Search

Monitoring is a core practice in any software system. Trends in microservices systems exacerbate the role of monitoring and pose novel challenges to data sources being used for monitoring, such as event logs. Current deployments create a distinct log per microservice; moreover, composing microservices by different vendors exacerbates format and semantic heterogeneity of logs. Understanding and traversing the logs from different microservices demands for substantial cognitive work by human experts. This paper proposes a novel approach to accompany microservices logs with black box tracing to help practitioners in making informed decisions for troubleshooting. Our approach is based on the passive tracing of request-response messages of the REpresentational State Transfer (REST) communication model. Differently from many existing tools for microservices, our tracing is application transparent and non-intrusive. We present an implementation called MetroFunnel and conduct an assessment in the context of two case studies: a Clearwater IP Multimedia Subsystem (IMS) setup consisting of Docker microservices and a Kubernetes orchestrator deployment hosting tens of microservices. MetroFunnel allows making useful attributions in traversing the logs; more important, it reduces the size of collected monitoring data at negligible performance overhead with respect to traditional logs.

In modern smartphones, system performances are tightly related to a variety of underlying subsystems. In particular, internal storage, along the years, has become crucial because it is extensively used to access content relevant to the system and, finally, to the end user. To understand its role in a commercial Android smartphone and to evaluate its effects on the User experience, within the context of a real usage, we analyzed Linux page fault handling, a critical mechanism that puts pressure on storage devices and may cause system inefficiencies. A kernel tracing technique has been conceived for real-time measurement of Android applications and services on commercial smartphones. The experimental results presented in this work are derived from the use of this kernel tracing on a 64-bit Android smartphone, equipped with a ufs storage subsystem. The main subject of the study is major page fault handling, a kernel mechanism behind many end-user actions recognized at industry level as source of possible performance deterioration in a smartphone. The analysis shows that major page fault handling is dominated by read accesses to ufs (between 30% and 40% of the total time), and that the related storage traffic is significantly affected by the ReadAhead mechanism, which is not always efficient.

Real-time containers can be seen as the convergence of hard real-time operating systems and container-based virtualization technologies that can be used to build mixed-criticality cloud systems. In this paper we investigate the implementation of this concept based on earliest deadline first and server-based scheduling on a Linux kernel with the RTAI real-time patch. With respect to earlier proposals, our solution allows more flexibility with simplified admission checks and better utilization of CPU resources. We compare the solution with the SCHED DEADLINE scheduling class, already available in the Linux kernel, and we show, with experiments on real hardware, that the proposal allows to prevent timing failures to tasks running within real-time containers, despite the presence of disturbances.

Emerging Multiprocessor System-on-Chip (MPSoC) platforms, featuring asymmetric multi-processing (AMP), are becoming the underlying tool to develop real-time embedded mixed-criticality systems. These type of systems are key enablers for the emerging Industry 4.0 and Industrial Internet of Things revolution. Virtualization technologies can even more boost the opportunities brought by MPSoCs, providing unique isolation and flexibility features in next generation embedded devices. However, the management of hardware accelerators, such as realtime processing units (RPUs) available on today's MPSoCs, is still an open challenge. In this paper, we propose a virtualization-based architectural solution to RPU sharing in modern MPSoCs. The aim is to isolate the execution on RPUs of many tasks offloaded by several different VMs running on traditional application processors on the same chip. The practical implications of such architecture are discussed in the context of a case study regarding the real-time plasma vertical stabilization control in the ITER11ITER is a Nuclear Facility INB-174. The views and opinions expressed herein do not necessarily reflect those of the ITER Organization.experimental fusion power plant.

Phishing attacks continue to pose a major threat for computer system defenders, often forming the first step in a multi-stage attack. There have been great strides made in phishing detection; however, some phishing emails appear to pass through filters by making simple structural and semantic changes to the messages. We tackle this problem through the use of a machine learning classifier operating on a large corpus of phishing and legitimate emails. We design SAFe-PC (Semi-Automated Feature generation for Phish Classification), a system to extract features, elevating some to higher level features, that are meant to defeat common phishing email detection strategies. To evaluate SAFe-PC , we collect a large corpus of phishing emails from the central IT organization at a tier-1 university. The execution of SAFe-PC on the dataset exposes hitherto unknown insights on phishing campaigns directed at university users. SAFe-PC detects more than 70 percent of the emails that had eluded our production deployment of Sophos, a state-of-the-art email filtering tool. It also outperforms SpamAssassin, a commonly used email filtering tool. We also developed an online version of SAFe-PC , that can be incrementally retrained with new samples. Its detection performance improves with time as new samples are collected, while the time to retrain the classifier stays constant.

