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VDN: Virtual Machine Image Distribution Network for Cloud Data Centers

Chunyi Peng 1 , Minkyong Kim 2 , Zhe Zhang 2 , Hui Lei 2 1 University of California, Los Angeles 2 IBM T.J. Watson Research Center. VDN: Virtual Machine Image Distribution Network for Cloud Data Centers. IEEE INFOCOM 2012 Orlando, Florida USA. Cloud Computing.

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VDN: Virtual Machine Image Distribution Network for Cloud Data Centers

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  1. Chunyi Peng1, Minkyong Kim2, Zhe Zhang2, Hui Lei2 1University of California, Los Angeles 2IBM T.J. Watson Research Center VDN: Virtual Machine Image Distribution Network for Cloud Data Centers IEEE INFOCOM 2012 Orlando, Florida USA

  2. Cloud Computing the delivery of Computing as a Service C Peng (UCLA)

  3. Service Access in Virtual Machine Instances Cloud Clients Web browser, mobile app, thin client, terminal emulator, … Client Service Requests (e.g. HTTP) Application Software as a Service (SaaS) CRM, Email, virtual desktop, communications, games, … VM VM VM VM VM Platform as a Service (PaaS) Execution runtime, database, web server, development tools, … Platform Infrastructure as a Service (IaaS) Virtual machines, server storage, load balancer, networks, … Infra structure Problem: On-demand VM provisioning Picture source: http://www.wikimedia.org C Peng (UCLA)

  4. Time for VM Image Provisioning User request Req process VM Bootup VM image transfer time Our focus: Transfer time Response in several or tens of minutes in reality! C Peng (UCLA)

  5. Core Aggregation Access ToRswitch Why Slow? • VM image files are large (several or tens of GB) • Centralized image storage becomes a bottleneck Data Center Image-server RH5.6 RH5.6 C Peng (UCLA)

  6. Roadmap • Basic VDN idea: enable collaborative sharing • VDN solution on efficient sharing • Basic sharing units • Metadata management • Performance evaluation • Conclusion C Peng (UCLA)

  7. Core Aggregation Access ToRswitch VDN: Speedup VM Image Distribution • Enable collaborative sharing • Utilize the “free” VM images • Exploit source diversity and make full use of network bandwidth Image-server RH5.6 C Peng (UCLA) RH5.6 RH5.5 RH5.6 RH6.0 RH5.6

  8. How to Enable Collaborative Sharing? • What is the basic data unit for sharing? • File-based sharing: Allow sharing only among same files • Chunk-based sharing: Allow sharing of common chunks from different files • How to manage content location information? • Centralized solution: directory service, etc. • Distributed solution: P2P overlay, etc. C Peng (UCLA)

  9. What is the Appropriate Sharing Unit? • Two factors • The number of the same, alive VM image instances • The similarity of different VM images • Conduct real trace analysis and cross-image similarity measurement • VM traces from six operational data centers for 4 months • VM images including different Linux/Windows versions, IBM services (DB2, Rational, WebSphere) etc C Peng (UCLA)

  10. VM Instance Popularity • The distribution of image popularity is highly skewed • A few popular images take a large portion of VM instances • Many unpopular images have a small number of VM instances (< 5) • Few peers can involve in file-based sharing Unpopular VM images C Peng (UCLA)

  11. VM Instance Lifetime • The lifetime of VM instance varies • 40% instances (more popular VM instances) < 13 minutes • The unpopular VM images have longer lifetime • VM image distribution network should cope with various lifetime instances 13 min C Peng (UCLA)

  12. VM Image Structure • Tree-based VM image structure Enterprise Linux v5.5 (32bit) (26.6%) Enterprise Linux v5.5 (64bit) (18.7%) … Enterprise Linux v5.4 (32bit) (4%) … Enterprise Linux v5.6 (32bit) (0.2%) Red Hat (53%) Linux (60%) SUSE …… (7%) …… Windows (25%) Database …… IDE …… …… …… V7.0 B (0.7%) V7.0.0.11 S P (0.7%) V7.0.0.11 R B (0.3%) V7.0.0.11 S B (0.3%) V7.0.0.11 S D (0.2%) V7.0 P (0.1%) Services (11%) Web app. server …… Misc (4%) C Peng (UCLA)

