Abstract Over the last few years, big data have emerged as a paradigm for processing and analyzing a large volume of data. Coupled with other paradigms, such as cloud computing, service computing, and Internet of Things, big data processing takes advantage of the underlying cloud infrastructure, which allows hosting and managing massive amounts of data, while service computing allows to process and deliver various data sources as on‐demand services. This synergy between multiple paradigms has led to the emergence ofbig services, as a cross‐domain, large‐scale, and big data‐centric service model. Apart from the adaptation issues (e.g., need of high reaction to changes) inherited from other service models, the massiveness and heterogeneity of big services add a new factor of complexity to the way such a large‐scale service ecosystem is managed in case of execution deviations. Indeed, big services are often subject to frequent deviations at both the functional (e.g., service failure, QoS degradation, and IoT resource unavailability) and data (e.g., data source unavailability or access restrictions) levels. Handling these execution problems is beyond the capacity of traditional web/cloud service management tools, and the majority of big service approaches have targeted specific management operations, such as selection and composition. To maintain a moderate state and high quality of their cross‐domain execution, big services should be continuously monitored and managed in a scalable and autonomous way. To cope with the absence of self‐management frameworks for large‐scale services, the goal of this work is to design an autonomic management solution that takes the whole control of big services in an autonomous and distributed lifecycle process. We combine autonomic computing and big data processing paradigms to endow big services withself‐* andparallel processingcapabilities. The proposed management framework takes advantage of the well‐known MapReduce programming model and Apache Spark and manages big service's related data usingknowledge graph technology. We also define ascalable embedding modelthat allows processing and learning latent big service knowledge in a distributed manner. Finally, acooperative decision mechanismis defined to trigger non‐conflicting management policies in response to the captured deviations of the running big service. Big services' management tasks (monitoring, embedding, and decision), as well as the core modules (autonomic managers' controller, embedding module, and coordinator), are implemented on top of Apache Spark as MapReduce jobs, while the processed data are represented as resilient distributed dataset (RDD) structures. To exploit the shared information exchanged between the workers and the master node (coordinator), and for further resolution of conflicts between management policies, we endowed the proposed framework with a lightweight communication mechanism that allows transferring useful knowledge between the running map‐reduce tasks and filtering inappropriate intermediate data (e.g., conflicting actions). The experimental results proved the increased quality of embeddings and the high performance of autonomic managers in a parallel and cooperative setting, thanks to the shared knowledge.

Abstract Recent years have witnessed the emergence ofbig services, as a large‐scale big data‐centric service model, that resulted from the synergy between powerful computing paradigms (big data processing, service and cloud computing, Internet of Things, etc.). Big services are seen as a heterogeneous combination of physical and virtualized domain‐specific resources, with a huge volume of data and complex functionalities, all encapsulated and offered as services. This complexity of big services (composition units' heterogeneity, cross‐domain orientation, data massiveness), coupled with other environmental factors (cloud dynamicity, providers' policies, customer requirements) makes their management tasks beyond humans' capability. Therefore, endowing big service ecosystems with self‐adaptive behavior is a natural solution. To achieve this goal, this article models big services asautonomic computing systems, and structures their behavioral aspects (functional behavior, quality of service/data levels, management policies) as amulti‐view knowledge graph. To infer useful knowledge (e.g., conflicts between policies) for the autonomic big service's management tasks, we process the big service's knowledge graph (BSKG) via agraph neural network‐based graph embedding model. This latter is reinforced by anincremental learningmethod, that helps capturing the big services' frequent changes (e.g., QoS deviations, service failures, new policies), and drives autonomic managers to continuously update and enrich their knowledge w.r.t. the managed big service's current state. Finally, a flexibledecision mechanismexplores the BSKG structure and the latent knowledge, to locate and trigger the appropriate management policies, according to the big service's produced events.

Big services have recently emerged from the synergy between big data and cloud computing paradigms. This new big data-centric service model aims to provide customer-oriented massive services by combining both physical and virtualized resources from different domains. Although such complex ecosystem is able to process, encapsulate and offer huge volumes of data as services, its management operations are beyond the ability of human administrators, due to several challenges including the big services’ large-scale nature and complexity, the heterogeneity of their components (e.g., services, data sources, connected things), the dynamicity and uncertainty of their hosting cloud environments. To cope with the lack of understanding regarding big services capabilities, we propose to describe them using a novel meta-model for the quality of big services (QoBS). We also take advantage of a recent technology called knowledge graphs, to represent the big service information (service descriptions, services’ and data sources’ quality levels, management policies) as a heterogeneous information network. Finally, a multi-view representation learning approach is proposed to infer additional knowledge regarding big services capabilities.