic. The allocation scheme assigns the original and the replicated modules to the processing nodes of the system, and determines their starting time as well as the schedule for communication among them. Their results improve upon the work done previously