Abstract

Over the last years, big data has emerged as a new paradigm for the processing and analysis of massive volumes of data. Big data processing has been combined with service and cloud computing, leading to a new class of services called “Big Services”. In this new model, services can be seen as an abstract layer that hides the complexity of the processed big data. To meet users’ complex and heterogeneous needs in the era of big data, service reuse is a natural and efficient means that helps orchestrating available services’ operations, to provide customer on-demand big services. However different from traditional Web service composition, composing big services refers to the reuse of, not only existing high-quality services, but also high-quality data sources, while taking into account their security constraints (e.g., data provenance, threat level and data leakage). Moreover, composing heterogeneous and large-scale data-centric services faces several challenges, apart from security risks, such as the big services’ high execution time and the incompatibility between providers’ policies across multiple domains and clouds. Aiming to solve the above issues, we propose a scalable approach for big service composition, which considers not only the quality of reused services (QoS), but also the quality of their consumed data sources (QoD). Since the correct representation of big services requirements is the first step towards an effective composition, we first propose a quality model for big services and we quantify the data breaches using L-Severity metrics. Then to facilitate processing and mining big services’ related information during composition, we exploit the strong mathematical foundation of fuzzy Relational Concept Analysis (fuzzy RCA) to build the big services’ repository as a lattice family. We also used fuzzy RCA to cluster services and data sources based on various criteria, including their quality levels, their domains, and the relationships between them. Finally, we define algorithms that parse the lattice family to select and compose high-quality and secure big services in a parallel fashion. The proposed method, which is implemented on top of Spark big data framework, is compared with two existing approaches, and experimental studies proved the effectiveness of our big service composition approach in terms of QoD-aware composition, scalability, and security breaches.

Abstract

Recently, cloud computing has been combined with big data processing leading to a new model of services called big services. This model addresses the customers’ complex requirements by reusing and aggregating existing services from various domains and delivery models, and from multiple cloud availability zones. Existing web/cloud service composition approaches are not adequate for the big service context due to many reasons, including the large volume of data, the cross-domain and cross-cloud interoperability issues, etc. Considering the aforementioned facts, we provide a solution to the big service composition issue, by taking advantage of relational concept analysis (RCA), as a clustering method, and composite particle swarm optimisation (CPSO), as an optimisation technique. RCA is used to model the big service environment, whereas CPSO helps continuously optimising the quality of big service composition. The implementation and experimental studies on our approach have proven its feasibility and efficiency.

Abstract

Over the last decade, cloud computing has emerged as a new paradigm for delivering various on-demand virtualized resources as services. Cloud services have inherited not only the major characteristics of web services but also their classical issues, in particular, the interoperability issues and the heterogeneous nature of their hosting environments. This latter problem must be taken into account when composing various cloud services, in order to answer users’ complex requirements. Moreover, leading cloud providers started to offer their services across multiple clouds. This adds a new factor of heterogeneity, as composition engines must take into consideration the heterogeneity not only at the service level (eg, service descriptions) but also at the cloud level (eg, pricing models, security policies). In this context, the semantics of multicloud actors must be incorporated into the multicloud service composition (MCSC) process. However, most existing approaches have treated the semantic service composition in traditional single-cloud environments. The few works in multicloud settings have ignored the semantics of cloud zones and resources. Moreover, they often focus on the general aspect of MCSC (eg, horizontal or vertical compositions). Even the few researchers who have addressed both vertical and horizontal service compositions, conducted their research studies in the context of single- cloud environments, which were proven to be unrealistic and offer limited quality of service (QoS) and security support. To ensure a high interoperability when composing services from multiple heterogeneous clouds and to enable a horizontal/vertical semantic service compositions, we take advantage of a standardized and semantically enriched generic service description, including all aspects (technical, operational, business, semantic, contextual) and supporting different cloud service models (SaaS, PaaS, IaaS, etc). We also incorporate Semantic Web Rule Language into the MCSC process to enable not only rule-based reasoning about various composition constraints (eg, QoS constraints, cloud zones constraints) but also to provide accurate semantic matching of cloud services’ capabilities. Conducted experiments have proven the ability of our approach to combine high-quality services from the optimal number of clouds.

