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The detection of failures in distributed environments is a crucial part for developing dependable, robust, and selfhealing systems. The contribution of this paper is a new failure detection algorithm that can be described as an adaptive accrual algorithm coupled with features to increase flexiblity and decrease computation costs. Furthermore our evaluation results show a very good detection quality in the case of message losses.

Stream processing is a computing paradigm that has emerged from the necessity of handling high volumes of data in real time. In contrast to traditional databases, stream-processing systems perform continuous queries and handle data on-thefly. Today, a wide range of application areas relies on efficient pattern detection and queries over streams. The advent of Cloud computing fosters the development of elastic stream-processing platforms, which are able to dynamically adapt based on different cost-benefit trade-offs. This article provides an overview of the historical evolution and the key concepts of stream processing, with special focus on adaptivity and Cloud-based elasticity. C 2013 Wiley Periodicals, Inc.In this article, we present a systematic review of stream processing, with a focus on how elastic computing of data streams can be achieved on top of Cloud computing. The study attempts to put the Volume

Monitoring of applications deployed to Infrastructure as-a-Service clouds is still an open problem. In this paper, we discuss an approach based on the complex event processing paradigm, which allows application developers to specify and monitor high-level application performance metrics. We use the case of a Web 2.0 sentiment anal ysis application to illustrate the limitations we currently experience with regard to cloud monitoring, and show how our approach allows for more expressive definitions of monitored metrics. Furthermore, we indicate how the higher-level metrics produced by our approach can be used to increase application elasticity in an existing cloud middleware.