Chris Moultrie Dr. Prasad CSc 8350 [17] S. Gurumurthi, A. Sivasubramaniam, M. Kandemir, and H. Franke, "DRPM: dynamic speed control for power

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1 [1] This paper outlines different strategies for a new, improved version of raid named EERaid. They were able to produce savings of 60% and 70% of the energy used in typical RAID setups while keeping the efficiency on the same level as that of DPRM. They outline different setups based on current RAID striping such as RAID 0,1,5,10,50, etc. Very informative paper with good information. [2] This paper states a high performance (approaching performance of RAID 5) algorithm that is more fault tolerant than other RAID options. This involves multiple layers of RAID controllers and a variable number of disks. One master RAID controller has multiple RAID controllers set up in RAID 5 with each secondary controller attached to disks also in RAID 5. Faster and more reliable, but not more power efficient. [3] This paper outlines a model in which a manager dolls out resources to the users based on the client's bid for services. Those who bid higher get more resources, these resources are all scaled dynamically based on current server load. Outlines a complex cost equation and some energy saving benefits such as powering down (sleeping) nodes that are not currently needed, but waking them up when they are needed. [4] FAWN attempts to conserve energy with flash memory and low powered CPUs lacking L2/ L3 cache. They claim savings of 2 orders of magnitude of power for requests versus standard magnetic drives. They also claim that with less of the CPU taken up by L2/L3 cache there is more room so more processing power for simple actions like retrieving data. [5] This paper outlines 4 approaches to reducing the power consumption by disks. The first three show no real gain, but the fourth models a drive of varying speed so that it can be sped up for periods of high activity and slowed down for periods of low activity. They show 20% drop in power usage by this method. [6] A method for using redundancy in disks while saving energy. This method employs keeping one of two drives in a low-power state until one of three conditions are met: 1. high volume of accesses, 2. when disks fail, 3. at interval periods for syncing the two disks. While they see a savings of 27-60% the major issue seems to be data integrity. If the disk holding the log of the most current changes dies before the predetermined sync time, those changes would be lost. As it is not power efficient (currently) to power drives down for less than 30 seconds [reference to come later], this means potentially long periods of time without updates losing one of the important qualities of redundancy in disks. [7] Taking a different path, this paper aims to analyze the reliability of two types of parallel disk arrays: MAID and PDC. They introduce a new model for determining the life expectancy of drives and introduce methods for saving energy and extending the life of drives at the same time. Keeping drives out of landfills and using less energy at the same time offers great promise, but nothing was implemented besides their model for determining the life of drives. [8]While not able to realize significant gains (greater than 20%) in most simulations, this article's ideas warrant a closer look into possible modifications and adoption into current disk prefetching schemes. Banking on the unquoted statistic that applications use 20% of the data available 80%

2 of the time they believe they can of energy savings of 12-26%. This seems unlikely to be the case in a real world scenario since the 20% in the buffer is not enough for all data accesses and the 20% of the time spent accessing data is not shifted to one side or the other, but interspersed randomly across the log of accesses. [9] Buffered Disks is a popular topic and in this paper the authors were able to realize power savings of 38%. The idea behind the buffered disks in this paper has four tiers. There is a main BUD controller driving the entire system, below it is the buffered cache, which is in front of the buffered disks, which all sit in front of the data disks. Writes are sent down the aforementioned path and are sent to the first available spots each time. This ensures no data is waiting for a write while there are idle drives that could process it. Data in the cache (once loaded) is almost immediately written to avoid loss and preserve integrity. [10] This paper delves into compiler based organization of data on striped disks. This enables the compiler to inspect the program, determine the best size for pieces of data and splits them across the disks in round robin fashion accordingly. The only possible drawbacks I see are bringing a fully accepted compiler to the market that does not slow down considerably compile time. The technique of allowing the compiler to arrange the disk layout is capable of being combined with DRPM and other hardware energy saving techniques for an even larger gain. [11] The authors have developed a model for determining the reliability of power saving methods in parallel disk setups. This is a very important idea that has been overlooked by many researchers. In their model they determine (using Markov process) the reliability based on factors such as switching power modes and varying RPM while leaving the model open to other methods that may come. Mean Time To Data Loss and Mean Time To Failure are both important statistics determined in this paper. This model is excellent for determining the feasibility and usefulness of a power saving scheme on parallel disks. [12] MINT is a reliability model for disks, the authors in this paper take MINT and utilize it for reliability of multi-disk arrays (specifically MAID and PDC) using common power saving features such as varying voltage frequency and multi-rpm disks. Their study results in a model useful for many parallel disk systems, using data obtained from other studies, which can determine the average lifespan of disks based on their usage and configuration. [13] This is another compiler derived energy saving technique developed to work in combination with hardware techniques for saving energy. The authors achieved the best energy savings when using DRPM in conjunction with their interprocessor code-restructuring approach that schedules the code to be executed considering the disk layout information. Inter-P-TPM had an experimental savings of 43%, which was the best of the models tested. [14] Here, the idea of utilizing multiple arms simultaneously inside of a single disk to seek data is explored in detail. Many tests and comparisons are done to determine the benefits including a cost analysis based on the prices of current parts for disks. The higher cost of the disk is then compared to the energy savings gained by using such a disk. The intra-disk parallelism here shows great promise in the simulations and modeling but such a drive has not been produced since 1980 and that disk was considered to be too costly for the benefits achieved. Real data

