FlashBlox: Achieving Both Performance Isolation and Uniform Lifetime for Virtualized SSDs

Size: px
Start display at page:

Download "FlashBlox: Achieving Both Performance Isolation and Uniform Lifetime for Virtualized SSDs"

Transcription

1 FlashBlox: Achieving Both Performance Isolation and Uniform Lifetime for Virtualized SSDs Jian Huang Anirudh Badam Laura Caulfield Suman Nath Sudipta Sengupta Bikash Sharma Moinuddin K. Qureshi

2 Flash Has Changed Over the Last Decade Performance Improvement 100x lower latency 5,000x higher throughput 2

3 Flash Has Changed Over the Last Decade Performance Improvement 100x lower latency 5,000x higher throughput Increased Parallelism Dozens of parallel chips 2

4 Flash Has Changed Over the Last Decade Performance Improvement 100x lower latency 5,000x higher throughput Increased Parallelism Dozens of parallel chips Became Commodity Less than $0.3/GB 2

5 Flash Has Changed Over the Last Decade Performance Improvement 100x lower latency 5,000x higher throughput Increased Parallelism Dozens of parallel chips Became Commodity Less than $0.3/GB Significant improvements on Flash 2

6 Shared Flash-Based Solid State Disk (SSD) in the Cloud. 3

7 Shared Flash-Based Solid State Disk (SSD) in the Cloud. SSDs are virtualized and shared in data centers 3

8 Performance Interference in Shared SSD. Write Read Flash Translation Layer Flash-based SSD: A Black Box 4

9 Performance Interference in Shared SSD. Read/write Write Read interferences cause long (3x) tail latency! Flash Translation Layer Flash-based SSD: A Black Box 4

10 Performance Interference in Shared SSD. Write Read Flash Translation Layer 4

11 Performance Interference in Shared SSD. Write Read Flash Translation Layer 4

12 FlashBlox: Hardware Isolation in Cloud Storage. Flash Translation Layer 5

13 FlashBlox: Hardware Isolation in Cloud Storage. Leveraging parallel chips for hardware isolation Flash Translation Layer 5

14 Internal Parallelism Enables Hardware Isolation 6

15 Internal Parallelism Enables Hardware Isolation -Level Parallelism 6

16 Internal Parallelism Enables Hardware Isolation -Level Parallelism -Level Parallelism 6

17 Internal Parallelism Enables Hardware Isolation -Level Parallelism Plane-level parallelism is constrained as each chip contains only one address buffer -Level Parallelism Plane-Level Parallelism 6

18 Internal Parallelism Enables Hardware Isolation -Level Parallelism -Level Parallelism Plane-Level Parallelism Different parallelism level provides different isolation guarantee 6

19 New Abstractions for Hardware Isolation 7

20 New Abstractions for Hardware Isolation Virtual SSD ( Level) Virtual SSD ( Level) Virtual SSD (Plane Level) High Medium Low 7

21 New Abstractions for Hardware Isolation Virtual SSD ( Level) Virtual SSD ( Level) Virtual SSD (Plane Level) Software-based High Medium Low 7

22 Hardware Isolation Meets the Pay-As-You-Go Model in Cloud vssd () vssd () vssd (Software) 8

23 Hardware Isolation Meets the Pay-As-You-Go Model in Cloud Azure DocumentDB vssd () Azure SQL Database vssd () Amazon DynamoDB vssd (Software) 8

24 Hardware Isolation Meets the Pay-As-You-Go Model in Cloud Throughput Azure DocumentDB vssd () Azure SQL Database vssd () Single Amazon Partition Size Price DynamoDB vssd (Software) 8

25 Hardware Isolation Meets the Pay-As-You-Go Model in Cloud Hundreds of vssds can be supported in a single server vssd () vssd () vssd (Software) 8

26 Impact of Hardware Isolation on SSD Lifetime. Flash Translation Layer 9

27 Average #Blocks Erased/sec Impact of Hardware Isolation on SSD Lifetime The average rate at which. flash blocks are erased Flash Translation Layer 9

28 Average #Blocks Erased/sec Impact of Hardware Isolation on SSD Lifetime The average rate at which. flash blocks are erased Flash Translation Layer 9

29 Average #Blocks Erased/sec Impact of Hardware Isolation on SSD Lifetime The average rate at which. flash blocks are erased Flash Translation Layer Flash blocks wear out at different rate with different workload 9

30 Impact of Hardware Isolation on SSD Lifetime. Write Intensive Flash Translation Layer 9

31 App App App FlashBlox Challenges SSD Lifetime Performance Isolation 10

32 App App App FlashBlox Challenges SSD Lifetime Performance Isolation App App App SSD Lifetime Performance Isolation 10

33 App App App FlashBlox Challenges SSD Lifetime Performance Isolation 10

34 Used Erase Cycles FlashBlox: Swapping s for Wear Balance Adjusting the wear imbalance at a more coarse time granularity can achieve near-ideal SSD lifetime 11

35 Used Erase Cycles FlashBlox: Swapping s for Wear Balance The channel that has incurred the maximum wearout The channel that has the minimum rate of wearout 11

36 Used Erase Cycles FlashBlox: Swapping s for Wear Balance migration takes 15 minutes, once per 19 days Overall performance drops only for 0.04% of all the time 11

37 Used Erase Cycles How Frequently Should We Swap? Imbalance = MaxWear / AvgWear

38 Used Erase Cycles How Frequently Should We Swap? Imbalance = MaxWear 4 / AvgWear App M

39 Used Erase Cycles How Frequently Should We Swap? Imbalance = MaxWear 42 / AvgWear App M 1 M

40 Used Erase Cycles How Frequently Should We Swap? Imbalance = MaxWear 424/3 / AvgWear App M M M

41 Used Erase Cycles How Frequently Should We Swap? Imbalance = MaxWear 424/3 1 / AvgWear App M M M M

42 Used Erase Cycles How Frequently Should We Swap? Imbalance = MaxWear 1424/3 8/5 8/7 4/3 / AvgWear App M M M M M M M M

43 Used Erase Cycles How Frequently Should We Swap? Imbalance = MaxWear 1424/3 8/5 8/7 4/3 / AvgWear App M M M M M M M M How many times should we swap within SSD lifetime? 12

44 Quantifying the Swapping Frequency Assume there are N channels, wear imbalance target: 1+x after K rounds of cycling: Wear Imbalance = (MK + M)/(MK + M/N) = (K + 1)/(K + 1/N) (1 + x) Maximum Wearout Average Wearout 13

