Trade Of Between SSD and HDD

(1)4. VII. July 2016.

(2) www.ijraset.com IC Value: 13.98. Volume 4 Issue VII, July 2016 ISSN: 2321-9653. International Journal for Research in Applied Science & Engineering Technology (IJRASET). Trade Of Between SSD and HDD Anagha Ramnath Kadve Student, Department of Computer Engineering, Bharati Vidyapeeth Deemed University College of Engineering, Pune-46 Abstract— Energy, device endurance and Performance for storage systems are the trade-offs among them. Designs optimized for workload or one dimension often suffer from another. Therefore, it is necessary to study the trade-offs to enable adaptation to workloads and dimensions. Hybrid drives have been studied more closely, due to flash SSD has emerged. However, hybrids are mainly structured for large throughput, efficient energy consumption, or doing the better endurance-leaving quantitative study on the trade-offs undescribed. To help study the trade-offs, past endurance studies also less a concrete model. Previous designs are often based on nonflexible policies that cannot take easily to changing conditions. We structured and developed GreenDM, an all type of hybrid drives that forms Flash-based SSDs with traditional HDDs. For hot data, cache or as primary storage can be used by SSD. We keep our model endurance combine with GreenDM to go through these trade-offs. GreenDM not requires modifications and presents a block interface to existing software. GreenDM endeavors tunable parameters to not able the system to accommodate too many workloads. Using commodity SSD and HDD drives, we have developed, structured and carefully evaluated GreenDM with some workloads. Keywords—Disks, Solid State Disk, Hard Disk Drive, Cost Trade-off, Performance Trade-off, Flash Disks. I. INTRODUCTION In storage technology, the HDD has been the traditional mainstay. However, yearly 2% performance increases due to random I/O, the capacity of HDDs has increased 40% yearly. Which are some part of today's high capacity and performance required in the cloud, enterprise, web and virtualized applications, HDDs cannot bring a storage solution which is cost effective, even at drop-in information at per GB cost. Recently, to gain prominence has started the solid state drive (SSD) which is alternative storage device technology. SSDs have much less capacity per drive, but it offers exceptionally high performance. SSD have a write survival limit compared to HDDs and are relatively expensive. For the given substances of SDDs and HDDs, Nowadays IT sections have an option, but the challenge of finding the effective way to fulfill the cost capacity and performance needs of their project applications. To study these challenges and verify the merging SSDs and HDDs in their storage fabric, firstly IT section should measure the cost value, capacity, and performance of SSDs vs. HDDs for other applications. The motive to support IT sector with the information they needed to most efficiently take benefit of the different cost and capacity, performance, characteristics supported by SSDs and HDDs. II.. LITERATURE REVIEW. A. Solidstate storage Vs. Harddrives: In-Memory Bigger storage boost can be acquired by employing flash with in-memory applications. The basic applications like covering, databases and big data analytics in which memory or storage are obstacles. Flash should be started to forward enterprise applications and should be situated as near to the system memory and processor as possible where, eventually, all application data must consist for the application to act upon. The line within caching and storage is being bound by the flash. To reduce latency and increase application performance by orders of magnitude, it is mostly being placed outside of the storage array. B. Solid State Storage Vs. Hard Drives: Hybrid Best? Spinning disk being slower than solid state storage, it is unpredictable because it finds its ways, in such a way pricing rate fall too much. It may be used for applications and virtualization where throughput and latency are more. SSD are better for high I/O applications, and very low latencies are demanded in workloads in case of online transaction processing (OLTP) and high transaction database applications like large-volume financial trading systems where all transactions are critical, and latency tolerance is being counted in microseconds. SSDs work best in a hybrid configuration with HDDs. C. Measuring HDD And SSD Workload Performance. 473 ©IJRASET: All Rights are Reserved.