Abstract

Sleep analysis is considered as an important process in sleep disorders identification and highly dependent of sleep scoring. Sleep scoring is a complex, time consuming and exhausting task for experts. In this paper, we propose an automatic sleep scoring model based on unsupervised learning to avoid the pre-labeling task. Taking advantage of the distributed nature of Multi-agent Systems (MAS), we propose a classification model based on various physiological signals coming from heterogeneous sources. The proposed model offers a totally cooperative learning to automatically score sleep into several stages based on unlabeled data. The existing heterogeneous adaptive agents are dealing with a dynamic environment of various physiological signals. The efficiency of our approach was investigated using real data. Promising results were reached according to a comparative study carried out with the often used classification models. The generic proposed model could be used in fields where data are coming from heterogeneous sources and classification rules are not predefined.

Abstract

Many decision making situations are characterized by a hierarchical structure where a lower-level (follower) optimization problem appears as a constraint of the upper-level (leader) one. Such kind of situations is usually modeled as a BLOP (Bi-Level Optimization Problem). The resolution of the latter usually has a heavy computational cost because the evaluation of a single upper-level solution requires finding its corresponding (near) optimal lower-level one. When several objectives are optimized in each level, the BLOP becomes a multi-objective task and more computationally costly as the optimum corresponds to a whole non-dominated solution set, called the PF (Pareto Front). Despite the considerable number of recent works in multi-objective evolutionary bi-level optimization, the number of methods that could be applied to the combinatorial (discrete) case is much reduced. Motivated by this observation, we propose in this paper an Indicator-Based version of our recently proposed Co-Evolutionary Migration-Based Algorithm (CEMBA), that we name IB-CEMBA, to solve combinatorial multi-objective BLOPs. The indicator-based search choice is justified by two arguments. On the one hand, it allows selecting the solution having the maximal marginal contribution in terms of the performance indicator from the lower-level PF. On the other hand, it encourages both convergence and diversity at the upper-level. The comparative experimental study reveals the outperformance of IB-CEMBA on a multi-objective bi-level production-distribution problem. From the effectiveness viewpoint, the upper-level hyper-volume values and inverted generational distance ones vary in the intervals [0.8500, 0.9710] and [0.0072, 0.2420], respectively. From the efficiency viewpoint, IB-CEMBA has a good reduction rate of the Number of Function Evaluations (NFEs), lying in the interval [30.13%, 54.09%]. To further show the versatility of our algorithm, we have developed a case study in machine learning, and more specifically we have addressed the bi-level multi-objective feature construction problem.

Abstract

In this paper, we introduce a framework for knowledge reuse and deduction in mechanical
products design and development. The proposed system e®ectively exploits the capitalized and
inferred knowledge. To this end, we settled up an ontology dealing with the design process of
mechanical products such as \the car ». The ontology-based framework is supported by a
software tool that brings an automatic and personalized assistance to correspondent actors
using the deduction process. Indeed, the systems provides the relevant knowledge to the suitable
users in order to facilitate their professional tasks considering their roles and collaboration.
Experimental results have demonstrated the e®ectiveness of reusing knowledge during product
development lifecycle.

Abstract

Abstract

Cloud computing has reached huge popularity for delivering on-demand services on a pay-per-use basis over the internet. However, since the number of cloud users evolves, multi-cloud environment has been introduced where clouds are interconnected in order to satisfy customers’ requirements. Task scheduling in such environments is very challenging mainly due to the heterogeneity of resources. In this paper, a budget-constrained dynamic Bag-of-Tasks scheduling algorithm for heterogeneous multi-cloud environment is proposed. By performing experiments on synthetic data sets that we propose, we demonstrate the effectiveness of the algorithm in terms of makespan.