-Cloud computing offers the potential to dramatically reduce the cost of software services through the commoditization of information technology assets and ondemand usage patterns. However, the complexity of determining resource provision policies for applications in such complex environments introduces significant inefficiencies and has driven the emergence of a new class of infrastructure called Platform-as-a-Service (PaaS). In this paper, we present a novel PaaS architecture being developed in the EU IST IRMOS project targeting real-time Quality of Service (QoS) guarantees for online interactive multimedia applications. The architecture considers the full service lifecycle including service engineering, service level agreement design, provisioning and monitoring. QoS parameters at both application and infrastructure levels are given specific attention as the basis for provisioning policies in the context of temporal constraints. The generic applicability of the architecture is being verified and validated through implemented scenarios from three important application sectors (film post-production, virtual augmented reality for engineering design, collaborative e-Learning in virtual worlds).
Abstract-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.
Abstract-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 eLearning) that highlight its applicability even in cases where accurate performance estimation is critical, as in cloud environments aiming to facilitate real-time and interactivity.
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