45 Quantifying the Swapping Frequency Assume there are N channels, wear imbalance target: 1+x after K rounds of cycling: Wear Imbalance = (MK + M)/(MK + M/N) = (K + 1)/(K + 1/N) (1 + x) K (N 1 x) / (Nx) 13

46 Quantifying the Swapping Frequency Assume there are N channels, wear imbalance target: 1+x after K rounds of cycling: Wear Imbalance = (MK + M)/(MK + M/N) = (K + 1)/(K + 1/N) (1 + x) K (N 1 x) / (Nx) If N = 16, x = 0.1, then K = 9, which means after swap NK = 148 times, we can guarantee the wear imbalance is bounded in

47 Quantifying the Swapping Frequency Assume there are N channels, wear imbalance target: 1+x after K rounds of cycling: Wear Imbalance = (MK + M)/(MK + M/N) = (K + 1)/(K + 1/N) (1 + x) K (N 1 x) / (Nx) For an SSD with 5 years lifetime, swap once per 12 days can guarantee the channels are well balanced for worst case If N = 16, x = 0.1, then K = 9, which means after swap NK = 148 times, we can guarantee the wear imbalance is bounded in

48 Used Erase Cycles Adaptive Wear Leveling in Practice App App App App M M/3 M/

49 Used Erase Cycles Adaptive Wear Leveling in Practice App App App App M M/3 M/ Using erase rate as the trigger condition for swapping 14

50 Used Erase Cycles Intra Wear Leveling

51 Used Erase Cycles Intra Wear Leveling s will be swapped along with the channel migration 15

52 Used Erase Cycles Intra Wear Leveling s will be swapped along with the channel migration + Intra-chip wear leveling mechanisms 15

53 FlashBlox Architecture App Resource Manager -Level Wear Leveling -Level Wear Leveling Flash 16

54 FlashBlox Architecture App App Virtual SSD App Virtual SSD Resource Manager -Level Wear Leveling -Level Wear Leveling Flash 16

55 FlashBlox Architecture App App Virtual SSD App Virtual SSD Resource Manager Isolation, Bandwidth & Capacity Requirement (Virtual SSD to Parallel s Mappings) -Level Wear Leveling -Level Wear Leveling Flash 16

56 FlashBlox Architecture App App Virtual SSD App Virtual SSD Resource Manager Isolation, Bandwidth & Capacity Requirement Pay-As-You-Go (Virtual SSD to Parallel Model s Mappings) in Cloud -Level Wear Leveling -Level Wear Leveling Flash 16

57 FlashBlox Architecture App App Virtual SSD App Virtual SSD Resource Manager Isolation, Bandwidth & Capacity Requirement (Virtual SSD to Parallel s Mappings) -Level Wear Leveling Inter Swapping -Level Wear Leveling Flash 16

58 FlashBlox Architecture App App Virtual SSD App Virtual SSD Resource Manager Isolation, Bandwidth & Capacity Requirement (Virtual SSD to Parallel s Mappings) -Level Wear Leveling Inter Swapping -Level Wear Leveling Intra Swapping Other FTL Algorithms Intra Swapping Flash 16

59 FlashBlox Experimental Setup 16 channels 4 chips 4 planes 16 KB page size 14 data center workloads Yahoo Cloud Service Benchmark Bing Search / Index / PageRank Transactional Database Azure Storage 17

60 99th Percentile Latency (microsecons) Tail Latency Reduction with FlashBlox App1-Software Isolation App1-FlashBlox App2-Software Isolation App2-FlashBlox App1 App2 A: Session store recording recent actions B: Photo tagging C: User profile cache D: User status update E: Threaded conversations F: User database A+A A+B A+C A+D A+E A+F Yahoo Cloud Service Benchmark (YCSB) 18

61 99th Percentile Latency (microsecons) Tail Latency Reduction with FlashBlox App1-Software Isolation App1-FlashBlox App2-Software Isolation App2-FlashBlox App1 App2 A+A A+B A+C A+D A+E A+F Yahoo Cloud Service Benchmark (YCSB) Tail latency reduction: 2.6x, average latency reduction: 1.4x 18

62 Latency (milliseconds) Impact of Migration on Application Performance Bing Search s Performance During Migration Without Migration With Migration Time (Seconds) 19

63 Latency (milliseconds) Impact of Migration on Application Performance Bing Search s Performance During Migration 34% With Migration Without Migration Time (Seconds) 19

64 Latency (milliseconds) Impact of Migration on Application Performance Bing Search s Performance During Migration 34% Without Migration With Migration Time (Seconds) migration takes 15 minutes, once per 19 days Overall performance drops only for 0.04% of all the time 19

65 FlashBlox Summary 2.6x reduction on tail latency Near-ideal SSD lifetime Swap once per 19 days 20

66 Thanks! Jian Huang Anirudh Badam Laura Caulfield Suman Nath Sudipta Sengupta Bikash Sharma Moinuddin K. Qureshi Q&A 21

D E N A L I S T O R A G E I N T E R F A C E. Laura Caulfield Senior Software Engineer. Arie van der Hoeven Principal Program Manager

D E N A L I S T O R A G E I N T E R F A C E. Laura Caulfield Senior Software Engineer. Arie van der Hoeven Principal Program Manager 1 T HE D E N A L I N E X T - G E N E R A T I O N H I G H - D E N S I T Y S T O R A G E I N T E R F A C E Laura Caulfield Senior Software Engineer Arie van der Hoeven Principal Program Manager Outline Technology

More information

Performance Modeling and Analysis of Flash based Storage Devices

Performance Modeling and Analysis of Flash based Storage Devices Performance Modeling and Analysis of Flash based Storage Devices H. Howie Huang, Shan Li George Washington University Alex Szalay, Andreas Terzis Johns Hopkins University MSST 11 May 26, 2011 NAND Flash

More information

Performance Benefits of Running RocksDB on Samsung NVMe SSDs

Performance Benefits of Running RocksDB on Samsung NVMe SSDs Performance Benefits of Running RocksDB on Samsung NVMe SSDs A Detailed Analysis 25 Samsung Semiconductor Inc. Executive Summary The industry has been experiencing an exponential data explosion over the

More information

ParaFS: A Log-Structured File System to Exploit the Internal Parallelism of Flash Devices

