Scalable network servers are increasingly identified as a critical component in the exponential growth of the Internet. We focus on media servers for variable bit rate streams and study the scalability of alternative disk striping policies, previously known and new. In contrast to results of previous studies, we show that the highest sustained number of streams that can be supported increases almost linearly with the number of disks. We believe that important role for our conclusion play the performance evaluation method that we use and a new disk space allocation technique that we introduce. Also, with reasonable technological projections, our arguments remain valid into the foreseeable future.
We investigate the problem of smoothing multiplexed network traffic, when either a streaming server transmits data to multiple clients, or a server accesses data from multiple storage devices or other servers. We introduce efficient algorithms for lexicographically optimally smoothing the aggregate bandwidth requirements over a shared network link. In the data transmission problem, we consider the case in which the clients have different buffer capacities but no bandwidth constraints, or no buffer capacities but different bandwidth constraints. For the data access problem, we handle the general case of a shared buffer capacity and individual network bandwidth constraints. Previous approaches in the literature for the data access problem handled either the case of only a single stream or did not compute the lexicographically optimal schedule.Lexicographically optimal smoothing (lexopt smoothing) has several advantages. By provably minimizing the variance of the required aggregate bandwidth, maximum resource requirements within the network become more predictable, and useful resource utilization increases. ing a network link by multiple users can be improved, and new requests from future clients are more likely to be successfully admitted without the need for frequently rescheduling previously accepted traffic. Efficient resource management at the network edges can better meet quality of service requirements without restricting the scalability of the system.
dent on the amount of buffer space available at the client.We introduce an algorithm that uses bufJer space available at the server for smoothing disk transfers of variable bit-rate streams. Previous smoothing techniques prefetched stream data into the client buffer space, instead. However, emergence of personal computing devices with widely differenthardware configurations means that we should not always assume abundance of resources at the client side. The new algorithm is shown to have optimal smoothing effect under the specified constraints. We incorporate it into a prototype server, and demonstrate significant increase in the number of streams concurrently supported at different system scales. We also extend our algorithm for striping variable bit-rate streams on heterogeneous disks. High bandwidth utilization is achieved across all the different disks, which leads to server throughput improved by severalfactors at high loads.In this paper, our goal is to maximize the average number of users supported concurrently in video server systems, by applying smoothing techniques and combining them appropriately with disk striping and admission control policies. Thus, we introduce a stream smoothing algorithm that prefetches data into server buffers, which has several important advantages:ability to provide the benefits of smoothing even to clients with minimal memory resources (such as inexpensive mass-produced specialized devices), ability to limit the requirements for disk bandwidth, which is estimated to increase at rates an order of magnitude slower than network link bandwidth [9],
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.