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Cloud Computing (CC) marks a new step towards IT infrastructure dematerialization. Cloud provides IT resources, software and hardware, remotely accessible, as a service. The adoption of this model raises a number of challenges, particularly with regard to Quality of the provided services. To cope with a highly dynamic environment such as the CC, it is essential to determine in real time Quality of Service (QoS) to meet consumer's SLA (Service Level Agreements) specifications. In this context, agreements (contract) service level form an appropriate solution to specify these QoS guarantees. It specifies one or more service level objectives (SLO), to guarantee that the delivered QoS satisfies the consumer expectations. Monitoring these QoS agreements for the management of the relationships, between, cloud providers and their services customers is an area that attracts the attention of many researchers and industrialists in the cloud. In our work we introduce the concept of monitoring and respect of the QoS, then we present a third party service provider that ensures the respect of the Quality of Service in real-time to guarantee the performance and the reliability of the Cloud.

Abstract. The new era of the Web is known as the semantic Web or the Web of data. The semantic Web depends on ontologies that are seen as one of its pillars. The bigger these ontologies, the greater their exploitation. However, when these ontologies become too big other problems may appear, such as the complexity to charge big files in memory, the time it needs to download such files and especially the time it needs to make reasoning on them. We discuss in this paper approaches for segmenting such big Web ontologies as well as its usefulness. The segmentation method extracts from an existing ontology a segment that represents a layer or a generation in the existing ontology; i.e. a horizontally extraction. The extracted segment should be itself an ontology.Keywords: Ontology; Segmentation; OWL; Semantic Web. IntroductionThe Web ontologies present several interests for the Web, such as annotating data, distinguishing between homonyms and polysemy, generalizing or specializing concepts, driving intelligent user interfaces and even inferring entirely new (implicit) information [3] [4]. Ontologies are created by ontology engineers with the help of domain experts [5]. Let us take as an example an ontology representing a population. This ontology should have information about the citizens, their dates of birth, relationships, hobbies, addresses, competences, jobs, etc. It seems to be a great ontology allowing us to get new information about one person's tendencies, how these tendencies may be affected by his relationships; also, the companies can use it to target their advertisements. But, for one reason or another, we may not be concerned by the population under a certain age, or may be interested only in a particular city's population. For such purposes, we assume that segments of such big ontologies that contain only the desired information will respond better to the users' expectations.Several studies are focused on the extraction, classification [6] [7] and segmentation [3] [8] of data in Web ontology; these data can be represented in the ontology web. For example, N. Gherabi et al [9] present a new approach to mapping data stored in relational databases in the semantic Web, it uses simple mappings based on certain specifications of the database schema and explain how relational databases can be used to define a mapping mechanism between the relational database and the OWL ontology. In another work [10], the authors have developed a method to convert UML schemas to Web Ontology.J. Seidenberg and A. Rector have presented in [3] a method for extracting small segments from large ontologies using the GALEN as an example. The segmentation algorithm they have presented was based on the classes hierarchy, which leads to segmenting the ontology by extracting a specific class hierarchy. Such method responds to segmenting ontologies like the GALEN ontology where one could be interested in a concept like HEART and all its super classes and subclasses.

LTE is the most recent mobile technology deployed everywhere in the whole wide world. It provides high Downlink and Uplink throughput for all service classes especially for voice class. All the services offered by LTE are based on IMS architecture. The goal of this paper is to present the developed admission control algorithm for VoLTE based on the Load control information and the UE capability in term of the supported voice codec. This developed algorithm has high performance results compared to the existing one that uses only G.711. General TermsPerforming VoLTE quality of services is the goal of our paper.

This article describes how currently, service level agreements (SLAs) assurance forms one of the major challenges for cloud computing (CC) in order to guarantee quality of service (QoS) in real-time and control SLA violations. However, due to the highly dynamic nature of this open environment, it is important to have a binding agreement between all the service parties for ensuring trust while fulfilling the expected QoS. To properly operate and manage such complex situations, an effective and efficient monitoring is crucial. The participation of a trusted third party (TTP) is necessary in order to resolve conflicts between involved parties. This article proposes an autonomic SLA monitoring framework managed by TTP composed of two modules: the first one SLA establishment module, which aims at providing support for automated SLA generation and management. The second one, a service monitoring module to dynamically monitor QoS metrics by detecting SLA violations at runtime to verify compliances for the respective SLAs, and to propose a mechanism for an adaptive remedy rectification, as a contribution at the third maturity level of the autonomic computing paradigm as defined by IBM. The framework is validated with scenarios on response time and availability, the results obtained are promising. They confirm that this framework manages SLAs in an efficient way as it detects all violations to be communicated to concerned parties, and identifies particular penalty clauses that can be used to modify the reputation of a provider over time. The TTP framework equipped with such reputation module can provide real-time assessment for consumers informed decision making to continue using a service or to migrate to another service provider in the case of service degradation. This creates a fair competitiveness between providers and hence improves service performance and the reliability in the cloud.

Establishing and monitoring SLA violations in real-time has become a critical issue for Cloud Computing. In this paper the authors investigate this issue and propose a model to express the SLA contract requirements using Model Driven Engineering (MDE), as a mean for establishing service level agreements between a cloud provider and cloud customer in the context of a particular service provision. The participation of a Trusted Third Party (TTP) may be necessary in order to resolve conflicts between prospective signatories, likewise to monitor SLA violations in real-time in the goal to ensure online monitoring cloud services and provide better than best-effort behavior for clouds. The main focus of this work is firstly to use MDE technology for the creation of the SLA contract and then to integrate TTP that should be able to apply an advanced penalty model that guarantees the performance and the reliability of the Cloud.