design and validation of cloud storage systems using maude
play

Design and Validation of Cloud Storage Systems using Maude Peter - PowerPoint PPT Presentation

Jan 21, 2023 •231 likes •1.29k views

Design and Validation of Cloud Storage Systems using Maude Peter Csaba Olveczky University of Oslo University of Illinois at Urbana-Champaign Based on joint work with Jon Grov and members of UIUCs Center for Assured Cloud Computing

  1. Maude: Software Engineering Perspective (cont.) What about performance analysis? (Randomized) simulations 1 Probabilistic analysis (using PVeStA) 2 ◮ statistical model checking Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 19 / 58

  2. Maude: Software Engineering Perspective (cont.) Same artifact for: precise system description rapid prototyping extensive testing correctness analysis performance estimation Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 20 / 58

  3. Case Study I Modeling, Analyzing, and Extending Megastore Joint work with Jon Grov (U. Oslo) Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 21 / 58

  4. Megastore Megastore: Google’s wide-area replicated data store 3 billion write and 20 billion read transactions daily (2011) Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 22 / 58

  5. Megastore: Key Ideas (I) (Figure from http://cse708.blogspot.jp/2011/03/megastore-providing-scalable-highly.html ) Data divided into entity groups Peter’s email Books on rewriting logic Narciso’s documents Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 23 / 58

  6. Megastore: Key Ideas (II) Consistency for transactions accessing a single entity group ◮ no guarantee if transaction reads multiple entity groups Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 24 / 58

  7. Our Work [Developed and] formalized [our version of the] Megastore [approach] in Maude Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 25 / 58

  8. Our Work [Developed and] formalized [our version of the] Megastore [approach] in Maude ◮ first (public) formalization/detailed description of Megastore Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 25 / 58

  9. Our Work [Developed and] formalized [our version of the] Megastore [approach] in Maude ◮ first (public) formalization/detailed description of Megastore 56 rewrite rules (37 for fault tolerance features) Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 25 / 58

  10. Performance Estimation Key performance measures: ◮ average transaction latency ◮ number of committed/aborted transactions Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 26 / 58

  11. Performance Estimation Key performance measures: ◮ average transaction latency ◮ number of committed/aborted transactions Randomly generated transactions (rate 2.5 TPS) 30% 30% 30% 10% Madrid ↔ Paris 10 15 20 50 Network delays: Madrid ↔ New York 30 35 40 100 Paris ↔ New York 30 35 40 100 Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 26 / 58

  12. Performance Estimation Key performance measures: ◮ average transaction latency ◮ number of committed/aborted transactions Randomly generated transactions (rate 2.5 TPS) 30% 30% 30% 10% Madrid ↔ Paris 10 15 20 50 Network delays: Madrid ↔ New York 30 35 40 100 Paris ↔ New York 30 35 40 100 Simulating for 200 seconds: Avg. latency (ms) Commits Aborts Madrid 218 109 38 New York 336 129 16 Paris 331 116 21 Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 26 / 58

  13. Megastore-CGC: extending Megastore Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 27 / 58

  14. Motivation Some transactions must access multiple entity groups Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 28 / 58

  15. Motivation Some transactions must access multiple entity groups Our work: extend Megastore with consistency for transactions accessing multiple entity groups Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 28 / 58

  16. Motivation Some transactions must access multiple entity groups Our work: extend Megastore with consistency for transactions accessing multiple entity groups Megastore-CGC piggybacks ordering and validation onto Megastore’s coordination protocol ◮ no additional messages for validation/commit! ◮ maintains Megastore’s performance and fault tolerance Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 28 / 58

  17. Performance Comparison using Real-Time Maude Simulating for 1000 seconds (no failures) Megastore: Commits Aborts Avg. latency (ms) Madrid 652 152 126 Paris 704 100 118 New York 640 172 151 Megastore-CGC: Commits Aborts Val. aborts Avg.latency (ms) Madrid 660 144 0 123 Paris 674 115 15 118 New York 631 171 10 150 Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 29 / 58

