Adaptive Prefetching Scheme Using Web Log Mining in Cluster-based Web Systems
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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. 2009 IEEE.
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2009 IEEE International Conference on Web Services