ParaFS: A Log-Structured File System to Exploit the Internal Parallelism of Flash Devices ParaFS: A Log-Structured File System to Exploit the Internal Parallelism of Devices Jiacheng Zhang, Jiwu Shu, Youyou Lu Tsinghua University 1 Outline Background and Motivation ParaFS Design Evaluation

More information

Flash Trends: Challenges and Future

Flash Trends: Challenges and Future Flash Trends: Challenges and Future John D. Davis work done at Microsoft Researcher- Silicon Valley in collaboration with Laura Caulfield*, Steve Swanson*, UCSD* 1 My Research Areas of Interest Flash characteristics

More information

Solid State Drive (SSD) Cache:

Solid State Drive (SSD) Cache: Solid State Drive (SSD) Cache: Enhancing Storage System Performance Application Notes Version: 1.2 Abstract: This application note introduces Storageflex HA3969 s Solid State Drive (SSD) Cache technology

More information

Moneta: A High-Performance Storage Architecture for Next-generation, Non-volatile Memories

Moneta: A High-Performance Storage Architecture for Next-generation, Non-volatile Memories Moneta: A High-Performance Storage Architecture for Next-generation, Non-volatile Memories Adrian M. Caulfield Arup De, Joel Coburn, Todor I. Mollov, Rajesh K. Gupta, Steven Swanson Non-Volatile Systems

More information

Open-Channel SSDs Offer the Flexibility Required by Hyperscale Infrastructure Matias Bjørling CNEX Labs

Open-Channel SSDs Offer the Flexibility Required by Hyperscale Infrastructure Matias Bjørling CNEX Labs Open-Channel SSDs Offer the Flexibility Required by Hyperscale Infrastructure Matias Bjørling CNEX Labs 1 Public and Private Cloud Providers 2 Workloads and Applications Multi-Tenancy Databases Instance

More information

Fusion iomemory PCIe Solutions from SanDisk and Sqrll make Accumulo Hypersonic

Fusion iomemory PCIe Solutions from SanDisk and Sqrll make Accumulo Hypersonic WHITE PAPER Fusion iomemory PCIe Solutions from SanDisk and Sqrll make Accumulo Hypersonic Western Digital Technologies, Inc. 951 SanDisk Drive, Milpitas, CA 95035 www.sandisk.com Table of Contents Executive

More information

CIT 668: System Architecture. Amazon Web Services

CIT 668: System Architecture. Amazon Web Services CIT 668: System Architecture Amazon Web Services Topics 1. AWS Global Infrastructure 2. Foundation Services 1. Compute 2. Storage 3. Database 4. Network 3. AWS Economics Amazon Services Architecture Regions

More information

ECE 598-MS: Advanced Memory and Storage Systems Lecture 7: Unified Address Translation with FlashMap

ECE 598-MS: Advanced Memory and Storage Systems Lecture 7: Unified Address Translation with FlashMap ECE 598-MS: Advanced Memory and Storage Systems Lecture 7: Unified Address Translation with Map Jian Huang Use As Non-Volatile Memory DRAM (nanoseconds) Application Memory Component SSD (microseconds)

More information

Purity: building fast, highly-available enterprise flash storage from commodity components

Purity: building fast, highly-available enterprise flash storage from commodity components Purity: building fast, highly-available enterprise flash storage from commodity components J. Colgrove, J. Davis, J. Hayes, E. Miller, C. Sandvig, R. Sears, A. Tamches, N. Vachharajani, and F. Wang 0 Gala

More information

Pocket: Elastic Ephemeral Storage for Serverless Analytics

Pocket: Elastic Ephemeral Storage for Serverless Analytics Pocket: Elastic Ephemeral Storage for Serverless Analytics Ana Klimovic*, Yawen Wang*, Patrick Stuedi +, Animesh Trivedi +, Jonas Pfefferle +, Christos Kozyrakis* *Stanford University, + IBM Research 1

More information

Cascade Mapping: Optimizing Memory Efficiency for Flash-based Key-value Caching

Cascade Mapping: Optimizing Memory Efficiency for Flash-based Key-value Caching Cascade Mapping: Optimizing Memory Efficiency for Flash-based Key-value Caching Kefei Wang and Feng Chen Louisiana State University SoCC '18 Carlsbad, CA Key-value Systems in Internet Services Key-value

More information

The Impact of SSD Selection on SQL Server Performance. Solution Brief. Understanding the differences in NVMe and SATA SSD throughput

The Impact of SSD Selection on SQL Server Performance. Solution Brief. Understanding the differences in NVMe and SATA SSD throughput Solution Brief The Impact of SSD Selection on SQL Server Performance Understanding the differences in NVMe and SATA SSD throughput 2018, Cloud Evolutions Data gathered by Cloud Evolutions. All product

More information

SFS: Random Write Considered Harmful in Solid State Drives

SFS: Random Write Considered Harmful in Solid State Drives SFS: Random Write Considered Harmful in Solid State Drives Changwoo Min 1, 2, Kangnyeon Kim 1, Hyunjin Cho 2, Sang-Won Lee 1, Young Ik Eom 1 1 Sungkyunkwan University, Korea 2 Samsung Electronics, Korea

More information

Designing SSDs for large scale cloud workloads FLASH MEMORY SUMMIT, AUG 2014

Designing SSDs for large scale cloud workloads FLASH MEMORY SUMMIT, AUG 2014 Designing SSDs for large scale cloud workloads FLASH MEMORY SUMMIT, AUG 2014 2 3 Cloud workloads are different! Examples: Read-mostly, write-once per day Sequential write streams for object stores Synchronous

More information

Flash Memory Based Storage System

Flash Memory Based Storage System Flash Memory Based Storage System References SmartSaver: Turning Flash Drive into a Disk Energy Saver for Mobile Computers, ISLPED 06 Energy-Aware Flash Memory Management in Virtual Memory System, islped

More information

pblk the OCSSD FTL Linux FAST Summit 18 Javier González Copyright 2018 CNEX Labs

pblk the OCSSD FTL Linux FAST Summit 18 Javier González Copyright 2018 CNEX Labs pblk the OCSSD FTL Linux FAST Summit 18 Javier González Read Latency Read Latency with 0% Writes Random Read 4K Percentiles 2 Read Latency Read Latency with 20% Writes Random Read 4K + Random Write 4K

More information

Cloud Optimized Performance: I/O-Intensive Workloads Using Flash-Based Storage

Cloud Optimized Performance: I/O-Intensive Workloads Using Flash-Based Storage Cloud Optimized Performance: I/O-Intensive Workloads Using Flash-Based Storage Version 1.0 Brocade continues to innovate by delivering the industry s first 16 Gbps switches for low latency and high transaction

