A semantic-driven approach for Industry 4.0

Toward industry 4.0, modern manufacturing companies are aiming at building digital twins to manage physical assets, processes, people, and places. Since in this environment, massive amounts of data have been generated and collected, integration and management of various data sources is of paramount importance. In this context, cloud computing as a crucial part of Industry 4.0 facilitates distribution of computer resources without direct active management by users. Accordingly, an ontology enables efficient integration and management of data as a reference data model through representation of knowledge. Besides, data mining from massive data is very important to identify significant meaning of data, and to avoid unexpected errors through predictions from experiences described in data replica. This research addresses problems towards efficient integration and management of data for predictive maintenance.

Linear Scalability of Distributed Applications

Nicolas Bonvin

The explosion of social applications such as Facebook, LinkedIn and Twitter, of electronic commerce with companies like Amazon.com and Ebay.com, and of Internet search has created the need for new technologies and appropriate systems to manage effectively a considerable amount of data and users. These applications must run continuously every day of the year and must be capable of surviving sudden and abrupt load increases as well as all kinds of software, hardware, human and organizational failures. Increasing (or decreasing) the allocated resources of a distributed application in an elastic and scalable manner, while satisfying requirements on availability and performance in a cost-effective way, is essential for the commercial viability but it poses great challenges in today's infrastructures. Indeed, Cloud Computing can provide resources on demand: it now becomes easy to start dozens of servers in parallel (computational resources) or to store a huge amount of data (storage resources), even for a very limited period, paying only for the resources consumed. However, these complex infrastructures consisting of heterogeneous and low-cost resources are failure-prone. Also, although cloud resources are deemed to be virtually unlimited, only adequate resource management and demand multiplexing can meet customer requirements and avoid performance deteriorations. In this thesis, we deal with adaptive management of cloud resources under specific application requirements. First, in the intra-cloud environment, we address the problem of cloud storage resource management with availability guarantees and find the optimal resource allocation in a decentralized way by means of a virtual economy. Data replicas migrate, replicate or delete themselves according to their economic fitness. Our approach responds effectively to sudden load increases or failures and makes best use of the geographical distance between nodes to improve application-specific data availability. We then propose a decentralized approach for adaptive management of computational resources for applications requiring high availability and performance guarantees under load spikes, sudden failures or cloud resource updates. Our approach involves a virtual economy among service components (similar to the one among data replicas) and an innovative cascading scheme for setting up the performance goals of individual components so as to meet the overall application requirements. Our approach manages to meet application requirements with the minimum resources, by allocating new ones or releasing redundant ones. Finally, as cloud storage vendors offer online services at different rates, which can vary widely due to second-degree price discrimination, we present an inter-cloud storage resource allocation method to aggregate resources from different storage vendors and provide to the user a system which guarantees the best rate to host and serve its data, while satisfying the user requirements on availability, durability, latency, etc. Our system continuously optimizes the placement of data according to its type and usage pattern, and minimizes migration costs from one provider to another, thereby avoiding vendor lock-in.

