Storage Technologies and Solutions

Transcription

1 Storage Research for Solving Big Data Problem David Hung-Chang Du Qwest Chair Professor Computer Science and Engineering University of Minnesota CRIS: NSF I/UCRC Center on Intelligent Storage More information on 5/28/2014 University of Minnesota Digital Technology Center Intelligent Storage Consortium 1

2 Outline of Talk Two Major Changes in Computing & Communication Environment Big Data Problem Solving Big Data Problem Software Defined Network vs. Software Defined Storage Storage Research Projects at NSF I/UCRC Center on Intelligent Storage Conclusions 2

3 Instrument and Connect the World! Bridge Monitoring Building Environment Controls Earthquake Monitoring Elder Care Factories Fire Response First Responders Forest Management Soil Monitoring Supply Chain Wind Response and more more 5/28/2014 3

4 Sensors Everywhere Credit: Arthur Sanderson at RPI Hudson River Valley Sonoma Redwood Forest Kindly donated by Stewart Johnston smart buildings smart bridges Credit: MO Dept. of Transportation 4 OOPSLA Jeannette M. Wing 4

5 Digital Explosion: Data Centric The digital universe will grow over six-fold, from 281 exabytes in 2007 to 1,773 exabytes in 2011 > 90% of the information in the digital universe is unstructured and absolute # of files growing faster than the TBs ----from IDC Survey presented in ISW 2008

6 Big Data Problem Converting Analog to Digital All Data Access Traces in Digital World How to Gain Information from All Stored Data? How to Make Better Decisions? What to Keep and What to Preserve? Can We Develop Knowledge from All These Data? 6

7 Intelligent Storage Knowledge Information Objects Need New Architectures & Systems to Capture [ INTELLIGENCE ] Exploited to store and retrieve data more efficiently with Indexing/Search capability Extended attributes augmented view high level semantics associated. Files Blocks Traditional storage device view - raw bits, no associated semantics. 5/28/2014 7

8 Current Cyber Space A domain characterized by the use of electronics and the electromagnetic spectrum to store, modify, and exchange data via networked systems and associated physical infrastructure. 28 May

9 Inside the Net: A Different Story Closed equipment Software bundled with hardware Vendor-specific interfaces Over specified Slow protocol standardization Few people can innovate Equipment vendors write the code Long delays to introduce new features 9

10 Do We Need Innovation Inside? Many boxes (routers, switches, firewalls, ) with different interfaces and not programmable. 10

11 Proposed SDN Solution Standard API to Enable Programmable Logically Centralized Controller Control Plane Separation of Control Plane and Data Plane Open API Data Plane 11

12 Seamless Mobility See host sending traffic at new location Modify rules to reroute the traffic 12 12

13 Server Load Balancing Pre-install load-balancing policy Split traffic based on source IP src=0*, dst= src=1*, dst=

14 Example SDN Applications Seamless mobility and migration Server load balancing Dynamic access control Using multiple wireless access points Energy-efficient networking Adaptive traffic monitoring Denial-of-Service attack detection Network virtualization See

15 Network Function Virtualization (NFV) Slide from: 15

16 Use Case: vwoc (virtualized WAN Optimization Controller) 16

17 What is SDS? 1. Policy-Driven Storage (IOPS, latency, reliability, Fault tolerance, Provisioning, QoS) 2. Scale-out Architecture 3. Storage as a Seamless Pool of Resource (Storage Virtualization) 4. Control Integration from Multi-Vendors 5. Heterogeneous Storage Containers 6. Logical Centralized Resource Allocation

18 Transactional Analytics Web J2EE/OLTP Map/Reduce Pattern Web 2.0 Pattern Public Cloud Private Cloud Hybrid Cloud Bare Metal Cloud Software Defined Storage Plan Deploy Optimize Data storage and retrieval services Security & Compliance Encryption Archival/WORM Portable storage software on commodity hdwr Legacy high-function (external) storage systems Availability Clustering Replication Capacity/Performance Storage Class De-duplication/Compression/Thin Provisioning Slide from One Vendor 18

19 Storage Services Layer Service Abstractions Security and Availability Putting Things Together Authentication/Auditing Encryption Mirroring/DR Platinum High Availability Backup & Recovery Performance and Opt. Striping Clustering Compression Gold Silver Bronze Deduplication Tiering/ILM SOFTWARE DEFINED COMPUTE RESILIENCY SOFTWARE DEFINED STORAGE HETEROGENEITY CAPABILITY OPTIMIZATION FABRIC MANAGEMENT SOFTWARE DEFINED NETWORK Storage replication Disaster recovery Consistency groups Backup Storage Abstraction Storage Provisioning Storage Monitoring SAN/GPFS/NAS/DAS Storage tiers Performance aware placement Continous optimizations Migration FC/FCoE/iSCSI/ Infiniband Zone management Workload 19 Abstraction Resource Abstraction ` Mapping to Resource Continuous Optimization

20 SDN vs. SDS Consensus on Definition OpenFlow Switches as De Facto Devices Wide Area Networks Benefit Big Network Users IP Network Focus Support Applications No Clear Definition Yet Heterogeneous Types of Storage Containers Data Center Deployment Ensure QoS & Efficiency Virtual Machine Focus Integration with SDN and Compute