  13. VM Image Similarity • High similarity across VM images • Chunk schemes: fixed size and Rabin fingerprinting • Similarity: Sim(A,B) = |A’s chunks that appear in B| /|A| • Chunk-based sharing can exploit cross-image similarity C Peng (UCLA)

  14. Enable Chunk-based Sharing • Decouple VM images into VM chunks • Exploit similarity across VM images • Provide a higher source diversity and sharing opportunity RH5.6 RH5.5 RH5.6 RH6.0 RH5.6 RH5.6 Questions: How to maintain chunk location information (metadata) How to be scalable and also enable fast data transmission C Peng (UCLA)

  15. Internet How to Manage Location Information? • Solution I: centralized metadata server • Cons: be simple • Pros: bottleneck at metadata server • Solution II: P2P overlay network, e.g., DHT • Cons: distributed operations • Pros: be unaware of data center topology and may introduce high network overhead I-S C Peng (UCLA)

  16. Issues in Conventional P2P Practice One logic operation (lookup/publish) Multiple physical hops Hop costs (e.g. time) can be high! Solution: Reduce # of hops Reduce the cost of physical hops Keep it local or with close buddies C Peng (UCLA)

  17. Internet Topology-aware Metadata Management • Divide all the hosts into different-level hierarchies and manage chunks in each hierarchy • Utilize static/quasi-static (controlled) topology • Exploit high bandwidth local links in hierarchical structure I-S L3 L2 L2 L1 L1 L1 H H H C Peng (UCLA)

  18. VDN: Encourage Local Communication • Local chunk metadata storage • Index nodes maintain only metadata within this hierarchy • Unnecessary to maintain a global view at all index nodes • Local chunk metadata operation (e.g., lookup/publish) • Ask close index nodes first • Lower operation overhead • Local chunk data delivery • Enable high bandwidth transmission between close hosts (e.g. within the rack) C Peng (UCLA)

  19. VDN Operation Flows • Recursive operation from lower-hierarchy to higher-hierarchy L3 A. Metadata update B. Metadata lookup C. Data transmission Image-server 3C. L2 L2 3B. 1. L1 L1 L1 L1 2. 3A. 4A 4B Local Cache 5 C Peng (UCLA)

  20. Performance Evaluation • Setting • One-month real trace driven simulation • VM image: 128MB~ 8GB • Tree topology: 4x 4 x 8 (128 nodes) • Network bandwidth: • Static throughput for one physical link • Queue-based simulation for multiple transmissions on one link • Schemes • Baseline: centralized operation • Local: fetch VM chunks from local host if possible • VDN: enable collaborative sharing (4-) (8-nodes) 200Mbps I-S 500Mbps disk I/O: 1Gbps Net BW: 1Gbps 2Gbps C Peng (UCLA)

  21. Great Speedup on Image Distribution S1 data center S6 data center at S6, VM image size = 4GB C Peng (UCLA)

  22. Scalable to Heavy Traffic Loads • Adjust time-of-arrival using factor 1-60 S6, Median S6, 90th C Peng (UCLA)

  23. Low Metadata Management Overhead • Compare with three metadata management schemes • Naïve: on-demand topology-aware broadcast • Flat: manage metadata in a ring (e.g. DHT, P2P) • Topo: topology-aware design (VDN) • Assume the communication cost is 1:4:10 (reverse to bandwidth) (a) Number of messages (b) Communication cost C Peng (UCLA)

  24. Conclusion • VDN is a network-aware P2P paradigm for VM image distribution • Reduce image provisioning time • Achieve the reasonable overhead • Chunk-based sharing exploit inherent cross-image similarity • Network-aware operations can optimize the performance in the context of data centers C Peng (UCLA)

  25. THANKs C Peng (UCLA)

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