Cloud computing is a key supporting technology driving the fourth industrial revolution, spanning from the Internet of Things (IoT) and Cloud for Telecoms (C4T) to Industry 4.0 to Smart Cities, and so on. This pervasiveness of cloud technology is due to the ability to easily share and obtain resources on a pay-per-use and elastic provisioning model. Several enterprises use the cloud as a cheap solution to the achieve computational and storage capabilities they need without incurring the costs associated with owning and maintaining data centers. This implies that they rely on the correctness of the services provided by the cloud platforms and that any possible outage could result in considerable loss of reputation and money, which could effectively bring actions against the cloud service provider for violations to the agreed upon Service Level Agreement (SLA).

The industrial Internet of Things (IIoT) is currently foreseen as a foundation to implement the Industry 4.0 vision. However, device heterogeneity and the need of integration and configuration exposes the industrial infrastructure to potential threats, such as black-hole, man-in-the-middle, and malicious configuration attacks. In this article, we investigate how to manage distributed trust information and to enable trusted configuration actions in the IIoT, by opportunistically intermingling blockchain with the software defined networking and container orchestration technologies. In particular, we focus on how the joint and coordinated adoption of such technologies can make technicians’ interventions on industrial equipment both easier and more trusted. To this purpose, we present the design of a software architecture to simplify the management, configuration, and assessment of IIoT systems, and we discuss our experiences with the application of the proposed architecture in a railways use case.

Internet of Vehicles (IoV) requires trust management to implement effective authentication and authorization of nodes, as it is not possible to establish trusted connections to the roadside components that eventually a vehicle will meet during its journey, ahead of time. To cope with this issue, dynamic access control is required, where authorizations are granted considering security policies and the node trustworthiness. However, decentralized trust management is a preferable solution, but it implies a considerable consumption of energy. Consumption is further exacerbated by the means needed to protect from attacks the trust management entities themselves. This work proposes suitable trust management for the IoT and IoV by exploiting the eventual consistency and security guarantees of blockchain. The design of a solution based on such technology is described, and an empirical assessment of its protection degree is provided.

Computer applications, such as servers, databases and middleware, ubiquitously emit execution traces stored in log files. The use of logs for the analysis of application failures is known since the early days of computers. Field data studies have shown that application logs are fraught with uncertainty, i.e., missing or noisy events in the logs. A body of research that has dealt successfully with uncertainty in event logs is process mining from the business process management community, specifically by discovering process models. The literature has shown the value of process mining across several domains, but as yet there is no study that quantifies possible improvements from using process models, and the impact of uncertainty in the context of application failures. This work addresses the use of process mining for detecting failures from application logs. First, process models are discovered from logs; then conformance checking is used to detect deviations from the models. We contribute to knowledge engineering research with a systematic measurement study that quantifies the failure detection capability of conformance checking in spite of missing events, and its accuracy with respect to process models obtained from noisy logs. Analysis is done with a dataset of 55,462 execution traces from three independent real-life applications. We obtain a mixed answer depending on the application under test; our measurements provide insights into the use of process mining for failure analysis.

IT organizations rely on a variety of independent security monitors and data sources to develop situational awareness for detecting and responding to security incidents. In spite of the advances in Security Information and Event Management (SIEM) for handling monitoring data in production environments, computer defense still depends on many cognitive human processes. In this context, having machines doing part of the cognitive work in lieu of humans is by now a real necessity. We present our framework towards the vision of cognitive SIEM, its building components and ongoing work on the topic.

