Due to the ever-increasing size of sequence databases it has become clear that faster techniques must be employed to effectively perform biological sequence analysis in a reasonable amount of time. Exploiting the inherent parallelism between sequences is a common strategy. In this paper we enhance both the fine-grained and coursegrained parallelism within the HMMER [2] sequence analysis suite. Our strategies are complementary to one another and, where necessary, can be used as drop-in replacements to the strategies already provided within HMMER. We use conventional processors (Intel Pentium IV Xeon) as well as the freely available MPICH parallel programming environment [1]. Our results show that the MPICH implementation greatly outperforms the PVM HMMER implementation, and our SSE2 implementation also lends greater computational power at no cost to the user.

The required real-time and high-rate transfers for multimedia data severely limit the number of requests that can be serviced concurrently by Video-on-Demand (VOD) servers. Resource sharing techniques can be used to address this problem. We study how VOD servers can support heterogeneous receivers while delivering data in a client-pull fashion using enhanced resource sharing. We propose three hybrid solutions. The first solution simply combines existing resource sharing techniques and deals with clients as two bandwidth classes. The other two solutions, however, classify clients into multiple bandwidth classes and service them accordingly by capturing the proposed ideas of Adaptive Stream Merging or Enhanced Adaptive Stream Merging, respectively. We also discuss how scheduling policies can be adapted to the heterogeneous environment so as to exploit the variations in client bandwidth. We evaluate the effectiveness of the proposed solutions and analyze various scheduling policies through extensive simulation.

The number of media streams that can be supported concurrently is highly constrained by the stringent requirements of real-time playback and high transfer rates. To address this problem, media delivery techniques, such as Batching and Stream Merging, utilize the multicast facility to increase resource sharing. The achieved resource sharing depends greatly on how the waiting requests are scheduled for service. Scheduling has been studied extensively when Batching is applied, but up to our knowledge, it has not been investigated in the context of stream merging techniques, which achieve much better resource sharing. In this study, we analyze scheduling when stream merging is employed and propose a simple, yet highly effective scheduling policy, called Minimum Cost First (MCF). MCF exploits the wide variation in stream lengths by favoring the requests that require the least cost. We present two alternative implementations of MCF: MCF-T and MCF-P. We compare various scheduling policies through extensive simulation and show that MCF achieves significant performance benefits in terms of both the number of requests that can be serviced concurrently and the average waiting time for service.

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The number of video streams that can be serviced concurrently is highly constrained by the required real-time and high-rate transfers of multimedia data. Resource sharing techniques, such as Batching, Patching, and Earliest Reachable Merge Target (ERMT), can be used to address this problem by utilizing the multicast facility, which allows multiple requests to share the same set of server and network resources. They assume, however, that all clients have the same available download bandwidth and buffer space. We study how to efficiently support clients with varying available download bandwidth and buffer space, while delivering data in a client-pull fashion using enhanced resource sharing. In particular, we propose three hybrid solutions to address the variability in the download bandwidth among clients: Simple Hybrid Solution (SHS), Adaptive Hybrid Solution (AHS), and Enhanced Hybrid Solution (EHS). SHS simply combines Batching with either Patching or ERMT, leading to two alternatives: SHS-P and SHS-E, respectively. Batching is used for clients with bandwidth lower than double the video playback rate, and Patching/ERMT is used for the rest. In contrast, AHS and EHS classify clients into multiple bandwidth classes and service them accordingly. AHS employs a new stream type, called adaptive stream, and EHS employs an enhanced adaptive stream type to serve clients with bandwidth capacities ranging between the video playback rate and double that rate. AHS and EHS employ adaptive streams or enhanced adaptive streams in conjunction with Batching and Patching or ERMT, leading to four possible schemes: AHS-P, AHS-E, EHS-P, and EHS-E. Moreover, we consider the variability of the available buffer space among clients. Furthermore, we study how the waiting playback requests for different videos can be scheduled for service in the heterogeneous environment, capturing the variations in both the client bandwidth and buffer space. We evaluate the effectiveness of the proposed solutions and analyze various scheduling policies through extensive simulation.