More information

A Flash Scheduling Strategy for Current Capping in Multi-Power-Mode SSDs

A Flash Scheduling Strategy for Current Capping in Multi-Power-Mode SSDs A Flash Scheduling Strategy for Current Capping in Multi-Power-Mode SSDs Li-Pin Chang, Chia-Hsiang Cheng, and Kai-Hsiang Lin Department of Computer Science National Chiao-Tung University, Taiwan Presented

More information

[537] Flash. Tyler Harter

[537] Flash. Tyler Harter [537] Flash Tyler Harter Flash vs. Disk Disk Overview I/O requires: seek, rotate, transfer Inherently: - not parallel (only one head) - slow (mechanical) - poor random I/O (locality around disk head) Random

More information

Optimizing Flash-based Key-value Cache Systems

Optimizing Flash-based Key-value Cache Systems Optimizing Flash-based Key-value Cache Systems Zhaoyan Shen, Feng Chen, Yichen Jia, Zili Shao Department of Computing, Hong Kong Polytechnic University Computer Science & Engineering, Louisiana State University

More information

Gyu Sang Choi Yeungnam University

Gyu Sang Choi Yeungnam University Gyu Sang Choi Yeungnam University PRAM and NAND Flash Memory, and B+Tree in PRAM Outline NAND Flash Memory PRAM Hybrid Storage of PRAM and NAND Flash Memory PRAM Translation Layer (PTL) B+Tree in PRAM

More information

Toward SLO Complying SSDs Through OPS Isolation

Toward SLO Complying SSDs Through OPS Isolation Toward SLO Complying SSDs Through OPS Isolation October 23, 2015 Hongik University UNIST (Ulsan National Institute of Science & Technology) Sam H. Noh 1 Outline Part 1: FAST 2015 Part 2: Beyond FAST 2

More information

Decentralized Distributed Storage System for Big Data

Decentralized Distributed Storage System for Big Data Decentralized Distributed Storage System for Big Presenter: Wei Xie -Intensive Scalable Computing Laboratory(DISCL) Computer Science Department Texas Tech University Outline Trends in Big and Cloud Storage

More information

Maximizing Data Center and Enterprise Storage Efficiency

Maximizing Data Center and Enterprise Storage Efficiency Maximizing Data Center and Enterprise Storage Efficiency Enterprise and data center customers can leverage AutoStream to achieve higher application throughput and reduced latency, with negligible organizational

More information

Architectural Principles for Networked Solid State Storage Access

Architectural Principles for Networked Solid State Storage Access Architectural Principles for Networked Solid State Storage Access SNIA Legal Notice! The material contained in this tutorial is copyrighted by the SNIA unless otherwise noted.! Member companies and individual

More information

EXPLOITING INTRINSIC FLASH PROPERTIES TO ENHANCE MODERN STORAGE SYSTEMS. A Dissertation Presented to The Academic Faculty.

EXPLOITING INTRINSIC FLASH PROPERTIES TO ENHANCE MODERN STORAGE SYSTEMS. A Dissertation Presented to The Academic Faculty. EXPLOITING INTRINSIC FLASH PROPERTIES TO ENHANCE MODERN STORAGE SYSTEMS A Dissertation Presented to The Academic Faculty By Jian Huang In Partial Fulfillment of the Requirements for the Degree Doctor of

More information

WearDrive: Fast and Energy Efficient Storage for Wearables

WearDrive: Fast and Energy Efficient Storage for Wearables WearDrive: Fast and Energy Efficient Storage for Wearables Reza Shisheie Cleveland State University CIS 601 Wearable Computing: A New Era 2 Wearable Computing: A New Era Notifications Fitness/Healthcare

More information

Memory-Based Cloud Architectures

Memory-Based Cloud Architectures Memory-Based Cloud Architectures ( Or: Technical Challenges for OnDemand Business Software) Jan Schaffner Enterprise Platform and Integration Concepts Group Example: Enterprise Benchmarking -) *%'+,#$)

More information

Design Considerations for Using Flash Memory for Caching

Design Considerations for Using Flash Memory for Caching Design Considerations for Using Flash Memory for Caching Edi Shmueli, IBM XIV Storage Systems edi@il.ibm.com Santa Clara, CA August 2010 1 Solid-State Storage In a few decades solid-state storage will

More information

A Comparison of Capacity Management Schemes for Shared CMP Caches

A Comparison of Capacity Management Schemes for Shared CMP Caches A Comparison of Capacity Management Schemes for Shared CMP Caches Carole-Jean Wu and Margaret Martonosi Princeton University 7 th Annual WDDD 6/22/28 Motivation P P1 P1 Pn L1 L1 L1 L1 Last Level On-Chip

More information

Be Fast, Cheap and in Control with SwitchKV. Xiaozhou Li

Be Fast, Cheap and in Control with SwitchKV. Xiaozhou Li Be Fast, Cheap and in Control with SwitchKV Xiaozhou Li Goal: fast and cost-efficient key-value store Store, retrieve, manage key-value objects Get(key)/Put(key,value)/Delete(key) Target: cluster-level

More information

Partitioned Real-Time NAND Flash Storage. Katherine Missimer and Rich West

Partitioned Real-Time NAND Flash Storage. Katherine Missimer and Rich West Partitioned Real-Time NAND Flash Storage Katherine Missimer and Rich West Introduction Eric Risberg AP CircuitsToday 2 Introduction Eric Risberg AP CircuitsToday Analytics Vidhya 3 Chesky_W Mapping Ignorance

More information

SWAP: EFFECTIVE FINE-GRAIN MANAGEMENT

SWAP: EFFECTIVE FINE-GRAIN MANAGEMENT : EFFECTIVE FINE-GRAIN MANAGEMENT OF SHARED LAST-LEVEL CACHES WITH MINIMUM HARDWARE SUPPORT Xiaodong Wang, Shuang Chen, Jeff Setter, and José F. Martínez Computer Systems Lab Cornell University Page 1

More information

Presented by: Nafiseh Mahmoudi Spring 2017

Presented by: Nafiseh Mahmoudi Spring 2017 Presented by: Nafiseh Mahmoudi Spring 2017 Authors: Publication: Type: ACM Transactions on Storage (TOS), 2016 Research Paper 2 High speed data processing demands high storage I/O performance. Flash memory

More information

UCS Invicta: A New Generation of Storage Performance. Mazen Abou Najm DC Consulting Systems Engineer