  18. Model Checking Megastore-CGC Model checking scenarios 5 transactions , no failures, message delay 30 ms or 80 ms − → 108,279 reachable states, 124 seconds 3 transactions, one site failure and fixed message delay − → 1,874,946 reachable states, 6,311 seconds 3 transactions, fixed message delay and one message failure − → 265,410 reachable states, 858 seconds Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 30 / 58

  19. Case Study II Work by Si Liu, Muntasir Raihan Rahman, Stephen Skeirik, Indranil Gupta, Jos´ e Meseguer, Son Nguyen, Jatin Ganhotra (ICFEM’14, QEST’15) Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 31 / 58

  20. Apache Cassandra Key-value data store originally developed at Facebook Used by Amadeus, Apple, CERN, IBM, Netflix, Facebook/Instagram, Twitter, . . . Open source Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 32 / 58

  21. Cassandra Overview Read consistency either one, quorum, or all Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 33 / 58

  22. Cassandra Overview Read consistency either one, quorum, or all Write consistency either zero, one, quorum, or all [Figures from http://www.slideshare.net/nuboat/cassandra-distributed-data-store ] Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 33 / 58

  23. Motivation Formal model from 345K LOC 1 ◮ allows experimenting with different optimizations/variations Analyze basic property: eventual consistency 2 When/how often does Cassandra give stronger guarantees? 3 ◮ strong consistency ◮ read-your-writes Performance evaluation: 4 ◮ compare PVeStA analyses with real implementations Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 34 / 58

  24. Formal Analysis with Multiple Clients Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 35 / 58

  25. Performance Estimation Formal model + PVeStA vs. actual implementation Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 36 / 58

  26. P-Store P-Store [N. Schiper, P. Sutra, and F. Pedone; IEEE SRDS’10] Replicated and partitioned data store Serializability Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 37 / 58

  27. P-Store P-Store [N. Schiper, P. Sutra, and F. Pedone; IEEE SRDS’10] Replicated and partitioned data store Serializability Atomic multicast orders concurrent transactions Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 37 / 58

  28. P-Store P-Store [N. Schiper, P. Sutra, and F. Pedone; IEEE SRDS’10] Replicated and partitioned data store Serializability Atomic multicast orders concurrent transactions Group commitment for atomic commit Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 37 / 58

  29. Atomic Multicast Definition Atomic Multicast: Consistent reception order of messages (a): any pair of nodes receive the same atomic-multicast messages in the same order (b): induced “global read order” must be acyclic Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 38 / 58

  30. Atomic Multicast Definition Atomic Multicast: Consistent reception order of messages (a): any pair of nodes receive the same atomic-multicast messages in the same order (b): induced “global read order” must be acyclic Example A reads m 1 < m 2 B reads m 2 < m 3 C reads m 3 < m 1 satisfies (a) but not (b) Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 38 / 58

  31. Atomic Multicast in Maude (I) Fundamental problem in distributed systems Impose order on conflicting concurrent transactions Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 39 / 58

  32. Atomic Multicast in Maude (I) Fundamental problem in distributed systems Impose order on conflicting concurrent transactions Many algorithms for atomic multicast Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 39 / 58

  33. Atomic Multicast in Maude (I) Fundamental problem in distributed systems Impose order on conflicting concurrent transactions Many algorithms for atomic multicast Define generic atomic multicast primitive in Maude ◮ abstract ◮ covers all possible receiving orders Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 39 / 58

  34. Atomic Multicast in Maude (I) Fundamental problem in distributed systems Impose order on conflicting concurrent transactions Many algorithms for atomic multicast Define generic atomic multicast primitive in Maude ◮ abstract ◮ covers all possible receiving orders Infrastructure stores (un)read AM messages Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 39 / 58

  35. My Work: Atomic Multicast in Maude (II) Atomic-multicast message M : rl [atomic-multicast] : < O : Node | msgToSend : M , receivers : OS > => < O : Node | ... > (atomic-multicast M from O to OS) . Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 40 / 58