The ITER Plasma Control System (PCS) is an essential component for ITER operations. It will include multiple controls loops as well as a number of support functions dedicated to providing input control parameters and distributing commands to actuators. In addition, a supervisory system within the PCS architecture will manage the orchestration of the PCS control loops during the discharge as well as an Exception handling system to react to real-time changes in the plasma and/or in the plant systems. To investigate and develop controllers and supervisory techniques, a simulation platform is being developed to support the staged evolution of the PCS. The PCS Simulation Platform (PCSSP), based on Matlab/Simulink, is used to design and assess PCS control functions and verify the consistency of the PCS architecture. Moreover, the PCSSP will assist in implementing PCS algorithms, directly deploying PCS code on the ITER Real Time Framework (RTF). This paper will present the overall work-flow processes from control simulations, control assessment, code generation, interfaces and deployment into the RTF and PCS commissioning. This will include interface management and co-ordination with ITER investment protection, the pulse scheduling system, and the plant system configuration during operations. How the entire preparation process will be managed, for ITER first plasma operations will be reported here.

Over the past decades logs have been widely used for detecting and analyzing failures of computer applications. Nevertheless, it is widely accepted by the scientific community that failures might go undetected in the logs. This paper proposes a measurement study with a dataset of 3,794 log traces obtained from normative and failure runs of the Apache web server. We use process mining (i) to infer a model of the normative log behavior, e.g., presence and ordering of messages in the traces, and (ii) to detect failures within arbitrary traces by looking for deviations from the model (conformance checking). Analysis is done with the Integer Linear Programming (ILP) Miner, Inductive Miner and Alpha++ Miner algorithms. Our measurements indicate that, although only around 18% failure traces contain explicit error keywords and phrases, conformance checking allows detecting up to 87% failures at high precision, which means that most of the errors are hidden across the traces.

Recent advances in contextual anomaly detection attempt to combine resource metrics and event logs to uncover unexpected system behaviors at run-time. This is highly relevant for critical software systems, where monitoring is often mandated by international standards and guidelines. In this paper, we analyze the effectiveness of a metrics-logs contextual anomaly detection technique in a middleware for Air Traffic Control systems. Our study addresses the challenges of applying such techniques to a new case study with a dense volume of logs, and finer monitoring sampling rate. Guided by our experimental results, we propose and evaluate several actionable improvements, which include a change detection algorithm and the use of time windows on contextual anomaly detection.

This paper introduces real-time containers as a lightweight solution, if compared to virtual machines, to achieve temporal and fault isolation in mixed-criticality systems. The paper presents a reference architecture and an initial prototype implementation of the concept, using Docker containers on top of a patched real-time Linux kernel.

The idea of turning data centers executing scientific batch jobs into private clouds is as attractive as troubling. Cloud platforms may help both in limiting power consumption and in implementing fault tolerance strategies. However, there is also the fear that performance may worsen, and that the electricity required for longer job duration and fault tolerance implementation may overcome the saved one. In this paper, we present the consumability analysis for assessing the impact of cloud and fault tolerance tunings on scientific processing systems. The analysis considers performance, consumption, and dependability aspects, jointly. The aim is to pinpoint if, for a given system, there is a setting where consumption and job failure rate decrease, while performance is not affected. Applied to the scientific data center at our University, the analysis allowed us to find the proper selection of virtual machines’ configuration, consolidation strategy, and fault tolerance tuning.

Trust management is extremely important for sensor networks, and the emerging vision of the Internet of Things (IoT) does not represent an exception. In fact, it allows to realize a dynamic access control needed to cope with internal attacks conducted by compromised nodes, which are likely to occur in real world. However, trust management implies a considerable consumption of energy, due to the amount of messages exchanged to collect reputation scores. Consumption is further exacerbated by the means needed to protect from attacks the trust management entities themselves. This work proposes a suitable trust management for the IoT by exploiting the eventual consistency and security guarantees of blockchain. The design of a solution based on such a technology is described, and a qualitative assessment of its protection degree is provided.

This paper presents the notion of real-time containers, or rt-cases, conceived as the convergence of software container technologies, such as Linux Containers and/or Docker, and real-time operating systems. The idea is to allow critical containers, characterized by stringent timeliness and reliability requirements, to cohabit with traditional non real-time containers on the same hardware. The approach allows to keep the advantages of real-time virtualization, largely adopted in the industry, while reducing its inherent scalability limitation when to be applied to large-scale mixed-criticality systems. The paper provides a reference architecture scheme for implementing the real-time container concept on top on a patched real-time Linux kernel, and it overviews the challenges to be faced to implement the rt-case vision.

