This paper presents a technique for admission control of a set of horizontally scalable services, and their optimal placement, into a federated Cloud environment. In the proposed model, the focus is on hosting elastic services whose resource requirements may dynamically grow and shrink, depending on the dynamically varying number of users and patterns of requests. The request may also be partially accommodated in federated external providers, if needed or more convenient. In finding the optimum allocation, the presented mechanism uses a probabilistic optimization model, which takes into account eco-efficiency and cost, as well as affinity and anti-affinity rules possibly in place for the components that comprise the services. In addition to modelling and solving the exact optimization problem, we also introduce a heuristic solver that exhibits a reduced complexity and solving time. We show evaluation results for the proposed technique under various scenarios.

The paper shows how different concepts and technologies, such as high quality multimedia content production and delivery, social media interaction, and methodologies for monitoring and analysing social network activity, can be practically combined with traditional virtual reality installations into an innovative experimental system that enhances the experience of the visitors in cultural educational centers. Apart from offering a modern, immersive, richer experience to the visitors, the proposed system involves significant benefits for the organizers as well, supplying them with valuable feedback extracted from the monitoring and analysis mechanisms. The experimental system has been successfully deployed and used in the Foundation of the Hellenic World cultural center.

In modern utility computing infrastructures, like grids and clouds, one of the significant actions of a service provider is to predict the resources needed by the services included in its platform in an automated fashion for service provisioning optimization. Furthermore, a variety of software toolkits exist that implement an extended set of algorithms applicable to workload forecasting. However, their automated use as services in the distributed computing paradigm includes a number of design and implementation challenges. In this paper, a decoupled framework is presented, for taking advantage of software like GNU Octave in the process of creating and using prediction models during the service life cycle of a SOI. A performance analysis of the framework is also conducted. In this context, a methodology for creating parametric or gearbox services with multiple modes of operations based on the execution conditions is portrayed and is applied to transform the aforementioned service framework to optimize service performance. A new estimation algorithm is introduced, that creates performance rules of applications as black boxes, through the creation and usage of genetically optimized artificial neural networks. Through this combination, the critical parameters of the networks are decided through an evolutionary iterative process.

This paper presents an admission control test for deciding whether or not it is worth to admit a set of services into a Cloud, and in case of acceptance, obtain the optimum allocation for each of the components that comprise the services. In the proposed model, the focus is on hosting elastic services the resource requirements of which may dynamically grow and shrink, depending on the dynamically varying number of users and patterns of requests. In finding the optimum allocation, the presented admission control test uses an optimization model, which incorporates business rules in terms of trust, eco-efficiency and cost, and also takes into account affinity rules the components that comprise the service may have. The problem is modeled on the General Algebraic Modeling System (GAMS) and solved under realistic provider's settings that demonstrate the efficiency of the proposed method.

The emergence of new environments such as Cloud computing highlighted new challenges in traditional fields like performance estimation. Most of the current cloud environments follow the Software, Platform, Infrastructure service model in order to map discrete roles / providers according to the offering in each “layer”. However, the limited amount of information passed from one layer to the other has raised the level of difficulty in translating user-understandable application terms from the Software layer to resource specific attributes, which can be used to manage resources in the Platform and Infrastructure layers. In this paper, a generic black box approach, based on Artificial Neural Networks is used in order to perform the aforementioned translation. The efficiency of the approach is presented and validated through different application scenarios (namely FFMPEG encoding and real-time interactive e-Learning) that highlight its applicability even in cases where accurate performance estimation is critical, as in cloud environments aiming to facilitate real-time and interactivity.

The advancements in distributed computing have driven the emergence of service-based infrastructures that allow for on-demand provision of ICT assets. Taking into consideration the complexity of distributed environments, significant challenges exist in providing and managing the offered on-demand resources with the required level of Quality of Service (QoS), especially for real-time interactive multimedia applications. Common to these applications is the service-oriented approach being followed, thus applications consist of application service components that interact in order to provide the corresponding application functionality. In this paper we present an architectural design of a complete Workflow Management System (WfMS) for enacting application service components that have been deployed in a Virtualized Environment. The WfMS aims at synchronizing the application components, monitoring their execution and handling faults while adhering to the QoS requirements of the application.

This paper deals with the problem of scheduling workflow applications with quality of service (QoS) constraints, comprising real-time and interactivity constraints, over a service-oriented grid network. A novel approach is proposed, in which high-level advance reservations, supporting flexible start and end time, are combined with low-level soft real-time scheduling, allowing for the concurrent deployment of multiple services on the same host while fulfilling their QoS requirements. By undertaking a stochastic approach, in which a-priori knowledge is leveraged about the probability of activation of the application workflows within the reserved time-frame, the proposed methodology allows for the achievement of various trade-offs between the need for respecting QoS constraints (user perspective) and the need for having good resource saturation levels (service provider perspective).