In this paper I present the domain specific language HaGPipe for graphics programming in Haskell. HaGPipe has a clean, purely functional and strongly Continue reading
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Autonomous robots are robotic platforms with a high degree of autonomy, programmed to perform various behaviors or tasks. They can either be semi-autonomous, only operable within the strict confines of Continue reading
The set of web pages not reachable using conventional web search engines is usually called the hidden or deep web. One client-side hurdle for crawling the hidden web is Flash files. This project presents a tool for Continue reading
Split testing is a popular practise to compare and evaluate design elements of web-sites. In this report, it is explored how split testing can be employed to test features of motion-based Continue reading
This thesis presents and evaluates how to select the data to be logged in an embedded real-time system so as to be able to give confidence that it is possible to Continue reading
Data analytics plays a significant role in gaining insight of big data that can benefit in decision making and problem solving for various application domains such as science, engineering, and commerce. Cloud computing is a suitable platform for Big Data Analytic Applications (BDAAs) that can greatly reduce application cost by elastically provisioning resources based on user requirements and in a pay as you go model.
BDAAs are typically catered for specific domains and are usually expensive. Moreover, it is difficult to provision resources for BDAAs with fluctuating resource requirements and reduce the resource cost. As a result, BDAAs are mostly used by large enterprises. Therefore, it is necessary to have a general Analytics as a Service (AaaS) platform that can provision BDAAs to users in various domains as consumable services in an easy to use way and at lower price.
To support the AaaS platform, our research focuses on efficiently scheduling Cloud resources for BDAAs to satisfy Quality of Service (QoS) requirements of budget and deadline for data analytic requests and maximize profit for the AaaS platform. We propose an admission control and resource scheduling algorithm, which not only satisfies QoS requirements of requests as guaranteed in Service Level Agreements (SLAs), but also increases the profit for AaaS providers by offering a cost-effective resource scheduling solution.
We propose the architecture and models for the AaaS platform and conduct experiments to evaluate the proposed algorithm. Results show the efficiency of the algorithm in SLA guarantee, profit enhancement, and cost saving.
Source: University of Melbourne
Authors: Mehdi Sookhak | Abdullah Gani | Muhammad Khurram Khan | Rajkumar Buyya
Social networking offers teachers and learners exciting opportunities to communicate. Web 2.0 and its synchronous communications platforms Continue reading
The process of driving a car involves a cognitive load that varies over time. Additional load comes from secondary factors not directly associated Continue reading
Automation of model building enables new predictive models to be generated in a faster, easier and more straightforward way once new data is available to predict on. Automation can also reduce the demand for tedious bookkeeping that Continue reading
Because of its flexibility and convenience, WLAN has been an essential technology for enterprise Network. It becomes a heated issue to improve the Continue reading
Quality Monitor is application, which automatically analyzes software projects for quality and makes quality assessment reports. This thesis project aims to instantiate Quality Monitor for a large real-world .Net project and to Continue reading