Critical information systems strongly rely on event logging techniques to collect data, such as housekeeping/error events, execution traces and dumps of variables, into unstructured text logs. Event logs are the primary source to gain actionable intelligence from production systems. In spite of the recognized importance, system/application logs remain quite underutilized in security analytics when compared to conventional and structured data sources, such as audit traces, network flows and intrusion detection logs. This paper proposes a method to measure the occurrence of interesting activity (i.e., entries that should be followed up by analysts) within textual and heterogeneous runtime log streams. We use an entropy-based approach, which makes no assumptions on the structure of underlying log entries. Measurements have been done in a real-world Air Traffic Control information system through a data analytics framework. Experiments suggest that our entropy-based method represents a valuable complement to security analytics solutions.

Event logs have been widely used over the last three decades to analyze the failure behavior of a variety of systems. Nevertheless, the implementation of the logging mechanism lacks a systematic approach and collected logs are often inaccurate at reporting software failures: This is a threat to the validity of log-based failure analysis. This paper analyzes the limitations of current logging mechanisms and proposes a rule-based approach to make logs effective to analyze software failures. The approach leverages artifacts produced at system design time and puts forth a set of rules to formalize the placement of the logging instructions within the source code. The validity of the approach, with respect to traditional logging mechanisms, is shown by means of around 12,500 software fault injection experiments into real-world systems.

Grid computing is a largely adopted paradigm to federate geographically distributed data centers. Due to their size and complexity, grid systems are often affected by failures that may hinder the correct and timely execution of jobs, thus causing a non-negligible waste of computing resources. Despite the relevance of the problem, state-of-the-art management solutions for grid systems usually neglect the identification and handling of failures at runtime. Among the primary goals to be considered, we claim the need for novel approaches capable to achieve the objectives of scalable integration with efficient monitoring solutions and of fitting large and geographically distributed systems, where dynamic and configurable tradeoffs between overhead and targeted granularity are necessary. This paper proposes GAMESH, a Grid Architecture for scalable Monitoring and Enhanced dependable job ScHeduling. GAMESH is conceived as a completely distributed and highly efficient management infrastructure, concentrating on two crucial aspects for large-scale and multi-domain grid environments: (i) the scalable dissemination of monitoring data and (ii) the troubleshooting of job execution failures. GAMESH has been implemented and tested in a real deployment encompassing geographically distributed data centers across Europe. Experimental results show that GAMESH (i) enables the collection of measurements of both computing resources and conditions of task scheduling at geographically sparse sites, while imposing a limited overhead on the entire infrastructure, and (ii) provides a failure-aware scheduler able to improve the overall system performance, even in the presence of failures, by coordinating local job schedulers at multiple domains.

Virtualized multicore systems represent an emerging computing paradigm in the critical systems industry. Virtualization-based solutions leverage the different cores of the processor to run operating systems and applications within separate partitions, to support the development of parallel mixed-criticality systems, and to improve fault-tolerance by protecting and isolating the operating environments. The critical systems industry is subjected to international standards, which recommend fault injection as a mean to contribute with evidence to safety cases. This paper proposes a framework to inject hardware faults in virtualized multicore systems. Our proposal capitalizes on the error reporting architecture of modern processors and allows injecting faults both at hypervisor- and guest-OS-level. We implement the framework in the context of the widely used Intel Core i7 processor and Xen hypervisor. We demonstrate the use of the framework by means of about 60,000 injection experiments in a Linux-based virtualized multicore system installation.

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.

In the last years, context awareness, namely the provisioning of the current execution context to the application level, has received an increasing attention up to becoming a core capability in next generation mobile scenarios. Context awareness intrinsically forces a continuous delivery of context data to resource-constrained mobile devices, such as mobile phones and personal digital assistants, to allow application adaptation, and that can become too severe a constraint even for modern platforms (Android, iOS, etc.). This paper focuses on the realization of a context data distribution support for Android-based mobile phones with guaranteed quality levels on the context data delivery time. Android, notwithstanding its great potential, puts harsh constraints on the implementation of specific context distribution primitives, thus preventing the realization of a wide set of significant deployment scenarios. At this stage, we face that a large campaign of tests are necessary. We have collected main experimental results in a real testbed to highlight noteworthy details on the runtime performance obtainable with a real Android deployment. Copyright © 2012 John Wiley & Sons, Ltd.

