In regard to the rapid development of renewable energy sources, more and more photovoltaic (PV) generation systems have been connected to main power networks, and it is critical to enhance their transient performance under short-circuit Continue reading →
Demand Side Management (DSM) through optimization of home energy consumption in the smart grid environment is now one of the well-known research areas. Appliance scheduling has been done through many different algorithms to reduce Continue reading →
Chirp signals can achieve a high range resolution without sacrificing SNR or maximum range, making them a strong candidate for use in radar and sonar applications. Chirp signals are also power efficient and resistant to interference, making them well Continue reading →
Digital Radio Frequency Memories (DRFM) are widely used as modules in digital signal processing. These modules can provide several forms of signal manipulation and storage capabilities. With single event effects caused by environmental radiation the Continue reading →
This thesis presents the development and implementation of intelligent algorithms to increase autonomy of unmanned missions for quadrotor type UAVs. A six-degree-of freedom dynamic model of a quadrotor is developed in Matlab/Simulink in Continue reading →
General purpose receivers of today are designed with a broad bandwidth so that the receiver can accept a wide range of signal frequencies. These receivers usually accept one signal along with an y interference that is included. To increase the signal Continue reading →
A novel dynamic co-simulation methodology of overall wind turbine systems is presented. This methodology combines aerodynamics, mechanism dynamics, control system dynamics, and subsystems dynamics. Aerodynamics and turbine 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