Toward the Sustainable Development of Machine Learning Applications in Industry 4.0

As the level of digitization in industrial environments increases, companies are striving to improve efficiency and resilience to unplanned disruptions through the development of machine learning (ML)-based applications. Still, sustainable deployment and operation beyond proofs-of-concept is a challenging and resource-intensive task in dynamic enviroments such as industry 4.0, often impeding practical adoption in the long-term and thus sustainable ML product development. In this work, we systematically identify these challenges based on the CRISP-ML process model phases by applying a design science research approach. To this end, we conducted 15 interviews with data science practitioners in industry 4.0. Following a qualitative content analysis, design requirements and design principles for the development and sustainable long-term deployment of ML systems are derived to address identified challenges such as robustness to, and management of data drift caused by time-dependencies and machine/product differences, missing metadata, interfaces to other IT systems, expectation management, and MLOps guidelines

A Generalized Service Replication Process in Distributed Environments

Replication is one of the main techniques aiming to improve Web services’ (WS) quality of service (QoS) in distributed environments, including clouds and mobile devices. Service replication is a way of improving WS performance and availability by creating several copies or replicas of Web services which work in parallel or sequentially under defined circumstances. In this paper, a generalized replication process for distributed environments is discussed based on established replication studies. The generalized replication process consists of three main steps: sensing the environment characteristics, determining the replication strategy, and implementing the selected replication strategy. To demonstrate application of the generalized replication process, a case study in the telecommunication domain is presented. The adequacy of the selected replication strategy is demonstrated by comparing it to another replication strategy as well as to a non-replicated service. The authors believe that a generalized replication process will help service providers to enhance QoS and accordingly attract more customer

Evaluating performance of web services in cloud computing environment with high availability

This paper presents an methodology for attaining high availability to the demands of the web clients. In order to improve in response time of web services during peak hours dynamic allocation of host nodes will be used in this research work. As web users are very demanding: they expect web services to be quickly accessible from the world 24*7. Fast response time leads to high availability of web services, while slow response time degrades the performance of web services. With the increasing trend of internet, it becomes a part of life. People use internet to help in their studies, business, shopping and many more things. To achieve this objective LAMP platform is used which are Linux, Apache, My SQL, and PHP. LAMP is used to increase the quality of product by using open source software. The proposed strategy will work as middle layer and provide highly availability to the web clients

An Actor-Critic-Based UAV-BSs Deployment Method for Dynamic Environments

In this paper, the real-time deployment of unmanned aerial vehicles (UAVs) as flying base stations (BSs) for optimizing the throughput of mobile users is investigated for UAV networks. This problem is formulated as a time-varying mixed-integer non-convex programming (MINP) problem, which is challenging to find an optimal solution in a short time with conventional optimization techniques. Hence, we propose an actor-critic-based (AC-based) deep reinforcement learning (DRL) method to find near-optimal UAV positions at every moment. In the proposed method, the process searching for the solution iteratively at a particular moment is modeled as a Markov decision process (MDP). To handle infinite state and action spaces and improve the robustness of the decision process, two powerful neural networks (NNs) are configured to evaluate the UAV position adjustments and make decisions, respectively. Compared with the heuristic algorithm, sequential least-squares programming and fixed UAVs methods, simulation results have shown that the proposed method outperforms these three benchmarks in terms of the throughput at every moment in UAV networks

Into the Unknown: Active Monitoring of Neural Networks

Neural-network classifiers achieve high accuracy when predicting the class of an input that they were trained to identify. Maintaining this accuracy in dynamic environments, where inputs frequently fall outside the fixed set of initially known classes, remains a challenge. The typical approach is to detect inputs from novel classes and retrain the classifier on an augmented dataset. However, not only the classifier but also the detection mechanism needs to adapt in order to distinguish between newly learned and yet unknown input classes. To address this challenge, we introduce an algorithmic framework for active monitoring of a neural network. A monitor wrapped in our framework operates in parallel with the neural network and interacts with a human user via a series of interpretable labeling queries for incremental adaptation. In addition, we propose an adaptive quantitative monitor to improve precision. An experimental evaluation on a diverse set of benchmarks with varying numbers of classes confirms the benefits of our active monitoring framework in dynamic scenarios.Comment: published at RV 202

Une démarche orientée modèle pour déployer des systèmes logiciels répartis

Revues des Sciences et Technologies de l'Information - "L'Objet, logiciel, base de données, réseaux" (RSTI série)International audienceDeployment of distributed systems involves many heterogeneous technologies. The system administrator has to 1) master the deployment of each technology, 2) adapt it to machine properties and 3) execute it in respect with its order dependencies. These tasks are strongly prone to errors. In this article, we present DeployWare, a model-based approach for complex distributed systems deployment. This approach relies on a metamodel split in two parts. The first allows us to describe properties, dependencies, and actions to perform to deploy software. The second allows us to compose many software instances. This metamodel allows some behavioural deployment verifications to be performed. DeployWare models can be projected onto a component-based execution platform which manages automatically machine's heterogeneity and orchestration of dependencies.Le déploiement de systèmes distribués met en jeu de nombreuses technologies hétérogènes. L'administrateur système doit 1) maîtriser le déploiement de chaque logiciel, 2) l'adapter aux propriétés des machines et 3) l'exécuter en respectant l'ordre de ses dépendances. Ces tâches sont fortement propices aux erreurs. Dans cet article, nous présentons DeployWare, une approche à base de modèles pour le déploiement de systèmes distribués complexes. Cette approche repose sur un méta-modèle en deux parties. La première permet de décrire les propriétés, les dépendances et actions à effectuer pour déployer des logiciels. La seconde permet d'assembler des instances de logiciels. Ces deux parties sont réalisées de manière à rendre possible la vérification comportementale des procédures de déploiement et des systèmes. Les modèles DeployWare sont projetés vers une plate-forme d'exécution à base de composants qui gère automatiquement l'hétérogénéité des machines et l'orchestration des dépendances