  36. My Work: Atomic Multicast in Maude (II) Atomic-multicast message M : rl [atomic-multicast] : < O : Node | msgToSend : M , receivers : OS > => < O : Node | ... > (atomic-multicast M from O to OS) . Read: crl [receiveAtomicMulticast] : (msg M from O2 to O) < O : Node | ... > AM-TABLE => < O : Node | ... > updateAM(MC, O, AM-TABLE) if okToRead(MC, O, AM-TABLE) . Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 40 / 58

  37. Analyzing P-Store Find all reachable final states from init3 : Maude> (search init3 =>! C:Configuration .) Solution 1 C:Configuration --> ... < c1 : Client | pendingTrans : t1, txns : emptyTransList > < c2 : Client | pendingTrans : t2, txns : emptyTransList > < r1 : PStoreReplica | aborted : none, committed : < t1 : Transaction | ... > < r2 : PStoreReplica | aborted : none, committed : < t2 : Transaction | ... > ... Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 41 / 58

  38. Analyzing P-Store Find all reachable final states from init3 : Maude> (search init3 =>! C:Configuration .) Solution 1 C:Configuration --> ... < c1 : Client | pendingTrans : t1, txns : emptyTransList > < c2 : Client | pendingTrans : t2, txns : emptyTransList > < r1 : PStoreReplica | aborted : none, committed : < t1 : Transaction | ... > < r2 : PStoreReplica | aborted : none, committed : < t2 : Transaction | ... > ... sites validate transactions but client never gets result Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 41 / 58

  39. Analyzing P-Store (cont.) Solution 5 ... < r1 : PStoreReplica | aborted : none, committed : none, submitted : < t1 : Transaction | ... >, ... > < r2 : PStoreReplica | aborted : none, committed : < t2 : Transaction| ... > ... > Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 42 / 58

  40. Analyzing P-Store (cont.) Solution 5 ... < r1 : PStoreReplica | aborted : none, committed : none, submitted : < t1 : Transaction | ... >, ... > < r2 : PStoreReplica | aborted : none, committed : < t2 : Transaction| ... > ... > Host does not validate t1 even when needed info known Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 42 / 58

  41. Fixing P-Store Found the source of the errors ◮ all replicas must be involved in voting and notification ⋆ not just write replicas Modeled and analyzed proposed corrected version Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 43 / 58

  42. P-Store Summary “P-Store verified” 3 significant errors found one confusing definition key assumption missing Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 44 / 58

  43. Our Conclusions Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 45 / 58

  44. Our Conclusions I Developed formal models of large industrial data stores ◮ Google’s Megastore (from brief description) ◮ Apache Cassandra (from 345K LOC and description) ◮ P-Store (academic) Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 46 / 58

  45. Our Conclusions I Developed formal models of large industrial data stores ◮ Google’s Megastore (from brief description) ◮ Apache Cassandra (from 345K LOC and description) ◮ P-Store (academic) Automatic model checking analysis of consistency properties Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 46 / 58

  46. Our Conclusions I Developed formal models of large industrial data stores ◮ Google’s Megastore (from brief description) ◮ Apache Cassandra (from 345K LOC and description) ◮ P-Store (academic) Automatic model checking analysis of consistency properties Designed own transactional data stores ◮ Megastore-CGC ◮ variation of Cassandra Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 46 / 58

  47. Our Conclusions I Developed formal models of large industrial data stores ◮ Google’s Megastore (from brief description) ◮ Apache Cassandra (from 345K LOC and description) ◮ P-Store (academic) Automatic model checking analysis of consistency properties Designed own transactional data stores ◮ Megastore-CGC ◮ variation of Cassandra Errors, ambiguities, missing assumptions found in “verified” P-Store Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 46 / 58

  48. Our Conclusions I Developed formal models of large industrial data stores ◮ Google’s Megastore (from brief description) ◮ Apache Cassandra (from 345K LOC and description) ◮ P-Store (academic) Automatic model checking analysis of consistency properties Designed own transactional data stores ◮ Megastore-CGC ◮ variation of Cassandra Errors, ambiguities, missing assumptions found in “verified” P-Store Maude/PVeStA performance estimation close to real implementations Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 46 / 58

  49. Our “Software Engineering” Conclusions Quickly develop formal models/prototypes of complex systems ◮ experiment with different design choices Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 47 / 58