Wireless Sensor Networks (WSNs) are widely recognized as a promising solution to build next-generation monitoring systems. Their industrial uptake is however still compromised by the low level of trust on their performance and dependability. Whereas analytical models represent a valid mean to assess nonfunctional properties via simulation, their wide use is still limited by the complexity and dynamicity of WSNs, which lead to unaffordable modeling costs. To reduce this gap between research achievements and industrial development, this paper presents a framework for the assessment of WSNs based on the automated generation of analytical models. The framework hides modeling details, and it allows designers to focus on simulation results to drive their design choices. Models are generated starting from a high-level specification of the system and by a preliminary characterization of its fault-free behavior, using behavioral simulators. The benefits of the framework are shown in the context of two case studies, based on the wireless monitoring of civil structures.

Crisis information systems have reliability as a key technical concern to be guaranteed, given the critical role of information sharing in crisis management. However, a common agreement is missing about the requirements to be satisfied. Moreover, scarce details are provided to describe the means to apply in order to satisfy the reliability requirements and/or to tolerate the faults that may affect a crisis information system. The scope of this paper is to determine the requirements to be satisfied in order to provide reliability, to survey the available literature and practice on this topic, and to propose an innovative solution for reliable crisis information systems in the context of the platform under development within the EU-funded DESTRIERO project.

The anytime, anywhere access view of nomadic computing is evolving towards all the time, everywhere views of pervasive computing. The all the time access requires mobile devices to be always connected, even if connectivity may be compromised due to Access Point overload and to transient signal degradations. The everywhere access requires mobile devices to use heterogeneous Access Points (ranging from Bluetooth and 802.11 to 2.5G and 3G cellulars), leading to high variability of the connection status. A mobility management solution that leverages connection availability, while enabling applications to be aware of the connection status, is thus needed. This paper proposes a Last Second Soft Handoff scheme that leverages the availability of connection. The proposed scheme has been integrated in a mobility management architecture, which provides connection awareness via an API, named NCSOCKS. Implementation issues are discussed and experimental results are provided.

The increasing popularity of Software as a Service (SaaS) stresses the need of solutions to predict failures and avoid service interruptions, which invariably result in SLA violations and severe loss of revenue. A promising approach to continuously monitor the correct functioning of the system is to check the execution conformance to a set of invariants, i.e., properties that must hold when the system is deemed to run correctly. In this paper we propose a framework and a tool to automatically discover invariants from application logs and to online detect their violation. The framework has been applied on 9 months of log events from a real-world SaaS application. Results show that the proposed tool is able to automatically select 12 invariants with a stringent goodness of fit criteria out of more than 500 potential relationships. We also show the usefulness of our approach to detect runtime issues from logs in the form of violations of selected invariants, corresponding to silent errors that usually go unnoticed by the system maintenance personnel, even if they could represent symptoms of upcoming service failures.

This paper presents the main objectives and preliminary results of the NAPOLI FUTURA project, which aims to define novel approaches for protecting critical infrastructures from cyber attacks. The paper focuses on the architectural design of the NAPOLI FUTURA platform. The platform leverages cutting-edge big data analytics solutions to detect security attacks and to support live migration of services in the context of Critical Information Infrastructures (CIIs).

Performance, energy efficiency, and dependability are key characteristics of batch systems, which can be differently affected when adopting virtualization. Scientific literature usually analyzes the variation with respect to different config- urations of one characteristic, or the trade-off between two. In this paper, instead, we assess the impact of virtualization encompassing all of them. Results show that the joint analysis helps in finding the proper tuning of the system for balancing costs and benefits due to virtualization and related techniques.

Understanding the bugs in software platforms is extremely valuable for developers, especially during the testing phase. However, this is a rarely investigated issue for open source Cloud platforms till date. In this paper, we present the analysis of 146 bug reports from Apache Virtual Computing Lab, a representative open source Cloud platform. Analysis is performed by means of an empirical approach tailored to open source Clouds. For VCL development and test teams, these results provide useful guidelines, e.g., directing volunteers' effort to components where more residual bugs are expected to be found.

