The emerging Cloud Gaming Service provides a highly accessible video gaming experience. With Cloud Gaming, potential players without enough local resource can access high-quality gaming using low-spec devices. With advancing technology, we consider that if the processing power at low-spec devices can be well harvested, the quality delivered on Cloud Gaming can be further improved. Therefore, we propose a Hybrid-Streaming System that aimed at improving the graphic quality delivered by Cloud Gaming. By utilizing the available rendering power from both the Cloud Server and client PC, the system distributes rendering operations to both sides to achieve the desired improvement. Quantitative results show the proposed method improves graphics quality, as well as reducing the server’s workload while attaining acceptable network bandwidth consumption levels.

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To empower scientists who are engaged in nationwide or global-scale collaborative projects for scientific discovery, a large amount of scientific data needs to be visualized and then shared among the scientists. Tiled Display Wall (TDW) systems has been widely accepted and used for visualization of large-scale scientific data. Scalable Adaptive Graphics Environment (SAGE) has received attention from scientists as a middleware that organizes multiple display monitors into a network-aware large display monitor. Using a SAGE TDW, scientists can display multiple visualized contents on a single display monitor, each of which can be located at geographically distant site managed by other organizations. However, SAGE does not have a mechanism for managing multiple visualized data streams heading for a single TDW. In a conventional network, data flows for a same destination tend to share a same link, resulting in drop of packets and therefore poor visual quality. Moreover, because of the flexible nature of SAGE, rate of each visual data

Network performance in high-performance computing environments such as supercomputers and Grid systems takes a role of great importance in deciding the overall performance of computation. However, most Job Management Systems (JMSs) available today, which are responsible for managing multiple computing resources for distribution and balancing of a computational workload, do not consider network awareness for resource management and allocation. In this paper, the authors briefly overview our proposed and prototyped network-aware JMS that can allocate an appropriate set of computing and network resources to a job request. Also, we evaluate the usefulness and effectiveness of our proposal. Experiments conducted with the prototype implementation imply that our proposed networkaware JMS could reduce job execution time by 23.4 percent. I. INTRODUCTIONIn the area of scientific computation using parallel and distributed computing techniques, network performance affects the total execution time of computation. Recently, the dominant trend in computer architecture for high-performance computing has been cluster systems which are composed of multiple computers on a high-speed network. More than 80 percent of high-performance computers are cluster systems [1]. To gain high performance on a cluster system, the communication time must be inevitably reduced. Particularly, in large-scale and distributed computing environments such as campus Grid system, communication overhead becomes the prime limiting factor.Most cluster systems available today have deployed JMSs such as NQS [2], PBS [3] and the Open Grid Scheduler/Grid Engine (OGS/GE) [4]. The JMSs are generally used for computational workload distribution and balancing purposes. The user can submit a job to JMS without being aware which computing hosts of a cluster system are available. However, such traditional JMSs are designed to allocate only computing resources such as CPU and memory to each job submitted to them, without taking network performance into account. A major reason for this is explained from the assumption that network resources of a cluster system always have enough capacity to accommodate multiple job execution simultaneously.