  50. Our “Software Engineering” Conclusions Quickly develop formal models/prototypes of complex systems ◮ experiment with different design choices Simulation and model checking throughout design phase ◮ model-checking-based-testing for subtle “corner cases” ◮ replaces days of whiteboard analysis ◮ too many scenarios for standard test-based development ◮ catch bugs early! Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 47 / 58

  51. Our “Software Engineering” Conclusions Quickly develop formal models/prototypes of complex systems ◮ experiment with different design choices Simulation and model checking throughout design phase ◮ model-checking-based-testing for subtle “corner cases” ◮ replaces days of whiteboard analysis ◮ too many scenarios for standard test-based development ◮ catch bugs early! Single artifact for ◮ system description ◮ rapid prototyping ◮ model checking ◮ performance estimation Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 47 / 58

  52. Our “Software Engineering” Conclusions Quickly develop formal models/prototypes of complex systems ◮ experiment with different design choices Simulation and model checking throughout design phase ◮ model-checking-based-testing for subtle “corner cases” ◮ replaces days of whiteboard analysis ◮ too many scenarios for standard test-based development ◮ catch bugs early! Single artifact for ◮ system description ◮ rapid prototyping ◮ model checking ◮ performance estimation Megastore and Megastore-CGC modeler had no formal methods experience Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 47 / 58

  53. Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 48 / 58

  54. Amazon Web Services Amazon Web Services (AWS): ◮ world’s largest cloud computing service provider ◮ more profitable than Amazon’s retail business Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 49 / 58

  55. Amazon Web Services Amazon Web Services (AWS): ◮ world’s largest cloud computing service provider ◮ more profitable than Amazon’s retail business Amazon Simple Storage Service (S3) ◮ stores > 3 trillion objects ◮ 99.99% availability of objects ◮ > 1 million requests per second DynamoDB data store Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 49 / 58

  56. Amazon Web Services and Formal Methods Formal methods used extensively at AWS during design of S3, DynamoDB, . . . Used Lamports TLA+ ◮ model checking Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 50 / 58

  57. Experiences at Amazon WS Model checking finds “corner case” bugs that would be hard to find with standard industrial methods: Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 51 / 58

  58. Experiences at Amazon WS Model checking finds “corner case” bugs that would be hard to find with standard industrial methods: “We have found that standard verification techniques in industry are necessary but not sufficient. We routinely use deep design reviews, static code analysis, stress testing, and fault-injection testing but still find that subtle bugs can hide in complex fault-tolerant systems.” Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 51 / 58

  59. Experiences at Amazon WS Model checking finds “corner case” bugs that would be hard to find with standard industrial methods: “the model checker found a bug that could lead to losing data [...]. This was a very subtle bug; the shortest error trace exhibiting the bug included 35 high-level steps. [...] The bug had passed unnoticed through extensive design reviews, code reviews, and testing.” Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 51 / 58

  60. Experiences at Amazon WS II A formal specification is a valuable precise description of an algorithm: Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 52 / 58

  61. Experiences at Amazon WS II A formal specification is a valuable precise description of an algorithm: “the author is forced to think more clearly, helping eliminating “hand waving,” and tools can be applied to check for errors in the design, even while it is being written. In contrast, conventional design documents consist of prose, static diagrams, and perhaps psuedo-code in an ad hoc untestable language.” Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 52 / 58

  62. Experiences at Amazon WS II A formal specification is a valuable precise description of an algorithm: “Talk and design documents can be ambiguous or incomplete, and the executable code is much too large to absorb quickly and might not precisely reflect the intended design. In contrast, a formal specification is precise, short, and can be explored and experimented on with tools.” Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 52 / 58

  63. Experiences at Amazon WS III Formal methods are surprisingly feasible for mainstream software development and give good return on investment: Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 53 / 58

  64. Experiences at Amazon WS III Formal methods are surprisingly feasible for mainstream software development and give good return on investment: “In industry, formal methods have a reputation for requiring a huge amount of training and effort to verify a tiny piece of relatively straightforward code. Our experience with TLA+ shows this perception to be wrong. [...] Amazon engineers have used TLA+ on 10 large complex real-world systems. In each, TLA+ has added significant value. [...] Engineers have been able to learn TLA+ from scratch and get useful results in two to three weeks.” Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 53 / 58

