Power is a big problem in data centers and a significant fraction of this power is consumed by the storage system. Server storage systems use a large number of disks to achieve high performance, which increases their power consumption. In this paper, we propose to significantly reduce the power consumed by the storage system via intra-disk parallelism, wherein disk drives can exploit parallelism in the I/O request stream. Intra-disk parallelism can facilitate replacing a large disk array with a smaller one, using the minimum number of disk drives needed to satisfy the capacity requirements. We show that the design space of intra-disk parallelism is large and present a taxonomy to formulate specific implementations within this space. Using a set of commercial workloads, we perform a limit study to identify the key performance bottlenecks that arise when we replace a storage array that is tuned to provide high performance with a single high-capacity disk drive. These are the bottlenecks that intra-disk parallelism would need to alleviate. We then explore a particular intra-disk parallelism approach, where a disk is equipped with multiple arm assemblies that can be independently controlled, and evaluate three disk drive designs that embody this form of parallelism. We show that it is possible to match, and even surpass, the performance of a storage array for these workloads by using a single disk drive of sufficient capacity that exploits intra-disk parallelism, while significantly reducing the power consumed by the storage system compared to the multi-disk configuration. We evaluate the performance and power consumption of disk arrays composed of intra-disk parallel drives, discuss the engineering issues involved in implementing such drives, and finally provide a preliminary cost-benefit analysis of building and deploying intra-disk parallel drives, using cost data obtained from several companies in the disk drive industry.

Aus dem Isopropyliden‐glycerin (I) entsteht ein Tosylat (II) sowie über (III) und (IV) das stereoisomere Tosylat (V).

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Problem statement: In this study, a new ANFIS-based adaptive filter is proposed to remove the non-linear artifacts from the respiratory signal measured using MEMS based accelerometer sensor. The data recorded from the abdomen movement includes the respiratory signal, electromyogram, 50Hz power line interference and the random electrode noise. In order to avoid convergence into local extremes, the system employs ANFIS method. Approach: The proposed architecture is a combination of adaptive filter in which Least Mean Square and Recursive Least Square algorithms are employed and ANFIS, where ANFIS is recruited whenever the adaptive filter is suspected of reading a local extreme value. Results: The results showed that the normalized LMS performs better when compared to other LMS algorithms with SNR improvement of 4.17 dB and MSE value of 0.062. RLS provides least MSE value or 0.015 but only with highest filter order. Quantitative analysis reveals that ANFIS out performs the normalized LMS and RLS algorithms. Conclusion: The result obtained indicates that ANFIS is a useful Artificial Intelligence technique to cancel the non linear interferences from the respiratory signal with very low mean square value of 0.011