UCS Invicta: A New Generation of Storage Performance. Mazen Abou Najm DC Consulting Systems Engineer UCS Invicta: A New Generation of Storage Performance Mazen Abou Najm DC Consulting Systems Engineer HDDs Aren t Designed For High Performance Disk 101 Can t spin faster (200 IOPS/Drive) Can t seek faster

More information

Anti-Caching: A New Approach to Database Management System Architecture. Guide: Helly Patel ( ) Dr. Sunnie Chung Kush Patel ( )

Anti-Caching: A New Approach to Database Management System Architecture. Guide: Helly Patel ( ) Dr. Sunnie Chung Kush Patel ( ) Anti-Caching: A New Approach to Database Management System Architecture Guide: Helly Patel (2655077) Dr. Sunnie Chung Kush Patel (2641883) Abstract Earlier DBMS blocks stored on disk, with a main memory

More information

BERLIN. 2015, Amazon Web Services, Inc. or its affiliates. All rights reserved

BERLIN. 2015, Amazon Web Services, Inc. or its affiliates. All rights reserved BERLIN 2015, Amazon Web Services, Inc. or its affiliates. All rights reserved Amazon Aurora: Amazon s New Relational Database Engine Carlos Conde Technology Evangelist @caarlco 2015, Amazon Web Services,

More information

Application-Specific Configuration Selection in the Cloud: Impact of Provider Policy and Potential of Systematic Testing

Application-Specific Configuration Selection in the Cloud: Impact of Provider Policy and Potential of Systematic Testing Application-Specific Configuration Selection in the Cloud: Impact of Provider Policy and Potential of Systematic Testing Mohammad Hajjat +, Ruiqi Liu*, Yiyang Chang +, T.S. Eugene Ng*, Sanjay Rao + + Purdue

More information

Cloud Computing with FPGA-based NVMe SSDs

Cloud Computing with FPGA-based NVMe SSDs Cloud Computing with FPGA-based NVMe SSDs Bharadwaj Pudipeddi, CTO NVXL Santa Clara, CA 1 Choice of NVMe Controllers ASIC NVMe: Fully off-loaded, consistent performance, M.2 or U.2 form factor ASIC OpenChannel:

More information

Database Workload. from additional misses in this already memory-intensive databases? interference could be a problem) Key question:

Database Workload. from additional misses in this already memory-intensive databases? interference could be a problem) Key question: Database Workload + Low throughput (0.8 IPC on an 8-wide superscalar. 1/4 of SPEC) + Naturally threaded (and widely used) application - Already high cache miss rates on a single-threaded machine (destructive

More information

NAND Flash-based Storage. Jin-Soo Kim Computer Systems Laboratory Sungkyunkwan University

NAND Flash-based Storage. Jin-Soo Kim Computer Systems Laboratory Sungkyunkwan University NAND Flash-based Storage Jin-Soo Kim (jinsookim@skku.edu) Computer Systems Laboratory Sungkyunkwan University http://csl.skku.edu Today s Topics NAND flash memory Flash Translation Layer (FTL) OS implications

More information

MQSim: A Framework for Enabling Realistic Studies of Modern Multi-Queue SSD Devices

MQSim: A Framework for Enabling Realistic Studies of Modern Multi-Queue SSD Devices MQSim: A Framework for Enabling Realistic Studies of Modern Multi-Queue SSD Devices Arash Tavakkol, Juan Gómez-Luna, Mohammad Sadrosadati, Saugata Ghose, Onur Mutlu February 13, 2018 Executive Summary

More information

Virtual Memory. Reading. Sections 5.4, 5.5, 5.6, 5.8, 5.10 (2) Lecture notes from MKP and S. Yalamanchili

Virtual Memory. Reading. Sections 5.4, 5.5, 5.6, 5.8, 5.10 (2) Lecture notes from MKP and S. Yalamanchili Virtual Memory Lecture notes from MKP and S. Yalamanchili Sections 5.4, 5.5, 5.6, 5.8, 5.10 Reading (2) 1 The Memory Hierarchy ALU registers Cache Memory Memory Memory Managed by the compiler Memory Managed

More information

Onyx: A Prototype Phase-Change Memory Storage Array

Onyx: A Prototype Phase-Change Memory Storage Array Onyx: A Prototype Phase-Change Memory Storage Array Ameen Akel * Adrian Caulfield, Todor Mollov, Rajesh Gupta, Steven Swanson Non-Volatile Systems Laboratory, Department of Computer Science and Engineering

More information

SOS : Software-based Out-of-Order Scheduling for High-Performance NAND Flash-Based SSDs

SOS : Software-based Out-of-Order Scheduling for High-Performance NAND Flash-Based SSDs SOS : Software-based Out-of-Order Scheduling for High-Performance NAND Flash-Based SSDs Sangwook Shane Hahn, Sungjin Lee and Jihong Kim Computer Architecture & Embedded Systems Laboratory School of Computer

More information

SSD Failures in Datacenters: What? When? And Why?

SSD Failures in Datacenters: What? When? And Why? SSD Failures in Datacenters: What? When? And Why? Iyswarya Narayanan, Di Wang, Myeongjae Jeon, Bikash Sharma, Laura Caulfield, Anand Sivasubramaniam, Ben Cutler, Jie Liu, Badriddine Khessib, Kushagra Vaid

More information

Seagate Enterprise SATA SSD with DuraWrite Technology Competitive Evaluation

Seagate Enterprise SATA SSD with DuraWrite Technology Competitive Evaluation August 2018 Seagate Enterprise SATA SSD with DuraWrite Technology Competitive Seagate Enterprise SATA SSDs with DuraWrite Technology have the best performance for compressible Database, Cloud, VDI Software

More information

KVFTL - Optimization of Storage Space Utilization for Key-Value-Specific Flash Storage Devices

KVFTL - Optimization of Storage Space Utilization for Key-Value-Specific Flash Storage Devices KVFTL - Optimization of Storage Space Utilization for Key-Value-Specific Flash Storage Devices ASP-DAC 217 Yen-Ting Chen, Ming-Chang Yang, Yuan-Hao Chang, Tseng-Yi Chen, Hsin-Wen Wei, and Wei-Kuan Shih

More information

STORAGE LATENCY x. RAMAC 350 (600 ms) NAND SSD (60 us)

