Cloud applications based on the "Functions as a Service" (FaaS) paradigm have become very popular. Yet, due to their stateless nature, they must frequently interact with an external data store, which limits their performance. To mitigate this issue, we introduce OFC, a transparent, vertically and horizontally elastic in-memory caching system for FaaS platforms, distributed over the worker nodes. OFC provides these benefits cost-effectively by exploiting two common sources of resource waste: (i) most cloud tenants overprovision the memory resources reserved for their functions because their footprint is non-trivially input-dependent and (ii) FaaS providers keep function sandboxes alive for several minutes to avoid cold starts. Using machine learning models adjusted for typical function input data categories (e.g., multimedia formats), OFC estimates the actual memory resources required by each function invocation and hoards the remaining capacity to feed the cache. We build our OFC prototype based on enhancements to the OpenWhisk FaaS platform, the Swift persistent object store, and the RAM-Cloud in-memory store. Using a diverse set of workloads, we show that OFC improves by up to 82 % and 60 % respectively the execution time of single-stage and pipelined functions.

Ontology is becoming the key knowledge capture structure in many domains. It plays a very important role in the area of semantic web and is widely used in multiple fields including Intelligent Tutoring Systems (ITS) and e-Learning. Ontologies are intensively used in domain knowledge modeling in specific areas which can evolve. However, current tools used to implement ontologies fail to provide functions to adequately ensure their evolution. To deal with this issue, we have developed an ontology evolution management system named «MS-ONTO», founded on a formal description of evolution operators. MS-ONTO allows the preservation of both the internal and external integrity constraints during the ontology evolution: the external integrity meaning the preservation of its usage while internal integrity means its conformity to the constraints (implicit or explicit) related to the ontology model itself. MS-ONTO should be integrated as a plug-in in existing ontology editors such as NeOn Toolkit and Protege.

The present article proposes a module called xCCM (eXtended Content Composition Module) that permits the use of the existing contents in the e-Learning platform to produce new contents adapted to the learner's profile. Our field of application is xMoodle 2.0 (a Moodle extention) [1] that integrates a module permitting us to create granules of contents within. xCCM is based on Web 2.0 technology, it relies on the principle of nodes to build contents called adapted contents and it is integrated to xMoodle 2.0 platform as a key tool for the authors.

Shortage of medical personnel and the existence of inexperienced ones, are frequently the causes of false diagnosis, and call for a reflection on the methods of training of the later. One of the solutions might be the application of new technologies in the training of medical students, in order to counter the shortage of experts as well as training conditions which are usually inadequate (no patients available for practice). The aim of this paper is to propose a virtual patient, on which an expert can formulate pathology, and hand this patient to a learner for exercises on medical diagnostics.