(3) www.ijraset.com IC Value: 13.98. Volume 4 Issue VII, July 2016 ISSN: 2321-9653. International Journal for Research in Applied Science & Engineering Technology (IJRASET) 1) Latency: A access time means latency. Read Access Time of HDD's is 15.785 ms sounds, but the Access Time of SSD's is 0.031 ms which is 509 times faster than HDD's. On the HDD, read access time is slower than the write, to find all the address of some space is required. HDDs is slower than the SSDs access time of write by 102 times. 2) Throughput: Speed of SSD is 514.28 MB/s vs. HDD is 149.86 MB/s which is 3.4 times slower. SSD'S 4K read speed is surprisingly large that is 36.79MB/s, compared to the HDD’s is 0.69MB/s, and again speed is 53 times slower than HDD. Write speed of HDDs is 4k, which is 1.22 MB/s is much better than read speed but decreased by 128.65 MB/s in SDD and 105 times faster. 3) Capacity: When HDD comes to sheer capacity and how much data can be stored that time HDDs work as workhorses. When SSDs are only affordable at lower capacities, SSHD technology also proposal large size points at affordable price points. SSDs capacity are extremely high expensive. 4) Bandwidth: To supporting computing, SSDs provide high performance for restarting and peak read/write performance that needs to be enhanced multitasking capabilities. On the other hand, SSD performance for booting, loading data, and launching can provide by SSHD. Plentiful performance for the majority of PC platforms provided by HDDs. D. HDD Versus SSD Comparison Chart. Speed. Heat, Electricity, Noise. Defragmentation. Components. Weight. Dealing with vibration. HDD. SDD. Compared to SSD, HDD has longer read/write times, supports fewer IOPs and higher latency. To generating heat, rotate the platters and noise, hard disk drives use more electricity. Due to fragmentation, the performance of HDD drives worsens; so that, they require to be defragmented periodically. HDD include moving parts – thin layer of magnetic material is covered by one or many platters holds by a motor-driven spindle. On disks, top write head and read head are positioned HDD’s drives are heavier than SSD’s.. Compared to HDD, SSD has faster read/writes, supports more IOPs and lower latency. In solid state drives, no such rotation is needed; they do not generate heat or noise and use less power. SSD is not require defragmentation because drive performance of SSD does not get affected by fragmentation A memory chip is nonmoving parts of SSD. With an interface, connector SSD is integrated circuits (ICs), which is interconnected. Controller, cache, and capacitor are the three main components. SSD drives have motor, spindle and rotating disks, hence they are lighter than HDD drives. SSD drives have 2000Hz vibration which is more durable than HDD.. Due to vibration, HDDs moving part make them more habitual to crashes and damages.. III. CONCLUSIONS Solid state hybrid drives take a nearer look. They in all probability allow the good combination of performance traits to meet your needs. Even the HDD and SSD are nearer. HDD vs. SSD – Which is good for your budget and requirements? For instance, go for SDD if you are a heavy user then Otherwise HDD is good. If we count higher the checkmarks, the HDD gets 3, and SSD gets 9. It means that an SSD is three times better than an HDD. It all depends on specific requirement. The comparison in between SSD and HDD is just to put out the pros and cons. But in my study view, SSDs is better than HDD. REFERENCES [1] [2]. Kasavajhala, V., 2011. Solid state drive vs. hard disk drive price and performance study. Proc. Dell Tech. White Paper, pp.8-9. IN-MEMORY, I.N.S.I.D.E., 2012. Using in-memory analytics to quickly crunch big data.. 474 ©IJRASET: All Rights are Reserved.