STORAGE LATENCY x. RAMAC 350 (600 ms) NAND SSD (60 us) 1 STORAGE LATENCY 2 RAMAC 350 (600 ms) 1956 10 5 x NAND SSD (60 us) 2016 COMPUTE LATENCY 3 RAMAC 305 (100 Hz) 1956 10 8 x 1000x CORE I7 (1 GHZ) 2016 NON-VOLATILE MEMORY 1000x faster than NAND 3D XPOINT

More information

An Evaluation of Different Page Allocation Strategies on High-Speed SSDs

An Evaluation of Different Page Allocation Strategies on High-Speed SSDs An Evaluation of Different Page Allocation Strategies on High-Speed SSDs Myoungsoo Jung and Mahmut Kandemir Department of CSE, The Pennsylvania State University {mj,kandemir@cse.psu.edu} Abstract Exploiting

More information

Storage Systems : Disks and SSDs. Manu Awasthi CASS 2018

Storage Systems : Disks and SSDs. Manu Awasthi CASS 2018 Storage Systems : Disks and SSDs Manu Awasthi CASS 2018 Why study storage? Scalable High Performance Main Memory System Using Phase-Change Memory Technology, Qureshi et al, ISCA 2009 Trends Total amount

More information

Benefits of Multi-Node Scale-out Clusters running NetApp Clustered Data ONTAP. Silverton Consulting, Inc. StorInt Briefing

Benefits of Multi-Node Scale-out Clusters running NetApp Clustered Data ONTAP. Silverton Consulting, Inc. StorInt Briefing Benefits of Multi-Node Scale-out Clusters running NetApp Clustered Data ONTAP Silverton Consulting, Inc. StorInt Briefing BENEFITS OF MULTI- NODE SCALE- OUT CLUSTERS RUNNING NETAPP CDOT PAGE 2 OF 7 Introduction

More information

Preface. Fig. 1 Solid-State-Drive block diagram

Preface. Fig. 1 Solid-State-Drive block diagram Preface Solid-State-Drives (SSDs) gained a lot of popularity in the recent few years; compared to traditional HDDs, SSDs exhibit higher speed and reduced power, thus satisfying the tough needs of mobile

More information

NAND Flash-based Storage. Computer Systems Laboratory Sungkyunkwan University

NAND Flash-based Storage. Computer Systems Laboratory Sungkyunkwan University NAND Flash-based Storage Jin-Soo Kim (jinsookim@skku.edu) Computer Systems Laboratory Sungkyunkwan University http://csl.skku.edu Today s Topics NAND flash memory Flash Translation Layer (FTL) OS implications

More information

Getting Real: Lessons in Transitioning Research Simulations into Hardware Systems

Getting Real: Lessons in Transitioning Research Simulations into Hardware Systems Getting Real: Lessons in Transitioning Research Simulations into Hardware Systems Mohit Saxena, Yiying Zhang Michael Swift, Andrea Arpaci-Dusseau and Remzi Arpaci-Dusseau Flash Storage Stack Research SSD

More information

CA485 Ray Walshe Google File System

CA485 Ray Walshe Google File System Google File System Overview Google File System is scalable, distributed file system on inexpensive commodity hardware that provides: Fault Tolerance File system runs on hundreds or thousands of storage

More information

Part II: Data Center Software Architecture: Topic 2: Key-value Data Management Systems. SkimpyStash: Key Value Store on Flash-based Storage

Part II: Data Center Software Architecture: Topic 2: Key-value Data Management Systems. SkimpyStash: Key Value Store on Flash-based Storage ECE 7650 Scalable and Secure Internet Services and Architecture ---- A Systems Perspective Part II: Data Center Software Architecture: Topic 2: Key-value Data Management Systems SkimpyStash: Key Value

More information

Balancing DRAM Locality and Parallelism in Shared Memory CMP Systems

Balancing DRAM Locality and Parallelism in Shared Memory CMP Systems Balancing DRAM Locality and Parallelism in Shared Memory CMP Systems Min Kyu Jeong, Doe Hyun Yoon^, Dam Sunwoo*, Michael Sullivan, Ikhwan Lee, and Mattan Erez The University of Texas at Austin Hewlett-Packard

More information

Next-Generation Cloud Platform

Next-Generation Cloud Platform Next-Generation Cloud Platform Jangwoo Kim Jun 24, 2013 E-mail: jangwoo@postech.ac.kr High Performance Computing Lab Department of Computer Science & Engineering Pohang University of Science and Technology

More information

Data Organization and Processing

Data Organization and Processing Data Organization and Processing Indexing Techniques for Solid State Drives (NDBI007) David Hoksza http://siret.ms.mff.cuni.cz/hoksza Outline SSD technology overview Motivation for standard algorithms

More information

Analysis of SSD Health & Prediction of SSD Life

Analysis of SSD Health & Prediction of SSD Life Analysis of SSD Health & Prediction of SSD Life Dr. M. K. Jibbe Technical Director NetApp, Inc Bernard Chan Senior Engineer NetApp, Inc Agenda Problem Statement SMART Endurance Reporting Wear Life Prediction

More information

Loosely coupled: asynchronous processing, decoupling of tiers/components Fan-out the application tiers to support the workload Use cache for data and content Reduce number of requests if possible Batch

More information

NAND Flash-based Storage. Jin-Soo Kim Computer Systems Laboratory Sungkyunkwan University

NAND Flash-based Storage. Jin-Soo Kim Computer Systems Laboratory Sungkyunkwan University NAND Flash-based Storage Jin-Soo Kim (jinsookim@skku.edu) Computer Systems Laboratory Sungkyunkwan University http://csl.skku.edu Today s Topics NAND flash memory Flash Translation Layer (FTL) OS implications

More information

IaaS Vendor Comparison

IaaS Vendor Comparison IaaS Vendor Comparison Analysis of competitor products Tobias Deml Senior Systemberater BU Cloud & Core Technologies February 01, 2018 2 Tobias Deml Senior Systemberater BU Cloud & Core Technologies Topics

More information

IBM Tivoli Storage Manager for Windows Version Installation Guide IBM

IBM Tivoli Storage Manager for Windows Version Installation Guide IBM IBM Tivoli Storage Manager for Windows Version 7.1.8 Installation Guide IBM IBM Tivoli Storage Manager for Windows Version 7.1.8 Installation Guide IBM Note: Before you use this information and the product

More information

Onyx: A Protoype Phase Change Memory Storage Array

Onyx: A Protoype Phase Change Memory Storage Array Onyx: A Protoype Phase Change ory Storage Array Ameen Akel Adrian M. Caulfield Todor I. Mollov Rajesh K. Gupta Steven Swanson Computer Science and Engineering University of California, San Diego Abstract