The increasing complexity of smart phones makes them more susceptible to accidental failures. However, there is still little understanding on the dependability behavior of modern smart phones. In this paper, we propose the design and implementation of a logger to collect relevant failure data from iOS devices, such as iPhone and iPad. The preliminary use of the logger on real-world devices shows that it is able to collect meaningful failure data and to provide interesting insight on the dependability behavior of iOS.

Failure analysis is valuable to dependability engineers because it supports designing effective mitigation means, defining strategies to reduce maintenance costs, and improving system service. Event logs, which contain textual information about regular and anomalous events detected by the system under real workload conditions, represent a key source of data to conduct failure analysis. So far, event logs have been successfully used in a variety of domains. This chapter describes methodology and well-established techniques underlying log-based failure analysis. Description introduces the workflow leading to analysis results starting from the raw data in the log. Moreover, the chapter surveys relevant works in the area with the aim of highlighting main objectives and applications of log-based failure analysis. Discussion reveals benefits and limitations of logs for evaluating complex systems.

Context awareness, namely the provisioning of the current execution context at the application level, forces the continuous delivery of context data to resource-constrained mobile devices, and that can become too severe a constraint even for modern support (Android, iOS, etc.). This article focuses on the realization of a context data distribution infrastructure for Android-based mobile phones, and highlights important details on the implementation of specific context distribution primitives. Finally, we present new experimental results to assess the runtime performances obtainable with a real Android deployment.

This paper presents a novel approach to assess time coalescence techniques. These techniques are widely used to reconstruct the failure process of a system and to estimate dependability measurements from its event logs. The approach is based on the use of automatically generated logs, accompanied by the exact knowledge of the ground truth on the failure process. The assessment is conducted by comparing the presumed failure process, reconstructed via coalescence, with the ground truth. We focus on supercomputer logs, due to increasing importance of automatic event log analysis for these systems. Experimental results show how the approach allows to compare different time coalescence techniques and to identify their weaknesses with respect to given system settings. In addition, results revealed an interesting correlation between errors caused by the coalescence and errors in the estimation of dependability measurements. Index Terms—Event Log Analysis, supercomputer dependability, data coalescence, dependability assessment

The design and realization of health monitoring systems has attracted the interest of large communities both from industry and academia. Remote and continuous monitoring of patient’s vital signs is the target of an emerging business market that aims both to improve the quality of life of patients and to reduce costs of national healthcare services. Such applications, however, are particularly critical from the point of view of dependability. This presents the design of a set of services for the assurance of high degrees of dependability to generic mobile health monitoring systems. The design is based on the results of a detailed failure modes and effects analysis (FMEA), conducted to identify the typical dependability threats of health monitoring systems. The FMEA allowed the authors to conceive a set of configurable monitoring services, enriching the system with the ability to detect failures at runtime, and enabling the realization of dependable services for future mobile health monitoring systems.

Middleware plays a key role for the achievement of the mission of future large scale complex critical infrastructures, envisioned as federations of several heterogeneous systems over Internet. However, available approaches for data dissemination result still inadequate, since they are unable to scale and to jointly assure given QoS properties. In addition, the best-effort delivery strategy of Internet and the occurrence of node failures further exacerbate the correct and timely delivery of data, if the middleware is not equipped with means for tolerating such failures. This paper presents a peer-to-peer approach for resilient and scalable data dissemination over large-scale complex critical infrastructures. The approach is based on the adoption of epidemic dissemination algorithms between peer groups, combined with the semi-active replication of group leaders to tolerate failures and assure the resilient delivery of data, despite the increasing scale and heterogeneity of the federated system. The effectiveness of the approach is shown by means of extensive simulation experiments, based on Stochastic Activity Networks.

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 complexity of smart phones introduces new dependability threats. Often, novel applications and features are delivered with scarce testing, due to the impressive market pressure. Despite these concerns, there is still little understanding on the dependability behavior of today smart phones. This paper proposes the design of a logging platform for the Android OS. The logging platform enables the collection of failure data, useful to assess the dependability of smart phones at runtime. Preliminary experimental results on real-world Android devices show the feasibility of the approach and encourage further research activities.