(4) www.ijraset.com IC Value: 13.98. Volume 4 Issue VII, July 2016 ISSN: 2321-9653. International Journal for Research in Applied Science & Engineering Technology (IJRASET) [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35]. Leong, D., 2009. A new revolution in enterprise storage architecture. IEEE Potentials, 28(6), pp.32-33. Pébay, P., Thompson, D., Bennett, J. and Mascarenhas, A., 2011, May. Design and performance of a scalable, parallel statistics toolkit. In Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW), 2011 IEEE International Symposium on (pp. 1475-1484). IEEE. Hong, S. and Shin, D., 2010, May. Nand flash-based disk cache using slc/mlc combined flash memory. In Storage Network Architecture and Parallel I/Os (SNAPI), 2010 International Workshop on (pp. 21-30). IEEE. Raval, K.S., Suryawanshi, R.S., Naveenkumar, J. and Thakore, D.M., 2011. The Anatomy of a Small-Scale Document Search Engine Tool: Incorporating a new Ranking Algorithm. International Journal of Engineering Science and Technology, 1(3), pp.5802-5808. Archana, R.C., Naveenkumar, J. and Patil, S.H., 2011. Iris Image Pre-Processing And Minutiae Points Extraction. International Journal of Computer Science and Information Security, 9(6), p.171. Jayakumar, M.N., Zaeimfar, M.F., Joshi, M.M. and Joshi, S.D., 2014. INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY (IJCET). Journal Impact Factor, 5(1), pp.46-51. Naveenkumar, J. and Joshi, S.D., 2015. Evaluation of Active Storage System Realized through MobilityRPC. Jayakumar, D.T. and Naveenkumar, R., 2012. SDjoshi,“. International Journal of Advanced Research in Computer Science and Software Engineering,” Int. J, 2(9), pp.62-70. Jayakumar, N., Singh, S., Patil, S.H. and Joshi, S.D., Evaluation Parameters of Infrastructure Resources Required for Integrating Parallel Computing Algorithm and Distributed File System. Jayakumar, N., Bhardwaj, T., Pant, K., Joshi, S.D. and Patil, S.H., A Holistic Approach for Performance Analysis of Embedded Storage Array. Naveenkumar, J., Makwana, R., Joshi, S.D. and Thakore, D.M., 2015. OFFLOADING COMPRESSION AND DECOMPRESSION LOGIC CLOSER TO VIDEO FILES USING REMOTE PROCEDURE CALL. Journal Impact Factor, 6(3), pp.37-45. Naveenkumar, J., Makwana, R., Joshi, S.D. and Thakore, D.M., 2015. Performance Impact Analysis of Application Implemented on Active Storage Framework. International Journal, 5(2). Salunkhe, R., Kadam, A.D., Jayakumar, N. and Thakore, D., In Search of a Scalable File System State-of-the-art File Systems Review and Map view of new Scalable File system. Salunkhe, R., Kadam, A.D., Jayakumar, N. and Joshi, S., Luster A Scalable Architecture File System: A Research Implementation on Active Storage Array Framework with Luster file System. Jayakumar, N., Reducts and Discretization Concepts, tools for Predicting Student’s Performance. Jayakumar, M.N., Zaeimfar, M.F., Joshi, M.M. and Joshi, S.D., 2014. INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY (IJCET). Journal Impact Factor, 5(1), pp.46-51. Kumar, N., Angral, S. and Sharma, R., 2014. Integrating Intrusion Detection System with Network Monitoring. International Journal of Scientific and Research Publications, 4, pp.1-4. Namdeo, J. and Jayakumar, N., 2014. Predicting Students Performance Using Data Mining Technique with Rough Set Theory Concepts. International Journal, 2(2). Naveenkumar, J., Keyword Extraction through Applying Rules of Association and Threshold Values. International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), ISSN, pp.2278-1021. Kakamanshadi, G., Naveenkumar, J. and Patil, S.H., 2011. A Method to Find Shortest Reliable Path by Hardware Testing and Software Implementation. International Journal of Engineering Science and Technology (IJEST), ISSN, pp.0975-5462. Naveenkumar, J. and Raval, K.S., Clouds Explained Using Use-Case Scenarios. Naveenkumar J, S.D.J., 2015. Evaluation of Active Storage System Realized Through Hadoop. International Journal of Computer Science and Mobile Computing, 4(12), pp.67–73. RishikeshSalunkhe, N.J., 2016. Query Bound Application Offloading: Approach Towards Increase Performance of Big Data Computing. Journal of Emerging Technologies and Innovative Research, 3(6), pp.188–191. Sagar S lad s d joshi, N.J., 2015. Comparison study on Hadoop’s HDFS with Lustre File System. International Journal of Scientific Engineering and Applied Science, 1(8), pp.491–494. Salunkhe, R. et al., 2015. In Search of a Scalable File System State-of-the-art File Systems Review and Map view of new Scalable File system. In nternational Conference on electrical, Electronics, and Optimization Techni ques (ICEEOT) - 2016. pp. 1–8. BVDUCOE, B.B., 2011. Iris Image Pre-Processing and Minutiae Points Extraction. International Journal of Computer Science & Information Security. P. D. S. D. J. Naveenkumar J, “Evaluation of Active Storage System Realized through MobilityRPC,” Int. J. Innov. Res. Comput. Commun. Eng., vol. 3, no. 11, pp. 11329–11335, 2015 N. Jayakumar, S. Singh, S. H. Patil, and S. D. Joshi, “Evaluation Parameters of Infrastructure Resources Required for Integrating Parallel Computing Algorithm and Distributed File System,” IJSTE, vol. 1, no. 12, pp. 251–254, 2015. N. Jayakumar, T. Bhardwaj, K. Pant, S. D. Joshi, and S. H. Patil, “A Holistic Approach for Performance Analysis of Embedded Storage Array,” Int. J. Sci. Technol. Eng., vol. 1, no. 12, pp. 247–250, 2015. J. Naveenkumar, R. Makwana, S. D. Joshi, and D. M. Thakore, “Performance Impact Analysis of Application Implemented on Active Storage Framework,” Int. J., vol. 5, no. 2, 2015. N. Jayakumar, “Reducts and Discretization Concepts, tools for Predicting Student’s Performance,” Int. J. Eng. Sci. Innov. Technol., vol. 3, no. 2, pp. 7–15, 2014. J. Namdeo and N. Jayakumar, “Predicting Students Performance Using Data Mining Technique with Rough Set Theory Concepts,” Int. J. Adv. Res. Comput. Sci. Manag. Stud., vol. 2, no. 2, 2014. R. Salunkhe, A. D. Kadam, N. Jayakumar, and S. Joshi, “Luster A Scalable Architecture File System: A Research Implementation on Active Storage Array Framework with Luster file System.,” in ICEEOT, 2015.. 475 ©IJRASET: All Rights are Reserved.