More information

The Intersection of Cloud & Solid State Storage

The Intersection of Cloud & Solid State Storage The Intersection of Cloud & Solid State Storage Val Bercovici Cloud Czar, NetApp Office of the CTO SNIA Cloud Storage Initiative SNIA Solid State Storage Initiative Cloud Backdrop Worldwide IT spending

More information

Middleware and Flash Translation Layer Co-Design for the Performance Boost of Solid-State Drives

Middleware and Flash Translation Layer Co-Design for the Performance Boost of Solid-State Drives Middleware and Flash Translation Layer Co-Design for the Performance Boost of Solid-State Drives Chao Sun 1, Asuka Arakawa 1, Ayumi Soga 1, Chihiro Matsui 1 and Ken Takeuchi 1 1 Chuo University Santa Clara,

More information

A File-System-Aware FTL Design for Flash Memory Storage Systems

A File-System-Aware FTL Design for Flash Memory Storage Systems 1 A File-System-Aware FTL Design for Flash Memory Storage Systems Po-Liang Wu, Yuan-Hao Chang, Po-Chun Huang, and Tei-Wei Kuo National Taiwan University 2 Outline Introduction File Systems Observations

More information

The Oracle Database Appliance I/O and Performance Architecture

The Oracle Database Appliance I/O and Performance Architecture Simple Reliable Affordable The Oracle Database Appliance I/O and Performance Architecture Tammy Bednar, Sr. Principal Product Manager, ODA 1 Copyright 2012, Oracle and/or its affiliates. All rights reserved.

More information

Ambry: LinkedIn s Scalable Geo- Distributed Object Store

Ambry: LinkedIn s Scalable Geo- Distributed Object Store Ambry: LinkedIn s Scalable Geo- Distributed Object Store Shadi A. Noghabi *, Sriram Subramanian +, Priyesh Narayanan +, Sivabalan Narayanan +, Gopalakrishna Holla +, Mammad Zadeh +, Tianwei Li +, Indranil

More information

Get ready to be what s next.

Get ready to be what s next. Get ready to be what s next. Jared Shockley http://jaredontech.com Senior Service Engineer Prior Experience @jshoq Primary Experience Areas Agenda What is Microsoft Azure? Provider-hosted Apps Hosting

More information

TRASH DAY: COORDINATING GARBAGE COLLECTION IN DISTRIBUTED SYSTEMS

TRASH DAY: COORDINATING GARBAGE COLLECTION IN DISTRIBUTED SYSTEMS TRASH DAY: COORDINATING GARBAGE COLLECTION IN DISTRIBUTED SYSTEMS Martin Maas* Tim Harris KrsteAsanovic* John Kubiatowicz* *University of California, Berkeley Oracle Labs, Cambridge Why you should care

More information

Reducing Solid-State Storage Device Write Stress Through Opportunistic In-Place Delta Compression

Reducing Solid-State Storage Device Write Stress Through Opportunistic In-Place Delta Compression Reducing Solid-State Storage Device Write Stress Through Opportunistic In-Place Delta Compression Xuebin Zhang, Jiangpeng Li, Hao Wang, Kai Zhao and Tong Zhang xuebinzhang.rpi@gmail.com ECSE Department,

More information

Serverless Architecture Hochskalierbare Anwendungen ohne Server. Sascha Möllering, Solutions Architect

Serverless Architecture Hochskalierbare Anwendungen ohne Server. Sascha Möllering, Solutions Architect Serverless Architecture Hochskalierbare Anwendungen ohne Server Sascha Möllering, Solutions Architect Agenda Serverless Architecture AWS Lambda Amazon API Gateway Amazon DynamoDB Amazon S3 Serverless Framework

More information

Evaluation Report: Improving SQL Server Database Performance with Dot Hill AssuredSAN 4824 Flash Upgrades

Evaluation Report: Improving SQL Server Database Performance with Dot Hill AssuredSAN 4824 Flash Upgrades Evaluation Report: Improving SQL Server Database Performance with Dot Hill AssuredSAN 4824 Flash Upgrades Evaluation report prepared under contract with Dot Hill August 2015 Executive Summary Solid state

More information

Agenda. AWS Database Services Traditional vs AWS Data services model Amazon RDS Redshift DynamoDB ElastiCache

Agenda. AWS Database Services Traditional vs AWS Data services model Amazon RDS Redshift DynamoDB ElastiCache Databases on AWS 2017 Amazon Web Services, Inc. and its affiliates. All rights served. May not be copied, modified, or distributed in whole or in part without the express consent of Amazon Web Services,

More information

Deploy a High-Performance Database Solution: Cisco UCS B420 M4 Blade Server with Fusion iomemory PX600 Using Oracle Database 12c

Deploy a High-Performance Database Solution: Cisco UCS B420 M4 Blade Server with Fusion iomemory PX600 Using Oracle Database 12c White Paper Deploy a High-Performance Database Solution: Cisco UCS B420 M4 Blade Server with Fusion iomemory PX600 Using Oracle Database 12c What You Will Learn This document demonstrates the benefits

More information

Big and Fast. Anti-Caching in OLTP Systems. Justin DeBrabant

Big and Fast. Anti-Caching in OLTP Systems. Justin DeBrabant Big and Fast Anti-Caching in OLTP Systems Justin DeBrabant Online Transaction Processing transaction-oriented small footprint write-intensive 2 A bit of history 3 OLTP Through the Years relational model

More information

AWS: Basic Architecture Session SUNEY SHARMA Solutions Architect: AWS

AWS: Basic Architecture Session SUNEY SHARMA Solutions Architect: AWS AWS: Basic Architecture Session SUNEY SHARMA Solutions Architect: AWS suneys@amazon.com AWS Core Infrastructure and Services Traditional Infrastructure Amazon Web Services Security Security Firewalls ACLs

More information

A Case Study: Performance Evaluation of a DRAM-Based Solid State Disk

A Case Study: Performance Evaluation of a DRAM-Based Solid State Disk A Case Study: Performance Evaluation of a DRAM-Based Solid State Disk Hitoshi Oi The University of Aizu November 2, 2007 Japan-China Joint Workshop on Frontier of Computer Science and Technology (FCST)

More information

A Caching-Oriented FTL Design for Multi-Chipped Solid-State Disks. Yuan-Hao Chang, Wei-Lun Lu, Po-Chun Huang, Lue-Jane Lee, and Tei-Wei Kuo