  65. Experiences at Amazon WS III Formal methods are surprisingly feasible for mainstream software development and give good return on investment: “Using TLA+ in place of traditional proof writing would thus likely have improved time to market, in addition to achieving greater confidence in the system’s correctness.” Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 53 / 58

  66. Experiences at Amazon WS III Quick and easy to experiment with different design choices: Peter Csaba ¨ Olveczky (U. Oslo/UIUC) Cloud Storage Systems in Maude UCM, February 20, 2017 54 / 58

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

An Introduction to Maude Joe Hendrix An Introduction to Maude p. 1/12 Talk Outline Maude is

An Introduction to Maude Joe Hendrix An Introduction to Maude p. 1/12 Talk Outline Maude is

An Introduction to Maude Joe Hendrix An Introduction to Maude p. 1/12 Talk Outline Maude is many things: Program Language Can be used for representing sequential and concurrent computation. Meta Language Maude can be used to write

137 views • 12 slides
Validation of Critical Systems Christiano Braga Instituto de Computao with Rewriting Logic

Validation of Critical Systems Christiano Braga Instituto de Computao with Rewriting Logic

2nd School of Theoretical Computer Science and Formal Methods Validation of Critical Systems Christiano Braga Instituto de Computao with Rewriting Logic in Maude Universidade Federal Fluminense s 0 n 1 n 2 start s 1 s 5 t 1 n 2 n 1 t 2 s 2

1.07k views • 72 slides
Cloud Storage Nabil Abdennadher nabil.abdennadher@hesge.ch 1 Cloud storage Objective

Cloud Storage Nabil Abdennadher nabil.abdennadher@hesge.ch 1 Cloud storage Objective

Cloud Storage Nabil Abdennadher nabil.abdennadher@hesge.ch 1 Cloud storage Objective Provide logical storage pools that abstract a more complex distributed infrastructure made of commodity hardware Separate storage service

627 views • 15 slides
Distributed and Cloud Storage Systems Corso di Sistemi Distribuiti e Cloud Computing A.A. 2017/18

Distributed and Cloud Storage Systems Corso di Sistemi Distribuiti e Cloud Computing A.A. 2017/18

Universit degli Studi di Roma Tor Vergata Dipartimento di Ingegneria Civile e Ingegneria Informatica Distributed and Cloud Storage Systems Corso di Sistemi Distribuiti e Cloud Computing A.A. 2017/18 Valeria Cardellini Why to scale

1.15k views • 82 slides
Distributed and Cloud Storage Systems Corso di Sistemi Distribuiti e Cloud Computing A.A. 2018/19

Distributed and Cloud Storage Systems Corso di Sistemi Distribuiti e Cloud Computing A.A. 2018/19

Universit degli Studi di Roma Tor Vergata Dipartimento di Ingegneria Civile e Ingegneria Informatica Distributed and Cloud Storage Systems Corso di Sistemi Distribuiti e Cloud Computing A.A. 2018/19 Valeria Cardellini Why to scale

396 views • 37 slides
Distributed Storage Systems part 1 Marko Vukoli Distributed Systems and Cloud Computing This

Distributed Storage Systems part 1 Marko Vukoli Distributed Systems and Cloud Computing This

Distributed Storage Systems part 1 Marko Vukoli Distributed Systems and Cloud Computing This part of the course (5 slots) Distributed Storage Systems CAP theorem and Amazon Dynamo Apache Cassandra Distributed Systems

767 views • 52 slides
Array BP-XOR Codes for Reliable Cloud Storage Systems Yongge Wang UNC Charlotte, USA July

Array BP-XOR Codes for Reliable Cloud Storage Systems Yongge Wang UNC Charlotte, USA July

Challenges in Cloud Storage and Our Solutions Background Edge-colored graphs Array BP-XOR codes XOR-based SSS scheme Flat non-MDS BP-XOR codes Array BP-XOR Codes for Reliable Cloud Storage Systems Yongge Wang UNC Charlotte, USA July

