Yong Yan’s research while affiliated with IEEE Computer Society and other places

With the falling price of memory, an increasing number of multimedia servers and proxies are now equipped with a large memory space. Caching media objects in the memory of a proxy helps to reduce the network traffic, the disk I/O bandwidth requirement, and the data delivery latency. The running buffer approach and its alternatives are representative techniques to caching streaming data in the memory. There are two limits in the existing techniques. First, although multiple running buffers for the same media object co-exist in a given processing period, data sharing among multiple buffers is not considered. Second, user access patterns are not insightfully considered in the buffer management. In this paper, we propose two techniques based on shared running buffers in the proxy to address these limits. Considering user access patterns and characteristics of the requested media objects, our techniques adaptively allocate memory buffers to fully utilize the currently buffered data of streaming sessions, with the aim to reduce both the server load and the network traffic. Experimentally comparing with several existing techniques, we show that the proposed techniques achieve significant performance improvement by effectively using the shared running buffers.

With the falling price of the memory, an increasing number of multimedia servers and proxies are now equipped with a large DRAM memory space. Caching media objects in the memory of a proxy helps to reduce network traffic, disk I/O bandwidth requirement, and data delivery latency. The running buffer approach and its alternatives are representative techniques to cache streaming data in the memory. However, there are two limits in the existing techniques. First, although multiple running buffers for the same media object co-exist in a given processing period, data sharing among the multiple buffers is not considered. Second, user access patterns are not insightfully considered in the buffer management. In this paper, we propose two techniques based on shared running buffers (SRB) in the proxy to address these limits. Considering user access patterns and characteristics of the requested media objects, our techniques adoptively allocate memory buffers to fully utilize the currently buffered data of streaming sessions, with the aim to reduce both the server load and the network traffic. Experimentally comparing with several existing techniques, we show that the proposed techniques have achieved significant performance improvement by effectively using the shared running buffers.

We are developing mmGrid (Multimedia Grid) as an extensible middleware architecture supporting multimedia applications in a grid computing environment. Our vision is to provide support for interactive applications from the following domains: graphics, visualization, streaming media and tele-immersion. The initial deployment will be within an enterprise as a mechanism for provisioning computing resources. However the scheduling system of mmGrid will be flexible, and will allow interactive and batch jobs to use the grid-computing paradigm. This paper presents our argument for using remote display technology in this environment. We also report on the use cases we support at this point, the system architecture for mmGrid and our research directions.

shared running buffer, proxy caching, patching, streaming media delivery, VOD With the falling price of memory, an increasing number of multimedia servers and proxies are now equipped with a large memory space. Caching media objects in memory of a proxy helps to reduce the network traffic, the disk I/O bandwidth requirement, and the data delivery latency. The running buffer approach and its alternatives are representative techniques to caching streaming data in the memory. There are two limits in the existing techniques. First, although multiple running buffers for the same media object co-exist in a given processing period, data sharing among multiple buffers is not considered. Second, user access patterns are not insightfully considered in the buffer management. In this paper, we propose two techniques based on shared running buffers (SRB) in the proxy to address these limits. Considering user access patterns and characteristics of the requested media objects, our techniques adaptively allocate memory buffers to fully utilize the currently buffered data of streaming sessions, with the aim to reduce both the server load and the network traffic. Experimentally comparing with several existing techniques, we show that the proposed techniques achieve significant performance improvement by effectively using the shared running buffers.

middleware architecture supporting multimedia applications in a grid computing environment. Our vision is to provide support for interactive applications from the following domains: graphics, visualization, streaming media and tele-immersion. The initial deployment will be within an enterprise as a mechanism for provisioning computing resources. However the scheduling system of mmGrid will be flexible, and will allow interactive and batch jobs to use the grid-computing paradigm. This paper presents our argument for using remote display technology in this environment. We also report on the use cases we support at this point, the system architecture for mmGrid and our research directions.

With the falling price of the memory, an increasing number of mul- timedia servers and proxies are now equipped with a large mem- ory space. Caching media objects in the memory of a proxy helps to reduce the network traffic, the disk I/O bandwidth requirement, and the data delivery latency. The running buffer approach and its alternatives are representative techniques to cache streaming data in the memory. There are two limits in the existing techniques. First, although multiple running buffers for the same media ob- ject co-exist in a given processing period, data sharing among the multiple buffers is not considered. Second, user access patterns are not insightfully considered in the buffer management. In this study, we propose two techniques based on shared running buffers (SRB) in the proxy to address these limits. Considering user ac- cess patterns and characteristics of the requested media objects, our techniques adaptively allocate memory buffers to fully utilize the currently buffered data of streaming sessions to serve existing re- quests concurrently, with the aim to reduce both the server load and the network traffic. Experimentally comparing with several exist- ing techniques, we have shown that the proposed techniques have achieved significant performance improvement by effectively using the sharing running buffers.

this paper, we present an experimental metric using network latency for measuring and evaluating parallel program and architecture sealability. We first give the definitions of latency and sealability. Furthermore we show the analytical relationships among the latency metric, the isoefficiency function, and the isospeed metric. Finally we give a measurement method for using the latency metric. We include experimental measurements on the KSR-I to show the effectiveness of the latency metric in predicting and evaluating the sealability

Exploiting cache locality of parallel programs at runtime is a complementary approach to a compiler optimization. This is particularly important for those applications with dynamic memory access patterns. We propose a memory-layout oriented technique to exploit cache locality of parallel loops at runtime on Symmetric Multiprocessor (SMP) systems. Guided by application-dependent and targeted architecture-dependent hints, our system, called Cacheminer, reorganizes and partitions a parallel loop using the memory-access space of its execution. Through effective runtime transformations, our system maximizes the data reuse in each partitioned data region assigned in a cache, and minimizes the data sharing among the partitioned data regions assigned to all caches. The executions of tasks in the partitions are scheduled in an adaptive and locality-presented way to minimize the execution time of programs by trading off load balance and locality. We have implemented the Cacheminer runtime library on two commercial SMP servers and an SimCS simulated SMP. Our simulation and measurement results show that our runtime approach can achieve comparable performance with the compiler optimizations for programs with regular computation and memory-access patterns, whose load balance and cache locality can be well optimized by the tiling and other program transformations. However, our experimental results show that our approach is able to significantly improve the memory performance for the applications with irregular computation and dynamic memory access patterns. These types of programs are usually hard to optimize by static compiler optimizations

Exploiting cache locality of parallel programs at runtime is a complementary approach to a compiler optimization. This is particularly important for those applications with dynamic memory access patterns. We propose a memory-layout oriented technique to exploit cache locality of parallel loops at runtime on Symmetric Multiprocessor (SMP) systems. Guided by application-dependent and targeted architecture-dependent hints, our system, called Cacheminer, reorganizes and partitions a parallel loop using the memory-access space of its execution. Through effective runtime transformations, our system maximizes the data reuse in each partitioned data region assigned in a cache, and minimizes the data sharing among the partitioned data regions assigned to all caches. The executions of tasks in the partitions are scheduled in an adaptive and locality-preserved way to minimize the execution time of programs by trading off load balance and locality. We have implemented the Cacheminer runtime library on two commercial SMP servers and an SimOS simulated SMP. Our simulation and measurement results show that our runtime approach can achieve comparable performance with the compiler optimizations for programs with regular computation and memory-access patterns, whose load balance and cache locality can be well optimized by the tiling and other program transformations. However, our experimental results show that our approach is able to significantly improve the memory performance for the applications with irregular computation and dynamic memory access patterns. These types of programs are usually hard to optimize by static compiler optimizations.