3 from different organizations based on their type of disk seeks was used to evaluate the performance gains of the Intra-Disk Parallelism. DASH Taxonomy was also introduced in this paper. [15] HYBUD is a hybrid of two data storage schemes, non-volatile flash storage and buffered disks. Although flash storage is expensive using a small amount of it in a buffered disk architecture will mitigate the cost and improve energy usage. Requests will first go to the flash memory, then to the buffered disks and finally the data disks. The majority of the time the data disks will be in suspended mode because much of the frequently used data will be cached in one of the storage solutions ahead of it. Flash is a good first-buffer because it has very quick read times, but has slower write times which is why it is not as good for permanent storage. [16]This paper outlines a different BUD architecture (PREBUD) where a future window is developed to gather a list of data blocks that will be accessed in the future. The buffered disks then pre-fetch the blocks that are expected to be used before they are requested, leaving the data disks to be in a low power state for longer if the future window only includes blocks that are currently on the buffer disks. This scheme also differs from other prefetching schemes in that it pre-fetches data with consideration of the energy usage it will cause. It will combine requests for data to maximize the length of time that the data disk stays in low powered mode. Buffered disks (if too busy from too many requests) may be bypassed to achieve faster data retrieval times. [17]The strategy employed in this paper is called DRPM. Dynamic RPM scaling is similar to TPM (traditional power management) in that disks power is lowered with lower usage. However, unlike TPM which only has 3 states (only 1 for data seeking): in-use, idle, and suspended, DRPM has multiple usable power states based on the amount of power given to the spindle to raise or lower RPM of the platter. While a lower RPM will lower the speed of the response to the request for data, it is the authors' contention that this is negligible and only matters for a high volume of seeks, which would then incur a higher speed of the spindle. Much time is spent analyzing the performance and power cost however not much is spent in regards to the reliability of such a system, which warrants further study. [18] This paper outlines a scheme for conserving energy based on DVS (dynamic voltage scaling) coupled with an expected retrieval time of the data. The user makes a request, and the expected answer time is calculated and the request is distributed across the disk array. If there are enough requests that the request time will not be fulfilled, then the controller will scale up the voltage to the array so that they will seek the data quicker, but if there are few requests then voltage can be scaled down. [19] Hibernator was able to realize energy savings of up to 65% versus standard RAID 5 configurations without DRPM. This system uses a corse grained approach to sleeping and waking the drives which should extend the life of the drives as there are a finite number of starts and stops that a disk can handle. This setup was also able to give soft guarantees for response times whereas DRPM and PDC were not able to come close to the requested response time. Hibernator also dynamically shifts data across the array to provide faster accesses and a more

