Blazes: Coordination Analysis for Distributed Programs

Distributed consistency is perhaps the most discussed topic in distributed systems today. Coordination protocols can ensure consistency, but in practice they cause undesirable performance unless used judiciously. Scalable distributed architectures avoid coordination whenever possible, but under-coordinated systems can exhibit behavioral anomalies under fault, which are often extremely difficult to debug. This raises significant challenges for distributed system architects and developers. In this paper we present Blazes, a cross-platform program analysis framework that (a) identifies program locations that require coordination to ensure consistent executions, and (b) automatically synthesizes application-specific coordination code that can significantly outperform general-purpose techniques. We present two case studies, one using annotated programs in the Twitter Storm system, and another using the Bloom declarative language.Comment: Updated to include additional materials from the original technical report: derivation rules, output stream label

Assessing the reliability of diverse fault-tolerant software-based systems

We discuss a problem in the safety assessment of automatic control and protection systems. There is an increasing dependence on software for performing safety-critical functions, like the safety shut-down of dangerous plants. Software brings increased risk of design defects and thus systematic failures; redundancy with diversity between redundant channels is a possible defence. While diversity techniques can improve the dependability of software-based systems, they do not alleviate the difficulties of assessing whether such a system is safe enough for operation. We study this problem for a simple safety protection system consisting of two diverse channels performing the same function. The problem is evaluating its probability of failure in demand. Assuming failure independence between dangerous failures of the channels is unrealistic. One can instead use evidence from the observation of the whole system's behaviour under realistic test conditions. Standard inference procedures can then estimate system reliability, but they take no advantage of a system’s fault-tolerant structure. We show how to extend these techniques to take account of fault tolerance by a conceptually straightforward application of Bayesian inference. Unfortunately, the method is computationally complex and requires the conceptually difficult step of specifying 'prior' distributions for the parameters of interest. This paper presents the correct inference procedure, exemplifies possible pitfalls in its application and clarifies some non-intuitive issues about reliability assessment for fault-tolerant software

Real-time predictive maintenance for wind turbines using Big Data frameworks

This work presents the evolution of a solution for predictive maintenance to a Big Data environment. The proposed adaptation aims for predicting failures on wind turbines using a data-driven solution deployed in the cloud and which is composed by three main modules. (i) A predictive model generator which generates predictive models for each monitored wind turbine by means of Random Forest algorithm. (ii) A monitoring agent that makes predictions every 10 minutes about failures in wind turbines during the next hour. Finally, (iii) a dashboard where given predictions can be visualized. To implement the solution Apache Spark, Apache Kafka, Apache Mesos and HDFS have been used. Therefore, we have improved the previous work in terms of data process speed, scalability and automation. In addition, we have provided fault-tolerant functionality with a centralized access point from where the status of all the wind turbines of a company localized all over the world can be monitored, reducing O&M costs

An objective based classification of aggregation techniques for wireless sensor networks

Wireless Sensor Networks have gained immense popularity in recent years due to their ever increasing capabilities and wide range of critical applications. A huge body of research efforts has been dedicated to find ways to utilize limited resources of these sensor nodes in an efficient manner. One of the common ways to minimize energy consumption has been aggregation of input data. We note that every aggregation technique has an improvement objective to achieve with respect to the output it produces. Each technique is designed to achieve some target e.g. reduce data size, minimize transmission energy, enhance accuracy etc. This paper presents a comprehensive survey of aggregation techniques that can be used in distributed manner to improve lifetime and energy conservation of wireless sensor networks. Main contribution of this work is proposal of a novel classification of such techniques based on the type of improvement they offer when applied to WSNs. Due to the existence of a myriad of definitions of aggregation, we first review the meaning of term aggregation that can be applied to WSN. The concept is then associated with the proposed classes. Each class of techniques is divided into a number of subclasses and a brief literature review of related work in WSN for each of these is also presented