Cloud computing is gaining popularity by giving access to seemingly unlimited virtual resources. However, Cloud data centres are built with physical resources and their electricity consumption has been continuously growing over the past decades. Microservices are an important building block of Cloud applications, calling for new solutions to observe their energy consumption. Distributed tracing is widely deployed to diagnose latency and failures in microservice-based applications, yet it does not expose the energy cost of individual end-user requests. Such a gap limits energy-aware debugging, accountability, and control. This paper presents Spanergy, an energy-aware distributed tracing approach that correlates per-microservice power measurements with traces and that attributes measured energy consumption to request segments, i.e. trace spans. We showcase Spanergy with synchronous request chains and asynchronous interactions across microservices. We present a rigorous experimental protocol and statistical analysis plan to quantify overhead and to validate conservation and coverage properties on realistic configurations. Enabling OpenTelemetry tracing increased total experiment energy by 59.1% relative to the uninstrumented baseline, and Spanergy post-processing added 15.2% of the baseline energy. Hence, Spanergy's incremental energy cost is smaller than the energy overhead of enabling tracing itself, making the approach lightweight in practice. Spanergy also reveals that a non-negligible fraction of request energy comes from spans outside the latency-critical path. These results show that energy-aware tracing is feasible at modest overhead and provides actionable insights for energy-efficient microservices.

Data usage control enables data owners to enforce policies for their data, by defining authorizations, but also obligations, which are actions to be performed before, during or after being granted access such as accepting web cookies, and conditions bearing on the system and environment attributes, e.g., the time. Usage control is often coupled with information flow control to monitor how data are propagated. While usage control is well established and modeled in centralized systems, the literature has only partially addressed usage control for distributed systems, for instance by distributing the usage control system components. However, when it comes to assigning policy to certain data, it is always enforced by a central authority. This paper proposes an extended usage control model to integrate decentralized information flow control (DIFC), which enables users to decide collectively which policy to apply to their common data. Functions to handle connection status aspects are also considered, for dynamic Internet of Things (IoT) or peer-to-peer networks where parts of the distributed network can be disconnected. Architectural aspects and formal definitions to enable decentralized policies for shared data are proposed as a novelty, resulting from the integration of DIFC. We used the TLA+ formal specification language on the proposed model and its attached model checker TLC to detect potential issues. We detected potential deadlocks due to the new connection functions as well as temporal ordering issues then suggested mitigations accordingly. A privacy analysis is provided using a car-sharing scenario to highlight the benefits of usage control.

The Internet of Things (IoT) brings new ways to collect privacy-sensitive data from billions of devices. Well-tailored distributed ledger technologies (DLTs) can provide high transaction processing capacities to IoT devices in a decentralized fashion. However, privacy aspects are often neglected or unsatisfying, with a focus mainly on performance and security. In this article, we introduce decentralized usage control mechanisms to empower IoT devices to control the data they generate. Usage control defines obligations, i.e., actions to be fulfilled to be granted access, and conditions on the system in addition to data dissemination control. The originality of this article is to consider the usage control system as a component of distributed ledger networks, instead of an external tool. With this integration, both technologies work in synergy, benefiting their privacy, security, and performance. We evaluated the performance improvements of integration using the IOTA technology, particularly suitable due to the participation of small devices in the consensus. The results of the tests on a private network show an approximate 90% decrease of the time needed for the usage control system to push a transaction and make its access decision in the integrated setting, regardless of the number of nodes in the network.

In the last years, Internet of Things (IoT) platforms have been designed to provide IoT applications with various services such as device discovery, context management, and data filtering. The lack of standardization has led each IoT platform to propose its own abstractions, APIs, and data models. As a consequence, programming interactions between an IoT consuming application and an IoT platform is time-consuming, is error prone, and depends on the developers’ level of knowledge about the IoT platform. To address these issues, this article introduces IoTvar , a middleware library deployed on the IoT consumer application that manages all its interactions with IoT platforms. IoTvar relies on declaring variables automatically mapped to sensors whose values are transparently updated with sensor observations through proxies on the client side. This article presents the IoTvar architecture and shows how it has been integrated into the FIWARE, OM2M, and muDEBS platforms. We also report the results of experiments performed to evaluate IoTvar, showing that it reduces the effort required to declare and manage IoT variables and has no considerable impact on CPU, memory, and energy consumption.

International audience

Microservice architectures focus on developing modular and independent functional units, which can be automatically deployed, enabling agile DevOps. One major challenge is to manage the rapid evolutionary changes in microservices and perform continuous redeployment without interrupting the application execution. The existing solutions provide limited capacities to help software architects model, plan, and perform version management activities. The architects lack a representation of a microservice architecture with versions tracking. In this paper, we propose runtime models that distinguishes the type model from the instance model, and we build up an evolution graph of configuration snapshots of types and instances to allow the traceability of microservice versions and their deployment. We demonstrate our solution with an illustrative application that involves synchronous (RPC calls) and asynchronous (publish-subscribe) interaction within information systems.

International audience

International audience

Failure prediction of industrial systems is a promising application domain for data mining approaches and should naturally rely on log messages which are a prime source of data as they are generated by many systems. However, before extracting relevant information of such log messages, another critical step is to parse the logs, that is to say to transform a raw unstructured text from the log messages into a suitable input for data mining. These two problems (log parsing then log mining) are often studied separately while they are directly related in the context of failure prediction; moreover, few performance benchmarks are publicly available. In this paper, we focus on the impact of log parsing techniques via natural language processing on the performances of log mining on two datasets. The first one is a log of an industrial aeronautical system comprising over 4,500,000 messages collected over one year of operation; the second one is a public benchmark set from an HDFS cluster. On the latter, we show that it is possible to raise the F-score from 96% to 99.2% while using simpler and more robust log parsing techniques that require less parameter tuning provided that they are correctly combined with log mining techniques.

Hard drives are an essential component of modern data storage. In order to reduce the risk of data loss, hard drive failure prediction methods using the Self-Monitoring, Analysis and Reporting Technology attributes have been proposed. However, these methods were developed from datasets not necessarily representative of operational systems. In this paper, we consider the Backblaze public dataset, a recent operational dataset from over 47,000 drives, exhibiting hard drive heterogeneity with 81 models from 5 manufacturers, an extremely unbalanced ratio of 5000:1 between healthy and failure samples and a realworld loosely controlled environment. We observe that existing predictive models no longer perform sufficiently well on this dataset. We therefore selected machine learning classification methods able to deal with a very unbalanced training set, namely SVM, RF and GBT, and adapted them to the specific constraints of hard drive failure prediction. Our results reach over 95% precision and 67% recall on a one year real-world public dataset of over 12 million records with only 2586 failures.

International audience

The PERCO platform is an industry-provided software platform that specifically addresses the requirements of highly available, dependable autonomous systems. Its basic tenet is to use UML specifications to build as much of the application as possible, while integrating real-time and fault-tolerant properties at the architectural level. PERCO was designed and built within the Alcatel-Thomson Common Laboratory (LCAT).

We report on the design and simulation of novel algorithms which will ensure that application software runs correctly on a MIMD system in which processing units (PU) can fail. The effect of these algorithms is evaluated for random task graphs using simulation as failure rates increase. An example of a specific application is also examined (the Fast Fourier Transform) for which we construct the task graph and then simulate its execution under various values of the failure rates of processors.