The popularity of multimedia streaming services via wireless networks presents major challenges in the management of network bandwidth. One challenge is to quickly and precisely estimate the available bandwidth for the decision of streaming rates of layered and scalable multimedia services. Previous studies based on wired networks are too burdensome to be applied to multimedia applications in wireless networks. In this paper, a new method, IdleGap, is suggested to estimate the available bandwidth of a wireless LAN based on the information from a low layer in the protocol stack. We use a network simulation tool, NS-2, to evaluate our new method with various ranges of cross-traffic and observation times. Our simulation results show that IdleGap accurately estimates the available bandwidth for all ranges of cross-traffic (100 Kbps ∼ 1 Mbps) with a very short observation time of 10 seconds.

This paper proposes Dynamic Thermal Management (DTM) based on a dynamic voltage and frequency scaling (DVFS

The main memory management has been a critical issue to provide high performance in web cluster systems. To overcome the speed gap between processors and disks, many prefetch schemes have been proposed as memory management in web cluster systems. However, inefficient prefetch schemes can degrade the performance of the web cluster system. Dynamic access patterns due to the web cache mechanism in proxy servers increase mispredictions to waste the I/O bandwidth and available memory. Too aggressive prefetch schemes incur the shortage of available memory and performance degradation. Furthermore, modern web frameworks including persistent HTTP make the problem more challenging by reducing the available memory space with multiple connections from a client and web processes management in a prefork mode. Therefore, we attempt to design an adaptive web prefetch scheme by predicting memory status more accurately and dynamically.First, we design Double Prediction-by-Partial-Match Scheme (DPS) that can be adapted to the modern web framework. Second, we propose Adaptive Rate Controller (ARC) to determine the prefetch rate depending on the memory status dynamically. Finally, we suggest Memory Aware Request Distribution (MARD) that distributes requests based on the available web processes and memory. For evaluating the prefetch gain in a server node, we implement an Apache module in Linux. In addition, we build a simulator for verifying our scheme with cluster environments. Simulation results show 10% performance improvement on average in various workloads.

Widely adopted distributor-based systems forward user requests to a balanced set of waiting servers in complete transparency to the users.The policy employed in forwarding requests from the front-end distributor to the backend servers dominates the overall system performance. The locality-aware request distribution (LARD) scheme improves the system response time by having the requests serviced by the web servers that contain the data in their caches. In this paper, we propose a proactive request distribution (PRORD) that applies an intelligent proactivedistribution at the front-end and complementary prefetching at the back-end server nodes to obtain the data of high relation to the previous requests in their caches. The pr-fetching scheme fetches the web pages in advance into the memory based on a confidence value of the web page, which is predicted by the proactive distribution scheme. Designed to work with the prevailing web technologies, such as HTTP 1.1, our scheme aims to provide reduced response time to the users. Simulations carried out with traces derived from the log files of real web servers witness performance boost of 15-45% compared to the existing distribution policies.

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