1.32k views • 76 slides
Kurma: Secure Geo-distributed Multi-cloud Storage Gateways Ming Chen and Erez Zadok Stony Brook

Kurma: Secure Geo-distributed Multi-cloud Storage Gateways Ming Chen and Erez Zadok Stony Brook

Kurma: Secure Geo-distributed Multi-cloud Storage Gateways Ming Chen and Erez Zadok Stony Brook University File Systems and Storage Lab (FSL) Cloud Storage Gateways l Benefits of cloud gateways Public NAS u Combine advantages of both Cloud

650 views • 51 slides
Cloud storage state of affairs Storage clusters contain thousands of storage nodes, with e.g. 500

Cloud storage state of affairs Storage clusters contain thousands of storage nodes, with e.g. 500

A mathematical theory of distributed storage Dagstuhl workshop (16321) Coding in the time of Big Data Michael Luby August 8, 2016 Research Cloud storage state of affairs Storage clusters contain thousands of storage nodes, with e.g. 500 TB

796 views • 37 slides
A Simulation-based Evaluation of a Hybrid Storage System combining P2P, F2F, and Cloud storage

A Simulation-based Evaluation of a Hybrid Storage System combining P2P, F2F, and Cloud storage

. . A Simulation-based Evaluation of a Hybrid Storage System combining P2P, F2F, and Cloud storage with a Distributed Reputation System Anders Skoglund andsk668@student.liu.se November 04, 2013 . Cloud storage P2P storage F2F storage

660 views • 36 slides
Large objects in the Cloud Thursday, 11 April 13 Riak Cloud Storage Cloud Storage software

Large objects in the Cloud Thursday, 11 April 13 Riak Cloud Storage Cloud Storage software

Large objects in the Cloud Thursday, 11 April 13 Riak Cloud Storage Cloud Storage software backed by Riak Simple API Multi-tenant, Per-tenant Reporting Pluggable Authentication Multi Data Center Replication (Enterprise)

369 views • 18 slides
Cloud Storage Systems CS 6410 - Fall 2019 Midhul Vuppalapati (mvv25@cornell.edu) History of

Cloud Storage Systems CS 6410 - Fall 2019 Midhul Vuppalapati (mvv25@cornell.edu) History of

Cloud Storage Systems CS 6410 - Fall 2019 Midhul Vuppalapati (mvv25@cornell.edu) History of Distributed Storage Systems Networked File Systems Network File System (NFS), Sun, 1986 Andrew File System (AFS), CMU, 1988 Goal : Make

815 views • 30 slides
Design &amp; Implementation of a Portable File Synchronisation Mechanism for a Cloud Storage

Design &amp; Implementation of a Portable File Synchronisation Mechanism for a Cloud Storage

National Technical University of Athens Design &amp; Implementation of a Portable File Synchronisation Mechanism for a Cloud Storage Environment Supervisor Prof. Nektarios Koziris Assistant Supervisor Dr. Vangelis Koukis Candidate Vasilis

764 views • 40 slides
Could Cloud Storage Be Disrupted in the Next Decade? Andromachi Chatzieleftheriou, Ioan

Could Cloud Storage Be Disrupted in the Next Decade? Andromachi Chatzieleftheriou, Ioan

Could Cloud Storage Be Disrupted in the Next Decade? Andromachi Chatzieleftheriou, Ioan Stefanovici , Dushyanth Narayanan Benn Thomsen, Antony Rowstron Microsoft Research Cloud Storage Landscape: Yesterday + Today Storage Technologies $$$$

147 views • 12 slides
Data Management Systems Storage Management Memory hierarchy Segments and file storage

Data Management Systems Storage Management Memory hierarchy Segments and file storage

Data Management Systems Storage Management Memory hierarchy Segments and file storage Database buffer cache Storage techniques in context Cloud native databases (Snowflake) Gustavo Alonso Institute of Computing Platforms

229 views • 12 slides
Cloud Scale Storage Systems Sean Ogden October 30, 2013 Evolution P2P routing/DHTs (Chord,

