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The management of Grid systems commonly lacks information for identifying the failures that may hinder the timely completion of jobs, and cause the wasting of computing resources. Monitoring can certainly help, but novel approaches need to be conceived for such large and geographically distributed systems. We propose a Grid Architecture for scalable Monitoring and Enhanced dependable job ScHeduling (GAMESH). GAMESH is a completely distributed and highly efficient management infrastructure for the dissemination of monitoring data and troubleshooting of job execution failures in large-scale and multi-domain Grid environments. Challenged in a real deployment and compared to other Grid management systems, GAMESH demonstrates to (i) ensure measurements of both computing resources and conditions of task scheduling at geographically sparse sites, while inducing a low overhead on the entire infrastructure, and (ii) enable failure-aware scheduling and improve overall system performance, even in the presence of failures, by coordinating local job schedulers at multiple domains.

This paper introduces the design of a logging framework for the on-line failure analysis of Android Smart Phones. The framework enables the collection of failure data at runtime, useful to assess the dependability of smart phones, to deeply investigate the causes of failures, and to reduce maintenance efforts. Existing tools mainly focus on the collection of failure data about application crashes. The aim of the proposed framework is to extend the collection to other types of failures, such as, application hang, phone freeze and phone self-reboot. The paper describes the main design choices behind the components of the framework, and investigates advantages and disadvantages of different solutions.

The increasing use of wireless sensor networks (WSNs) calls for solutions for mastering the complexity due to WSN heterogeneity and differentiated user needs. This paper presents an architecture, named iCAAS, designed to collect, to store, to manage and to make available to users data received from heterogeneous WSNs. The aim of the architecture is to adaptively deliver data to users depending on their specific interests and irrespective of adopted terminals (e.g., mobile device, workstation, ...) and of sensor networks details. The contribution of the paper is twofold. First we detail the requirements that these types of architectures should meet to fill the gap between sensors details and users needs. Second, we describe the structural organization of the proposed architecture, designed by taking into account the defined requirements. Implementation details and case studies are also provided, showing the effectiveness of the architecture when used in real world application scenarios.