EPFL2012

Rate allocation and optimized decoding in inter-session network coding

Eirina Bourtsoulatze

Recent advances in data processing and communication systems have led to a continuous increase in the amount of data communicated over today’s networks. These large volumes of data pose new challenges on the current networking infrastructure that only offers a best effort mechanism for data delivery. The emergence of new distributed network architectures, such as peer-to-peer networks and wireless mesh networks, and the need for efficient data delivery mechanisms have motivated researchers to reconsider the way that information is communicated and processed in the networks. This has given rise to a new research field called network coding. The network coding paradigm departs from the traditional routing principle where information is simply relayed by the network nodes towards the destination, and introduces some intelligence in the network through coding at the intermediate nodes. This in-network data processing has been proved to substantially improve the performance of data delivery systems in terms of throughput and error resilience in networks with high path diversity. Motivated by the promising results in the network coding research, we focus in this thesis on the design of network coding algorithms for simultaneous transmission of multiple data sources in overlay networks. We investigate several problems that arise in the context of inter-session network coding, namely (i) decoding delay minimization in inter-session network coding, (ii) distributed rate allocation for inter-session network coding and (iii) correlation-aware decoding of incomplete network coded data. We start by proposing a novel framework for data delivery from multiple sources to multiple clients in an overlay wireline network, where intermediate nodes employ randomized inter-session network coding. We consider networks with high resource diversity, which creates network coding opportunities with possibly large gains in terms of throughput, delay and error robustness. However, the coding operations in the intermediate nodes must be carefully designed in order to enable efficient data delivery. We look at the problem from the decoding delay perspective and design solutions that lead to a small decoding delay at clients through proper coding and rate allocation. We cast the optimization problem as a rate allocation problem, which seeks for the coding operations that minimize the average decoding delay in the client population. We demonstrate the validity of our algorithm through simulations in representative network topologies. The results show that an effective combination of intra- and inter-session network coding based on randomized linear coding permits to reach small decoding delays and to better exploit the available network resources even in challenging network settings. Next, we design a distributed rate allocation algorithm where the users decide locally how many intra- and inter-session network coded packets should be requested from the parent nodes in order to get minimal decoding delay. The capability to take coding decisions locally with only a partial knowledge of the network statistics is of crucial importance for applications where users are organized in dynamic overlay networks. We propose a receiver-driven communication protocol that operates in two rounds. First, the users request and obtain information regarding the network conditions and packet availability in their local neighborhood. Then, every user independently optimizes the rate allocation among different possible intra- and inter-session packet combinations to be requested from its parents. We also introduce the novel concept of equivalent flows, which permits to efficiently estimate the expected number of packets that are necessary for decoding and hence to simplify the rate allocation process. Experimental results indicate that our algorithm is capable of eliminating the bottlenecks and reducing the decoding delay of users with limited resources. We further investigate the application of the proposed distributed rate allocation algorithm to the transmission of video sequences and validate the performance of our system using the NS-3 simulator. The simulation results show that the proposed rate allocation algorithm is successful in improving the quality of the delivered video compared to intra-session network coding based solutions. Finally, we investigate the problem of decoding the source information from an incomplete set of network coded data with the help of source priors in a finite algebraic field. The inability to form a complete decoding system can be often caused by transmission losses or timing constraints imposed by the application. In this case, exact reconstruction of the source data by conventional algorithms such as Gaussian elimination is not feasible; however, partial recovery of the source data may still be possible, which can be useful in applications where approximate reconstruction is informative. We use the statistical characteristics of the source data in order to perform approximate decoding. We first analyze the performance of a hypothetical maximum a posteriori decoder, which recovers the source data from an incomplete set of network coded data given the joint statistics of the sources. We derive an upper bound on the probability of erroneous source sequence decoding as a function of the system parameters. We then propose a constructive solution to the approximate decoding problem and design an iterative decoding algorithm based on message passing, which jointly considers the network coding and the correlation constraints. We illustrate the performance of our decoding algorithm through extensive simulations on synthetic and real data sets. The results demonstrate that, even by using a simple correlation model expressed as a correlation noise between pairs of sources, the original source data can be partially decoded in practice from an incomplete set of network coded symbols. Overall, this thesis addresses several important issues related to the design of efficient data delivery methods with inter-session network coding. Our novel framework for decoding delay minimization can impact the development of practical inter-session network coding algorithms that are appropriate for applications with low delay requirements. Our rate allocation algorithms are able to exploit the high resource diversity of modern networking systems and represent an effective alternative in the development of distributed communication systems. Finally, our algorithm for data recovery from incomplete network coded data using correlation priors can contribute significantly to the improvement of the delivered data quality and provide new insights towards the design of joint source and network coding algorithms.

EPFL2013

Towards Seamless Continuation of Knowledge in Product Lifecycle Management

Apostolos Perdikakis

The current ICT for manufacturing landscape is characterized by scattered data formats, tools and processes dedicated to different phases in the product lifecycle. Due to the diversity of tools and data formats, manufacturing struggles to cope with new trends in this area. What is clearly missing in the current ICT landscape for manufacturing is an integrated, holistic view on data about products to be manufactured, resources (including human resources) and processes across the full product lifecycle. There is a need to formalize knowledge for the automatic integration in existing engineering tools. In this work ontology-based semantics were exploited to develop machine-understandable models, which are able to understand the meaning of the data and information they contain. The system developed using semantic web methods and tools is designed to support interoperability and data integration, as well as lead a way towards the usage of information contained in the system to extract useful knowledge. This knowledge may provide feedback for improving the design of future generations of products. Thus, the concept of seamless continuation of knowledge all along product-system lifecycles is introduced.The first step of this work was to study the state-of-the-art in PLM and Semantic web Technologies. This included theoretical background in concepts like Beginning Of Life (BOL), Middle Of Life (MOL) and End Of Life (EOL) regarding PLM as well as the definition and theoretical background of Ontologies and Semantic Web Technologies. After completing the study of theoretical background, more advanced research concepts and methodologies for both domains were thoroughly analysed. Examples include Closed Loop Lifecycle Management (CL2M) for PLM and NEON Method-ology for Ontology engineering. The next logical step followed was to find and discover relevant standards like STEP for PLM and W3C standards for Ontologies and Semantic Web Technologies. The engineering aspect of this study imposed the review of available tools both for PLM and Ontology Engineering. Ontology development tools like Protégé, TopBraid composer and Anzo Ontology editor were studied. More advanced software solutions for applications of the semantic web were also studied. Examples include Anzo Enterprise and the Open Semantic Framework. Inspiration also came from previous European projects like EC FP6 Promise. The problem of discontinuation, even after an Ontology model is created, is evident in most of the applications studied as background of this work. Ontology models have to be exploitable and that requires a framework of which they can be part of. The research for successful implementations of ontology driven frameworks was imperative. At first, the open seman-tic framework (osf) was the software stack that came into focus. After being thoroughly studied, it proved difficult to deploy and maintain, for it had still many problematic areas with its configuration and required people with very ad-vanced software skills (both in programing and system configuration). Nevertheless, the examples of use are promis-ing and give many ideas for exploitation. This finding triggered the efforts towards the definition and application of the solution proposed in this work, which has the following characteristics. First and most important, the solution is easy to deploy and maintain, since it is targeting SMEs and large enterprises that want to test the capabilities of the proposed[...]

EPFL2015