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This paper presents a methodology that combines latent factor models with graph-based models. The proposed recommendation system identifies a recommended item as a node of a graph. More specifically, the topology of the graph and the paths between the nodes are considered as critical features regarding the associations between them. Furthermore, in the current approach, these structural features are considered as feedback. These structural features are extracted from a pool of several application graphs which are afterwards generalized into a unified matrix of proximities. The main reason for the use of this structural feedback is to generate recommendations and discover unobserved relations using matrix factorization techniques. The approach is tested on a data set that consists of cloud-native microservices graphs.
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The new-coming 5G network is considered to be one of the most significant innovations today. This is due to the opportunities that is going to provide to the vertical industries. 5G infrastructures will introduce a new way for low-delay, reliable deployment of services. In fact, such infrastructures can be used for the placement of application services in the form of application graphs. An application graph consists of several application components (i.e. micro-services) that may be hosted in the same infrastructure or in different ones. Conflicting requirements that arise when deploying in such infrastructures are now handled through network slicing, which regards a way for partitioning conventional network and computing resources into virtual elements. In this paper, we define a universal application metamodel of a 5G compatible application in order to guarantee the annotation of each application descriptor with its proper requirements for their fulfillment at the instantiation time. In terms of application architecture, we consider each application graph as a service mesh topology in order to adopt this novel service architecture as a dominant methodology that is well fitting in the promising 5G capabilities.
The evolution of communication networks, bringing the fifth generation (5G) of mobile communications in the foreground, gives the vertical industries opportunities that were not possible until now. Flexible network and computing infrastructure management can be achieved, hence bringing more freedom to the service providers, to maximize the performance of their computing resources. Still, challenges regarding the orchestration of these resources may arise. For this reason, an engine that can recognize possible factors that might affect the use of these resources and come up with solutions when needed in real-time, is required. In this paper, we present a novel Complex Event Processing engine that is enriched with Machine Learning capabilities in order to be fully adaptive to its environment, as a solution for monitoring application components deployed in 5G infrastructures. The proposed engine utilizes Incremental DBSCAN to identify the normal behavior of the deployed services and adjust the rules accordingly.