4 balanced load to save energy. Hibernator is a very advanced, well thought out compilation of some different schemes plus some original ones. [1] D. Li and J. Wang, "EERAID: energy efficient redundant and inexpensive disk array," ACM SIGOPS European Workshop, [2] S.H. Baek, B.W. Kim, E.J. Joung, and C.W. Park, "Reliability and Performance of Hierarchical RAID with," Electronics, 2001, pp [3] J.S. Chase, D.C. Anderson, P.N. Thakar, A.M. Vahdat, and R.P. Doyle, "Managing energy and server resources in hosting centers," ACM SIGOPS Operating Systems Review, vol. 35, 2001, p [4] D. Andersen, J. Franklin, M. Kaminsky, A. Phanishayee, L. Tan, and V. Vasudevan, "FAWN: A fast array of wimpy nodes," Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles, ACM, 2009, p. 1â14. [5] E. Carrera, E. Pinheiro, and R. Bianchini, "Conserving disk energy in network servers," Proceedings of the 17th annual international conference on Supercomputing, ACM, 2003, p. 97. [6] E. Pinheiro and R. Bianchini, "Exploiting Redundancy to Conserve Energy," Energy, pp [7] S. Yin, X. Ruan, A. Manzanares, Z. Ding, J. Xie, J. Majors, and X. Qin, "Improving Reliability of Energy-Efficient Parallel Storage Systems by Disk Swapping," Structure, 2009, pp [8] A. Manzanares, K. Bellam, and X. Qin, "A Prefetching Scheme for Energy Conservation in Parallel Disk Systems," Proc. NSF Next Generation Software Program Workshop, April, [9] Z. Zong, M. Briggs, N. O'Connor, and X. Qin, "An Energy-Efficient Framework for Large- Scale Parallel Storage Systems," IEEE International Parallel and Distributed Processing Symposium, IPDPS 2007, 2007, p [10] S. Son, G. Chen, M. Kandemir, and A. Choudhary, "Exposing disk layout to compiler for reducing energy consumption of parallel disk based systems," Proceedings of the tenth ACM SIGPLAN symposium on Principles and practice of parallel programming, ACM, 2005, p [11] F. Shen, X. Qin, A. Salazar, A. Manzanares, and K. Bellam, "An Energy-Efficient Reliability Model for Parallel Disk Systems," 2009 Sixth International Conference on Information Technology: New Generations, 2009, pp [12] S. Yin, X. Ruan, A. Manzanares, and X. Qin, "How reliable are parallel disk systems when energy-saving schemes are involved," in Proc. IEEE International Conference on Cluster Computing (CLUSTER, [13] S. Woo Son, G. Chen, O. Ozturk, M. Kandemir, and A. Choudhary, "Compiler-Directed Energy Optimization for Parallel Disk Based Systems," IEEE Transactions on Parallel and Distributed Systems, vol. 18, 2007, pp [14] S. Sankar, S. Gurumurthi, and M. Stan, "Intra-disk parallelism: an idea whose time has come," ACM SIGARCH Computer Architecture News, vol. 36, 2008, p [15] M. Nijim, A. Manzanares, X. Ruan, and X. Qin, "HYBUD: An Energy-Efficient Architecture for Hybrid Parallel Disk Systems," 2009 Proceedings of 18th International Conference on Computer Communications and Networks, vol , 2009, pp [16] A. Manzanres, X. Ruan, S. Yin, M. Nijim, W. Luo, and X. Qin, "Energy-Aware Prefetching for Parallel Disk Systems: Algorithms, Models, and Evaluation," 2009 Eighth IEEE International Symposium on Network Computing and Applications, 2009, pp

5 [17] S. Gurumurthi, A. Sivasubramaniam, M. Kandemir, and H. Franke, "DRPM: dynamic speed control for power management in server class disks," Proceedings of the 30th annual international symposium on Computer architecture, ACM, 2003, p [18] M. Nijim, A. Manzanares, and X. Qin, "An Adaptive Energy-Conserving Strategy for Parallel Disk Systems," th IEEE/ACM International Symposium on Distributed Simulation and Real-Time Applications, 2008, pp [19] Q. Zhu, Z. Chen, L. Tan, Y. Zhou, K. Keeton, and J. Wilkes, "Hibernator: helping disk arrays sleep through the winter," Proceedings of the twentieth ACM symposium on Operating systems principles, ACM, 2005, p. 190.