21 CRIS Research Summary

22 Current Sponsor Companies Two Memberships One Membership 22

23 Current Research Thrusts Research on New Storage Technologies (Flash Memory based SSD, PCM, Shingled Write Disks: (Seagate, LSI, SGI and Western Digital (HGST)) Research on New Storage Hierarchies (multi-level caching/prefetching, data allocation/migration, and tiered storage: (HP, NetApp and Dell) Cloud Storage and Big Data (HP, NetApp, FedCentric and NEC-Labs) I/O Workload Characterization and Synthetic Workload Generation (Seagate, Xyratex and NetApp) 23

24 New Storage Technologies Flash Memory based SSD FTL Design PCM Prototype Shingled Write Disk Design and Layout 24

25 Challenges in New Technologies Investigating and Understanding Fundamental Properties Research of Design Issues What are their impacts on applications? How to effectively integrate the new technologies into existing memory/storage hierarchies? 25

26 Summary of SSD Research Results Robust and Reliable Design of SSDs Integrating SSDs into Storage Hierarchy New FTL Design: A Convertible FTL Design Efficient Wear-Leveling Algorithm Optimal/Efficient Read/Write Caching Hot and Cold Data Classification Bloom Filter Design and Key-Value Store Based on Flash Memory Using Sampling Technique for Meta-Data Management in FTL 5/28/

27 PCM Prototyping Effort 27

28 Non-Volatile Memory NVM Replaces DRAM as Main Memory NVM to Be Used As A Cache DRAM+NVM CPU CPU CPU Main Memory NVM Main Memory NVM Main Memory DRAM NVM Storage HDD Storage SSD Storage SSD 28

29 New Memory and Storage Hierarchies Data Storage Data Migration Multi-Level Caching Data Prefetching Tiered Storage 29

30 30

31 Possible Methods In-place Update : many small bands Protect previously-written data by Read-Modify-Write Behaves similar to regular disks Out-of-place Update : few large band Maintain data in circular log structure Data Addition to head pointer Data removal from tail pointer LBA-to-PBA mapping is not fixed Transfer random writes into sequential write Compromise sequential read performance Higher Space overhead Write Amplification Indirected Addressing Defragmentation (Garbage Collection) 31

32 Current Research Focuses on New Storage Technologies How to build large scale storage systems with SSD or SWD? Modeling multi-channel multi-chip SSD Investigating SSD reliability and performance with a wide set of metrics Investigating the impact of non-volatile memory as main memory Revisit FTL design issues for SSD when SSDs are composed of a large storage system instead of caching devices 32

33 Storage Layer Management and Caching Read Queues (RT) Read Queues (Prefetch) Write Queues (Offloading) When/ Where/how much SSD Big Memory with PCM off off On Cloud Storage SATA Disks 33

34 Local Storage + Cloud Storage Why? How? Where? 34

35 NAND Flash Package with Integrated ECC and General Purpose Processor Manufacturers incorporated hardware in flash package DDR SSD Controller Data buffer NAND Flash Package NAND Flash Die Processor ECC NAND Flash Die NAND Flash Package NAND Flash Die Processor ECC NAND Flash Die Host CPU PCIe Host communication Block Management Wear Leveling Garbage Collection DDR NAND Flash Package NAND Flash Die Processor ECC NAND Flash Die NAND Flash Package NAND Flash Die Processor ECC NAND Flash Die 35

36 Accelerating Hadoop on SGI UV2000(In- Memory System) Hadoop & MapReduce Are for Data Intensive Applications How to Speed Up in High Performance Based Computers 36

37 Research Focuses of Cloud Storage + Big Data Emphasize more on Virtual Machine environment Ensure QoS support for VMs in Cloud (VDI as An Application) How data deduplication can be applied in cloud + big data (more on primary storage dedupe)? Integration of cloud and local storage Integration of various file systems with federated file system 37

38 Framework of I/O Workload Characterization Original trace Comparison 2 Replayed trace Workload characterization Arrival pattern, File/Data access pattern in the form of parameters Replay on same/different storage system Workload Parameters Parameter adjustment Comparison 1 Changes to applications and /or system ( either host or storage) Replay by workload replayer Comparison 3 Adjusted Parameters Workload generation Synthetic trace Action Output 38

39 Recent Accomplishments Completed a tool for I/O workload characterization and generation for parallel file systems Hfplayer v.2 (replay engine) is now available Proposed a new cache replacement scheme for non-volatile memory as main memory and disk as storage device A detailed design of integrating cloud storage with local storage Proposed a journaling based scheme for SSD reliability 39

40 Research Focuses on I/O Workload Characterization and Generation Further Integration with block I/O, parallel file system I/O and replay engine How to improve the performance of storage systems? I/O workload phase detection How to apply knowledge in I/O workload to multi-level caching? 40

41 Conclusions Storage Research Face Challenges from Applications (Big Data, Long-Term Data Preservation, Cloud Storage, Scalability) Also Face Challenges from New Technologies (Emerging Memory/Storage Hierarchies) Integrated Approach Including Compute, Storage and Network Systems Consideration Is A Must (SDS???) 41

42 Thank You! Questions? 42 42