An Energy-Aware Video Streaming System for Portable Computing Devices

MDM'06 : 7th International Conference on Mobile Data Management , May 9-12, 2006 , Nara, JapanIn this demonstration, we show an energy-aware video streaming system which allows users to play back video for the specified duration within the remaining battery amount. In the system, we execute a proxy server on an intermediate node in the network. It receives the video stream from a content server, transcodes it to the videos with appropriate quality, and forwards it to a PDA or a laptop PC. Here, suitable parameter values of the video (such as picture size, frame rate and bitrate) which enable playback for the specified duration are automatically calculated on the proxy using our battery consumption model. The system also allows users to play back video segments with different qualities based on the importance specified to each video segment

Framework for virtual collaboration emphasized by awareness information and asynchronous interaction

ICME2008 : IEEE International Conference on Multimedia and Expo , Jun 23-26, 2008 , Hannover, GermanyIn this paper, we propose a framework which allows remote users to form conversation groups based on spatial relationship in a shared virtual space. Our proposed framework can transport awareness information of real world by capturing and transferring user’s audio visual information. Our framework also provides functions useful to CSCW, which allow each user to simultaneously join different conversation groups, and communicate with others asynchronously exchanging awareness information. We show a reference implementation architecture to realize the framework in an ordinary computing and networking environment

Distributed market broker architecture for resource aggregation in grid computing environments

CCGrid2005 : IEEE International Symposium on Cluster Computing and the Grid , May 9-12, 2005 , Cardiff, UKIn order to allow every user to extract aggregated computational power from idle PCs in the Internet, we propose a distributed architecture to achieve a market based resource sharing among users. The advantages of our proposed architecture are the following: (i) aggregated resources can be bought by one order; (ii) resource prices are decided based on market principles; and (iii) the load is balanced among multiple server nodes to make the architecture scalable w.r.t. the number of users. Through simulations, we have confirmed that the proposed method can mitigate the load at each server node to a great extent

Congestion Alleviation Scheduling Technique for Car Drivers Based on Prediction of Future Congestion on Roads and Spots

ITSC2007 : IEEE Intelligent Transportation Systems Conference , Sep 30-Oct 3, 2007 , Bellevue, WA, USAIn arranging efficient touring to various areas in urban areas, taking into account potential congestion is needed in order to schedule the order of these visits it is important to on the roads used and at the places to be visited. A number of scheduling methods have been proposed for finding (1) a noncongested route by sharing route information among users, or (2) a schedule to alleviate congestion at specific places based on the latest congestion information. However, these methods do not suffice since they do not deal with, simultaneously, congestion on road and at sites visited. In this paper, we propose a method of finding schedules for thousands of users by predicting, in advance, both types of congestion. Using the predicted results, the method adjusts each user's provisional schedule by changing visiting order of places, and reducing their number in keeping with each user's preferences. We have implemented the proposed method and evaluated it by simulations. The results showed it to achieve higher user satisfaction than existing methods

Task Scheduling Algorithm for Multicore Processor Systems with Turbo Boost and Hyper-Threading

PDPTA'14 : The 2014 International Conference on Parallel and Distributed Processing Techniques and Applications , Jul 21-24, 2014 , Las Vegas, NV, USAIn this paper, we propose a task scheduling algorithm for multiprocessor systems with Turbo Boost and Hyper-Threading technologies. The proposed algorithm minimizes the total computation time taking account of dynamic changes of the processing speed by the two technologies, in addition to the network contention among the processors. We constructed a clock speed model with which the changes of processing speed with Turbo Boost and Hyper-threading can be estimated for various processor usage patterns. We then constructed a new scheduling algorithm that minimizes the total execution time of a task graph considering network contention and the two technologies. We evaluated the proposed algorithm by simulations and experiments with a multi-processor system consisting of 4 PCs. In the experiment, the proposed algorithm produced a schedule that reduces the total execution time by 36% compared to conventional methods which are straightfor-ward extensions of an existing method

A Hardware Implementation Method of Multi-Objective Genetic Algorithms

CEC2006 : IEEE International Conference on Evolutionary Computation , Jul 16-21, 2006 , Vancouver, BC, CanadaMulti-objective genetic algorithms (MOGAs) are approximation techniques to solve multi-objective optimization problems. Since MOGAs search a wide variety of pareto optimal solutions at the same time, MOGAs require large computation power. In order to solve practical sizes of the multi objective optimization problems, it is desirable to design and develop a hardware implementation method for MOGAs with high search efficiency and calculation speed. In this paper, we propose a new method to easily implement MOGAs as high performance hardware circuits. In the proposed method, we adopt simple Minimal Generation Gap (MGG) model as the generation model, because it is easy to be pipelined. In order to preserve diversity of individuals, we need a special selection mechanism such as the niching method which takes large computation time to repeatedly compare superiority among all individuals in the population. In the proposed method, we developed a new selection mechanism which greatly reduces the number of comparisons among individuals, keeping diversity of individuals. Our method also includes a parallel execution architecture based on Island GA which is scalable to the number of concurrent pipelines and effective to keep diversity of individuals. We applied our method to multi-objective Knapsack Problem. As a result, we confirmed that our method has higher search efficiency than existing method