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Exploiting Data Deduplication to Accelerate Live Virtual Machine Migration

This study explores leveraging data deduplication to enhance live virtual machine migration for improved performance and reduced downtime. The research implements a novel approach and evaluates its impact on migration metrics, showcasing significant enhancements in data transfer efficiency and migration time.

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Exploiting Data Deduplication to Accelerate Live Virtual Machine Migration

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  1. Exploiting Data Deduplication to Accelerate Live Virtual Machine Migration Xiang Zhang 1,2, Zhigang Huo 1, Jie Ma 1, Dan Meng 1 1. National Research Center for Intelligent Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences 2. Graduate University of Chinese Academy of Sciences Email: zhangxiang@ncic.ac.cn

  2. Outline • Introduction • Design • Implementation • Evaluation • Conclusion & Future Work

  3. Live Migration • Definition • Migrating OS and Apps as a whole to another physical machine without rebooting the VM • Advantages • Load Balance • Services Consolidation • Fault Tolerance • ... • Usually a shared storage is deployed • Migrating VCPU context and memory image

  4. Pre-Copy • Pre-Copy is the default choice in Xen • First phase, initial memory pages are copied • Second phase, several rounds of incremental synchronization are employed • Last phase, VM is suspended, remaining memory image and VCPU context are copied • Pre-Copy is reliable

  5. Motivation of Research • Performance metrics of migration • Total Data Transferred • Total Migration Time • Downtime • Necessity for improving performance of Migration • Apps suffer less time of performance degradation • Would not miss many migration opportunities • Shorter downtime for latency-sensitive Apps

  6. Outline • Introduction • Design • Implementation • Evaluation • Conclusion & Future Work

  7. Analyzing Migration Data Regularities • During the first phase • Zero pages are in the majority for lightweight workloads • At least 25% of non-zero pages are identical or above 80% similar • Ratios of identical and similar pages to reference pages are 8:1 at least • During last two phases • Little zero pages • At least 50% of pages are above 80% similar to their old versions • Conclusion • Too many redundant data transferred during migration. • Migration with Data Deduplication (MDD)

  8. How to Find Identical and Similar Pages (1) • HashSimilarityDetector(k, s, c) [21] • Hashes (k * s) blocks on the page, and groups them into k groups of s hashes each • For each hash fingerprint, c candidates are stored as reference pages • HashSimilarityDetector(2, 1, 1), SuperFastHash of 64-byte blocks

  9. How to Find Identical and Similar Pages (2) • Similarity is transitive • Ptrans≈ Pold, Phash≈ Ptrans, so Phash≈ Pold • Need not to cache all the transferred pages • Only the privileged domain in source needs to maintain hash table • Reference pages are transferred and can be found by their frame numbers in destination

  10. How to Find Identical and Similar Pages (3) • Only indexing by hash fingerprints may cause data inconsistency Source Destination FPHash b1 b2 Px b1 Px-old Px-old Px-new b2 b1 Py b1 Px-new b2

  11. How to Find Identical and Similar Pages (4) • Double-Hash to eliminate data inconsistency Source Destination FNHash Px b2 b1 b2 Px-new b1 Px-old Px-old b1 Px-new b2 FPHash Py b1

  12. Data Deduplication during Migration • In source • Pparity= Ptrans ⊕ Pref • Encoding Pparity with RLE, then migrating • In destination • Decoding to get Pparity • Ptrans = Pparity ⊕ Pref • Advantages • Pparity contains less information than Ptrans • Reflects the exact different data at bit level • Contains many blocks of continuous zeros, even RLE can compress effectively • RLE is one of the fastest encoding algorithm

  13. Outline • Introduction • Design • Implementation • Evaluation • Conclusion & Future Work

  14. Implementation • Do data deduplication parallelly by multi-thread • Hash tables are maintained by LRU • Extended memcmp() to reduce the overhead of judging zero pages

  15. Outline • Introduction • Design • Implementation • Evaluation • Conclusion & Future Work

  16. Experimental Setup • Experiment platform • Cluster composed by six identical servers • One storage server, iSCSI protocol, isolated gigabit Ethernet • Two servers, which act as the source and destination of migration • Three servers work as clients for workloads • Server configuration • Two Intel Xeon E5520 quad-core CPUs, 2.2GHz • 8GB DDR RAM • Gigabit LAN • Xen-3.3.1 and modified Linux-2.6.18.8 • Migrated VM is configured with one VCPU and 1GB RAM • Migration shares the same network with workloads. • Workloads • Compilation, VOD, static web server, dynamic web server

  17. Total Data Transferred • Transferred data is reduced by 56.60% on average • Number of transferred pages is reduced by 48.73% on average (Banking) • Compression ratio is 49.27% on average (Banking)

  18. Total Migration Time and Downtime • MDD decreases total migration time and downtime by 34.93% and 26.16% on average • Less data transferred • Number of migration rounds are not reduced

  19. CPU Resource Required • Extra CPU resource which MDD requires is 47.21% of a CPU Average CPU Utilization Ratio of Migration (%)

  20. Influence to Apps • Run Apache in migrated VM, and migrate it in normal and adaptive mode respectively • The more limited network bandwidth is, the more essential data deduplication is Benefits of MDD in Different Migration Mode (%)

  21. Outline • Introduction • Design • Implementation • Evaluation • Conclusions & Future Work

  22. Conclusion & Future Work • Conclusion • Study the characteristics of run-time memory image data during migration • Present the design and implementation of MDD • MDD reduces total data transferred, total migration time and downtime by 56.60%, 34.93% and 26.16% respectively, reduces the influence of migration to Apps. • Future work • Extend MDD into live whole-system migration in wide-area environment

  23. Thank You! Any Questions?

  24. Related Work • Reducing transferred data • Post-Copy [7][12] • Self-Ballooning [7] • Trace and replay [13] • Adaptive compression [8] • Improving network bandwidth • InfiniBand RDMA [14]

  25. Backup Ecommerce Support

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