QoS within Business Grid Quality of Service (BGQoS)

Differences in domain QoS requirements have been an obstacle to utilising Grid Computing for main stream applications. While the resource could potentially provide potentially vital services as well as providing significant computing and storage capabilities, the lack of high level QoS specification capabilities has proven to be a hindrance. Business Grid Quality of Service (BGQoS) is a QoS model for business-oriented applications on Grid computing systems. BGQoS defines QoS at a high level facilitating an easier request model for the Grid Resource Consumer (GRC) and eliminates confusion for the Grid Resource Provider in supplying the appropriate resources to meet the GRC requirements. It offers high level QoS specification within multi-domain environments in a flexible manner. Employing component separation and dynamic QoS calculation, it provides the necessary tools and execution environment for a scalable set of requirements tailoring to specific domain demands and requirements. Moreover, through reallocation, the model provides the insurance that all QoS requirements are met throughout the execution period, including migrating tasks to different resources if necessary. This process is not random and adheres to a set of conditions which ensures that task execution and resource allocation happen when and in accordance with execution requirements. This paper focuses on BGQoS’ flexibility and QoS capability. More specifically, the concentration is on core operations within BGQoS and the methods used in order to deliver a sustained level of QoS which meets the GRC’s requirements while being versatile and flexible such that it can be tailored to specific domains. This paper also presents an experimental evaluation of BGQoS. The evaluation investigates the behaviour and performance of the separate operations and components within BGQoS, and moreover, it presents an investigation and comparison between the different operations and their effect on the full model

Financial Modeling and Prediction as a Service

© 2017 Springer Science+Business Media DordrechtThis paper describes our proposal for Quality of Service (QoS) for Financial Modeling and Prediction as a Service (FMPaaS), since a majority of papers does not focus on SaaS level. We focus on two factors for delivering successful QoS, which are performance and accuracy for FMPaaS. The design process, theories and models behind the FMPaaS service have been explained. To support our FMPaaS service, two APIs have been developed to improve on performance and accuracy. Two major experiments have been illustrated and results show that each API processing can be completed in 2.12 seconds and 100,000 simulations can be completed in an acceptable period of time. Accuracy tests have been performed while using Facebook as an example. Three points of comparisons between actual and predicted prices have been undertaken. Results support accuracy since results are between 93.72 % and 99.63 % for Facebook. Three case studies have been used and results can support the accuracy and validity of the high level of accuracy offered by FMPaaS