Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: State-of-the-art networked storage servers are equipped with increasingly porful computing capability and large DRAM memory as storage caches. Hover, their contribution to the performance improvement of networked storage system has become increasingly limited. This is because the client-side memory sizes are also increasing, which reduces capacity misses in the client buffer caches as ll as access locality in the storage servers, thus akening the caching effectiveness of server storage caches. Proactive caching in storage servers is highly desirable to reduce cold misses in clients.  propose an effective way to improve the utilization of storage server resources through prefetching in storage servers for clients. In particular, our design ll utilizes two unique strengths of networked storage servers which are not leveraged in existing storage server prefetching schemes. First, porful storage servers have idle CPU cycles, under-utilized disk bandwidth, and abundant memory space, providing many opportunities for aggressive disk data prefetching. Second, the servers have the knowledge about high-latency operations in storage devices, such as disk head positioning, which enables efficient disk data prefetching based on an accurate cost-benefit analysis of prefetch operations.  present STEP - a Sequentiality and Thrashing dEtec- tion based Prefetching scheme, and its implementation with Linux Kernel 2.6.16. Our performance evaluation by replaying storage performance council (SPC) 's OLTP traces shows that server performance improvements are up to 94% with an average of 25%. Improvements with frequently used Unix applications are up to 53% with an average of 12%. Our experiments also show that STEP has little effect on workloads with random access patterns, such as SPC b-search traces.
['Shuang Liang', 'Song Jiang', 'Xiaodong Zhang']
I am writing a research paper on a idea STEP: Sequentiality and Thrashing Detection Based Prefetching to Improve Performance of Networked Storage Servers
[{"from": "human", "value": "I am writing a research paper on a idea STEP: Sequentiality and Thrashing Detection Based Prefetching to Improve Performance of Networked Storage Servers"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: State-of-the-art networked storage servers are equipped with increasingly porful computing capability and large DRAM memory as storage caches. Hover, their contribution to the performance improvement of networked storage system has become increasingly limited. This is because the client-side memory sizes are also increasing, which reduces capacity misses in the client buffer caches as ll as access locality in the storage servers, thus akening the caching effectiveness of server storage caches. Proactive caching in storage servers is highly desirable to reduce cold misses in clients.  propose an effective way to improve the utilization of storage server resources through prefetching in storage servers for clients. In particular, our design ll utilizes two unique strengths of networked storage servers which are not leveraged in existing storage server prefetching schemes. First, porful storage servers have idle CPU cycles, under-utilized disk bandwidth, and abundant memory space, providing many opportunities for aggressive disk data prefetching. Second, the servers have the knowledge about high-latency operations in storage devices, such as disk head positioning, which enables efficient disk data prefetching based on an accurate cost-benefit analysis of prefetch operations.  present STEP - a Sequentiality and Thrashing dEtec- tion based Prefetching scheme, and its implementation with Linux Kernel 2.6.16. Our performance evaluation by replaying storage performance council (SPC) 's OLTP traces shows that server performance improvements are up to 94% with an average of 25%. Improvements with frequently used Unix applications are up to 53% with an average of 12%. Our experiments also show that STEP has little effect on workloads with random access patterns, such as SPC b-search traces."}]