(5)

(6)

New documents

With the aid of the estimates for the general Kloosterman sums and the properties of trigonometric sums, we obtain an upper bound of the general partial Gaussian sums over the number

Based on the extensive application of LSTM neural network in time series forecasting, this paper uses LSTM neural network to establish building energy consumption forecasting model,

In [], the quaternion matrix equation  was considered and the least-squares solution with the least norm was given through the use of the Kronecker product and the Moore-Penrose

Among them, data related to energy consumption, such as lighting power and socket power, are recorded on the energy management platform; data related to building operation, such as the

On the O/t convergence rate of the alternating direction method with LQP regularization for solving structured variational inequality problems Abdellah Bnouhachem1,2*† , Abdul Latif3†

The method in this paper obtained contour lines by using mathematical method on the basis of conventional free-form surface method and further made controllable change on the width,

In Section , we will establish the reverse Hölder inequality for weights ω in the class Anp μ adapted to our general product measure μ, which is not necessarily doubling with respect

This study mainly discusses: 1 the detection method of moving targets to acquire the images of moving zooplankton; 2 the two factors that affect the holographic images recognition

Our aim in this article is to investigate the superiorization and the bounded perturbation resilience of the viscosity algorithm and construct algorithms based on them.. Next, we list

Combining the analytic hierarchy process with the coefficient of variation method, comprehensive subjective evaluation And objective data evaluation to determine the weight; using the