A Caching-Oriented FTL Design for Multi-Chipped Solid-State Disks. Yuan-Hao Chang, Wei-Lun Lu, Po-Chun Huang, Lue-Jane Lee, and Tei-Wei Kuo A Caching-Oriented FTL Design for Multi-Chipped Solid-State Disks Yuan-Hao Chang, Wei-Lun Lu, Po-Chun Huang, Lue-Jane Lee, and Tei-Wei Kuo 1 June 4, 2011 2 Outline Introduction System Architecture A Multi-Chipped

More information

How to scale Windows Azure Application

How to scale Windows Azure Application Edwin Cheung Principal Program Manager China Cloud Innovation Centre Customer Advisory Team Microsoft Asia-Pacific Research and Development Group How to scale Windows Azure Application 4 Value Prop: (On-premise)

More information

Storage Systems : Disks and SSDs. Manu Awasthi July 6 th 2018 Computer Architecture Summer School 2018

Storage Systems : Disks and SSDs. Manu Awasthi July 6 th 2018 Computer Architecture Summer School 2018 Storage Systems : Disks and SSDs Manu Awasthi July 6 th 2018 Computer Architecture Summer School 2018 Why study storage? Scalable High Performance Main Memory System Using Phase-Change Memory Technology,

More information

HP ProLiant BladeSystem Gen9 vs Gen8 and G7 Server Blades on Data Warehouse Workloads

HP ProLiant BladeSystem Gen9 vs Gen8 and G7 Server Blades on Data Warehouse Workloads HP ProLiant BladeSystem Gen9 vs Gen8 and G7 Server Blades on Data Warehouse Workloads Gen9 server blades give more performance per dollar for your investment. Executive Summary Information Technology (IT)

More information

Request-Oriented Durable Write Caching for Application Performance appeared in USENIX ATC '15. Jinkyu Jeong Sungkyunkwan University

Request-Oriented Durable Write Caching for Application Performance appeared in USENIX ATC '15. Jinkyu Jeong Sungkyunkwan University Request-Oriented Durable Write Caching for Application Performance appeared in USENIX ATC '15 Jinkyu Jeong Sungkyunkwan University Introduction Volatile DRAM cache is ineffective for write Writes are dominant

More information

Accelerate Applications Using EqualLogic Arrays with directcache

Accelerate Applications Using EqualLogic Arrays with directcache Accelerate Applications Using EqualLogic Arrays with directcache Abstract This paper demonstrates how combining Fusion iomemory products with directcache software in host servers significantly improves

More information

Moneta: A High-performance Storage Array Architecture for Nextgeneration, Micro 2010

Moneta: A High-performance Storage Array Architecture for Nextgeneration, Micro 2010 Moneta: A High-performance Storage Array Architecture for Nextgeneration, Non-volatile Memories Micro 2010 NVM-based SSD NVMs are replacing spinning-disks Performance of disks has lagged NAND flash showed

More information

SSDs vs HDDs for DBMS by Glen Berseth York University, Toronto

SSDs vs HDDs for DBMS by Glen Berseth York University, Toronto SSDs vs HDDs for DBMS by Glen Berseth York University, Toronto So slow So cheap So heavy So fast So expensive So efficient NAND based flash memory Retains memory without power It works by trapping a small

More information

Extremely Fast Distributed Storage for Cloud Service Providers

Extremely Fast Distributed Storage for Cloud Service Providers Solution brief Intel Storage Builders StorPool Storage Intel SSD DC S3510 Series Intel Xeon Processor E3 and E5 Families Intel Ethernet Converged Network Adapter X710 Family Extremely Fast Distributed

More information

EECS750: Advanced Operating Systems. 2/24/2014 Heechul Yun

EECS750: Advanced Operating Systems. 2/24/2014 Heechul Yun EECS750: Advanced Operating Systems 2/24/2014 Heechul Yun 1 Administrative Project Feedback of your proposal will be sent by Wednesday Midterm report due on Apr. 2 3 pages: include intro, related work,

More information

Lecture 8: Virtual Memory. Today: DRAM innovations, virtual memory (Sections )

Lecture 8: Virtual Memory. Today: DRAM innovations, virtual memory (Sections ) Lecture 8: Virtual Memory Today: DRAM innovations, virtual memory (Sections 5.3-5.4) 1 DRAM Technology Trends Improvements in technology (smaller devices) DRAM capacities double every two years, but latency

More information

Accelerate Database Performance and Reduce Response Times in MongoDB Humongous Environments with the LSI Nytro MegaRAID Flash Accelerator Card

Accelerate Database Performance and Reduce Response Times in MongoDB Humongous Environments with the LSI Nytro MegaRAID Flash Accelerator Card Accelerate Database Performance and Reduce Response Times in MongoDB Humongous Environments with the LSI Nytro MegaRAID Flash Accelerator Card The Rise of MongoDB Summary One of today s growing database

More information

BzTree: A High-Performance Latch-free Range Index for Non-Volatile Memory

BzTree: A High-Performance Latch-free Range Index for Non-Volatile Memory BzTree: A High-Performance Latch-free Range Index for Non-Volatile Memory JOY ARULRAJ JUSTIN LEVANDOSKI UMAR FAROOQ MINHAS PER-AKE LARSON Microsoft Research NON-VOLATILE MEMORY [NVM] PERFORMANCE DRAM VOLATILE

More information

The Google File System

The Google File System The Google File System Sanjay Ghemawat, Howard Gobioff and Shun Tak Leung Google* Shivesh Kumar Sharma fl4164@wayne.edu Fall 2015 004395771 Overview Google file system is a scalable distributed file system

More information

Amazon Aurora Deep Dive

Amazon Aurora Deep Dive Amazon Aurora Deep Dive Anurag Gupta VP, Big Data Amazon Web Services April, 2016 Up Buffer Quorum 100K to Less Proactive 1/10 15 caches Custom, Shared 6-way Peer than read writes/second Automated Pay

More information

MicroFuge: A Middleware Approach to Providing Performance Isolation in Cloud Storage Systems

MicroFuge: A Middleware Approach to Providing Performance Isolation in Cloud Storage Systems 1 MicroFuge: A Middleware Approach to Providing Performance Isolation in Cloud Storage Systems Akshay Singh, Xu Cui, Benjamin Cassell, Bernard Wong and Khuzaima Daudjee July 3, 2014 2 Storage Resources

More information