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.

This paper presents PlanIoT, a middleware approach for enabling adaptive data flow management in IoT-enhanced spaces (e.g., buildings) using automated planning methodologies. Today’s sensorized spaces deploy applications falling to diverse categories such as analytics, real-time, transactional, video streaming and emergency response. Depending on the category, applications have different QoS requirements related to timely delivery, networking resources, accuracy, etc. Typically, state-of-the-art data exchange systems introduce policies for bandwidth allocation or prioritization for specific data types and applications (e.g., camera data). PlanIoT introduces a generic QoS model to evaluate the performance of data flowing in Edge infrastructures and generates their performance metrics dataset. Such a dataset is used as input to automated planning representations to intelligently satisfy QoS requirements of deployed applications. The experimental results show that PlanIoT improves the end-to-end response time of time-sensitive flows by more than 50%, especially with an overloaded Edge infrastructure. We also show the adaptivity of our approach by considering emergency cases that require Edge infrastructure reconfiguration.

This paper presents the implementation and guideline of PlanIoT, an adaptive flow management framework for IoT-enhanced spaces. Such spaces are composed of applications deployed at the Edge with varying QoS requirements in terms of response time, timely delivery, throughput, etc. Configuring the Edge infrastructure requires tuning multiple parameters for optimal QoS satisfaction of applications. This is a complex task especially when the system has to be re-adapted (e.g., emergency situations). The PlanIoT framework manages application data flows in an adaptive manner. This is achieved via the following core software components: (i) a queueing network composer; (ii) an automated planning modeler; and (iii) an AI planner. This artifact presents implementation details of these components as well as guidelines for using the PlanIoT framework.

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.

Runtime systems are commonly used by parallel applications in order to efficiently exploit the underlying hardware resources. A runtime system hides the complexity of the management of the hardware and exposes a high-level interface to application developers. To this end, it makes decisions by relying on heuristics that estimate the future behavior of the application. In this paper, we propose Pythia, a library that serves as an oracle capable of predicting the future behavior of an application, so that the runtime system can make more informed decisions. Pythia builds on the deterministic nature of many HPC applications: by recording an execution trace, Pythia captures the application main behavior. The trace can be provided for future executions of the application, and a runtime system can ask for predictions of future program behavior. We evaluate Pythia on 13 MPI applications and show that Pythia can accurately predict the future of most of these applications, even when varying the problem size. We demonstrate how Pythia predictions can guide a runtime system optimization by implementing an adaptive thread parallelism strategy in GNU OpenMP runtime system. The evaluation shows that, thanks to Pythia prediction, the adaptive strategy reduces the execution time of an application by up to 38 %.

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.

In this paper, we propose an authoring tool for creating mobile mixed reality games (MMRGs), referred to as M2RGC (Mobile Mixed Reality Game Creator). Goal is to transform the game creation process in a task that is as interesting as playing the game itself. Therefore we offer a user-friendly interface through which different MMRGs can be created by defining the objects used in the game, their behavior and interaction with sensors (e.g. GPS, compass). The tool itself uses the power of MPEG-4 BIFS[1] capabilities, such as the possibility of grouping multimedia assets in a scene. We build a wrapper over these MPEG-4 BIFS functionalities allowing anyone to discover the experience of MMRGs creation. Concerning the client runtime for playing the game on mobile phones, we deploy a MPEG-4 player on the Windows Mobile and Android platforms.

Mobile ad-hoc networks, MANETs, are self-organized and very dynamic systems where processes have no global knowledge of the system. In this paper, we propose a model that characterizes the dynamics of MANETs in the sense that it considers that paths between nodes are dynamically built and the system can have infinitely many processes but the network may present finite stable partitions. We also propose an algorithm that implements an eventually perfect partition participant detector PD which eventually detects the participant nodes of stable partitions.

In mobile environment, nodes can move around and voluntarily leave or join the network. Furthermore, they can crash or be disconnected from the network due to the absence of network signals. Therefore, failure, disconnection and mobility may create partitions in wireless networks which should be detected for fault and disconnection tolerance reasons. We present in this article an architecture of local and distributed detectors for mobile networks that detect failures, disconnections, and partitions. It is basically composed of three unreliable detectors: a heartbeat failure detector, a vector-based disconnection detector, and an eventually perfect partition detector.