Cloud Scale Storage Systems Sean Ogden October 30, 2013 Evolution P2P routing/DHTs (Chord,

Cloud Scale Storage Systems Sean Ogden October 30, 2013 Evolution P2P routing/DHTs (Chord, CAN, Pastry, etc.) P2P Storage (Pond, Antiquity) Storing Greg's baby pictures on machines of untrusted strangers that are connected with wifi

877 views • 38 slides
Important Lessons Lamport &amp; vector clocks both give a logical timestamps Total

Important Lessons Lamport &amp; vector clocks both give a logical timestamps Total

Important Lessons Lamport &amp; vector clocks both give a logical timestamps Total ordering vs. causal ordering Other issues in coordinating node activities Exclusive access to resources/

150 views • 10 slides
Distributed Systems (ICE 601) Replication &amp; Consistency - Part 1 Dongman Lee ICU Class

Distributed Systems (ICE 601) Replication &amp; Consistency - Part 1 Dongman Lee ICU Class

Distributed Systems (ICE 601) Replication &amp; Consistency - Part 1 Dongman Lee ICU Class Overview Introduction Replication Model Request Ordering Consistency Models Consistency Protocols Case study Lazy

472 views • 9 slides
Verteilte Systeme (Distributed Systems) Karl M. Gschka Karl.Goeschka@tuwien.ac.at

Verteilte Systeme (Distributed Systems) Karl M. Gschka Karl.Goeschka@tuwien.ac.at

Verteilte Systeme (Distributed Systems) Karl M. Gschka Karl.Goeschka@tuwien.ac.at http://www.infosys.tuwien.ac.at/teaching/courses/ VerteilteSysteme/ Dependability and fault tolerance Taxonomy Techniques and challenges

1.8k views • 100 slides
Solving Atomic Broadcast Eden : a Consensus Based Group Communication System p.1/ ?? Solving

Solving Atomic Broadcast Eden : a Consensus Based Group Communication System p.1/ ?? Solving

Solving Atomic Broadcast Eden : a Consensus Based Group Communication System p.1/ ?? Solving Atomic Broadcast ABcast ABdeliver Messages to order Identities of next messages Missing messages identities GAF Propose Decide Retransmissions

960 views • 57 slides
G-DUR A Middleware for Assembling, Analyzing, and Improving Transactional Protocols Masoud Saeida

G-DUR A Middleware for Assembling, Analyzing, and Improving Transactional Protocols Masoud Saeida

G-DUR A Middleware for Assembling, Analyzing, and Improving Transactional Protocols Masoud Saeida Ardekani * , INRIA &amp; UPMC-Lip6 Pierre Sutra, Universit de Neuchtel Marc Shapiro, INRIA &amp; UPMC-Lip6 * now @ Purdue University Masoud

312 views • 20 slides
Failures and Consensus Failures and Consensus Coordination Coordination If the solution to

Failures and Consensus Failures and Consensus Coordination Coordination If the solution to

Failures and Consensus Failures and Consensus Coordination Coordination If the solution to availability and scalability is to decentralize and replicate functions and data, how do we coordinate the nodes? data consistency update

712 views • 36 slides
Time Synchronization and Logical Clocks CS 240: Computing Systems and Concurrency Lecture 5

Time Synchronization and Logical Clocks CS 240: Computing Systems and Concurrency Lecture 5

Time Synchronization and Logical Clocks CS 240: Computing Systems and Concurrency Lecture 5 Mootaz Elnozahy Today 1. The need for time synchronization 2. Wall clock time synchronization 3. Logical Time 2 A distributed edit-compile

707 views • 59 slides
Message Ordering and Group Communications Course: Distributed Computing Faculty: Dr. Rajendra

Message Ordering and Group Communications Course: Distributed Computing Faculty: Dr. Rajendra

Message Ordering and Group Communications Course: Distributed Computing Faculty: Dr. Rajendra Prasath Spring 2019 About this topic This course covers various concepts in Message Ordering and Group communication in Distributed Systems. We will

556 views • 43 slides

More recommend