Many critical services are nowadays provided by large and complex software systems. However, the increasing complexity introduces several sources of non-determinism, which may lead to hang failures: the system appears to be running, but part of its services is perceived as unresponsive. Online monitoring is the only way to detect and to promptly react to such failures. However, when dealing with off-the-shelf-based systems, online detection can be tricky since instrumentation and log data collection may not be feasible in practice. In this paper, a detection framework to cope with software hangs is proposed. The framework enables the non-intrusive monitoring of complex systems, based on multiple sources of data gathered at the operating system (OS) level. Collected data are then combined to reveal hang failures. The framework is evaluated through a fault injection campaign on two complex systems from the air traffic management (ATM) domain. Results show that the combination of several monitors at the OS level is effective to detect hang failures in terms of coverage and false positives and with a negligible impact on performance.

This paper presents an effective approach for injecting faults/errors in WSN nodes operating systems. The approach is based on the injection of faults at the assembly level. Results show that depending on the concurrency model and on the memory management, the operating systems react to injected errors differently, indicating that fault containment strategies and hang-checking assertions should be implemented to avoid spreading and activations of errors.

The dependability evaluation and management of complex systems is often based on the collection of field data from event logs. Nevertheless, key decisions about log production and management are usually left to the late stages of development, leading to heterogeneous, inaccurate, and redundant logs. This in turn decreases the level of trust on logs. This paper proposes to enrich traditional logging by defining a set of rules, to be followed at design time, specifically conceived to improve the quality of logged failure data and to ease the coalescence of redundant or equivalent data. A tool for processing our rule-based logs has been developed to show the feasibility of the approach. The tool is applied on a real-world case study in order to evaluate the effectiveness of the approach when compared with traditional logging.

On-line failure detection is an essential means to control and assess the dependability of complex and critical software systems. In such context, effective detection strategies are required, in order to minimize the possibility of catastrophic consequences. This objective is however difficult to achieve in complex systems, especially due to the several sources of non-determinism (e.g., multi-threading and distributed interaction) which may lead to software hangs, i.e., the system is active but no longer capable of delivering its services. The paper proposes a detection approach to uncover application hangs. It exploits multiple indirect data gathered at the operating system level to monitor the system and to trigger alarms if the observed behavior deviates from the expected one. By means of fault injection experiments conducted on a research prototype, it is shown how the combination of several operating system monitors actually leads to an high quality of detection, at an acceptable overhead.

While the new generation of hand-held devices, e.g., smart phones, support a rich set of applications, growing complexity of the hardware and runtime environment makes the devices susceptible to accidental errors and malicious attacks. Despite these concerns, very few studies have looked into the dependability of mobile phones. This paper presents measurement-based failure characterization of mobile phones. The analysis starts with a high level failure characterization of mobile phones based on data from publicly available web forums, where users post information on their experiences in using hand-held devices. This initial analysis is then used to guide the development of a failure data logger for collecting failure-related information on SymbianOS-based smart phones. Failure data is collected from 25 phones (in Italy and USA) over the period of 14 months. Key findings indicate that: (i) the majority of kernel exceptions are due to memory access violation errors (56%) and heap management problems (18%), and (ii) on average users experience a failure (freeze or self shutdown) every 11 days. While the study provide valuable insight into the failure sensitivity of smart-phones, more data and further analysis are needed before generalizing the results.

The increasing success of mobile-enabled embedded devices is stressing the need for software architectures facing mobility-related issues. This paper proposes a simple yet effective mobility management scheme to ease the development of mobile ubiquitous applications. The scheme seamlessly handles handoff events and provides ubiquitous applications with both location-awareness and mobility prediction support. An implementation prototype has been developed on real-world Bluetooth enabled devices. Experimental results are then obtained from the prototype, showing the effectiveness of the proposed scheme.

The overwhelming success of mobile devices and wireless communications is stressing the need for the development of mobility-aware services. Device mobility requires services adapting their behavior to sudden context changes and being aware of handoffs, which introduce unpredictable delays and intermittent discontinuities. Heterogeneity of wireless technologies (Wi-Fi, Bluetooth, 3G) complicates the situation, since a different treatment of context-awareness and handoffs is required for each solution. This paper presents a middleware architecture designed to ease mobility-aware service development. The architecture hides technology-specific mechanisms and offers a set of facilities for context awareness and handoff management. The architecture prototype works with Bluetooth and Wi-Fi, which today represent two of the most widespread wireless technologies. In addition, the paper discusses motivations and design details in the challenging context of mobile multimedia streaming applications.