1 / 40

CSCE 668 DISTRIBUTED ALGORITHMS AND SYSTEMS

CSCE 668 DISTRIBUTED ALGORITHMS AND SYSTEMS. Fall 2011 Prof. Jennifer Welch. Problems Solvable in Failure-Prone Asynchronous Systems.

shelley-hardin
Download Presentation

CSCE 668 DISTRIBUTED ALGORITHMS AND SYSTEMS

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Set 19: Asynchronous Solvability CSCE 668DISTRIBUTED ALGORITHMS AND SYSTEMS Fall 2011 Prof. Jennifer Welch

  2. Problems Solvable in Failure-Prone Asynchronous Systems • Although consensus is not solvable in failure-prone asynchronous systems (neither message passing nor read/write shared memory), there are some interesting problems that are solvable: • set consensus • approximate agreement • renaming • k-exclusion weakenings of consensus - "opposite" of consensus - fault-tolerant variant of mutex Set 19: Asynchronous Solvability

  3. Model Assumptions • asynchronous • shared memory with read/write registers • heavy use of atomic snapshot objects • at most f crash failures of procs. • results can be translated to message passing if f < n/2 (cf. Chapter 10) • may be a few asides into message passing Set 19: Asynchronous Solvability

  4. Set Consensus Motivation • By judiciously weakening the definition of the consensus problem, we can overcome the asynchronous impossibility • We've already seen a weakening of consensus: • weaker termination condition for randomized algorithms • How about weakening the agreement condition? • One weakening is to allow more than one decision value: • allow a set of decisions Set 19: Asynchronous Solvability

  5. Set Consensus Definition Termination: Eventually, each nonfaulty processor decides. k-Agreement: The number of different values decided on by nonfaulty processors is at most k. Validity: Every nonfaulty processor decides on a value that is the input of some processor. new Set 19: Asynchronous Solvability

  6. Set Consensus Algorithm • Uses a shared atomic snapshot object X • can be implemented with read/write registers • update your segment of X with your input • repeatedly scan X until there are at least n - f nonempty segments • decide on the minimum value appearing in any segment Set 19: Asynchronous Solvability

  7. Correctness of Set Consensus Algorithm • Termination: at most f crashes. • Validity: every decision is some proc's input • Why does k-agreement hold? • We'll show it does as long as k > f. • Sanity check: When k = 1, we have standard consensus. As long as there is less than 1 failure, we can solve the problem. Set 19: Asynchronous Solvability

  8. k-Set Agreement Condition • Let S be set of min values in final scan of each nf proc; these are the nf decisions • Suppose in contradiction |S| > f + 1. • Let v be largest value in S, the decision of pi. • So pi's final scan misses at least f + 1 values, contradicting the code. Set 19: Asynchronous Solvability

  9. Synchronous vs. Asynchronous? • How does the previous, asynchronous, algorithm compare to the synchronous algorithm for k-set consensus from Chapter 5 homework? • Recall the synchronous algorithm runs in f/k + 1 rounds. Set 19: Asynchronous Solvability

  10. Set Consensus Lower Bound Theorem: There is no asynchrounous algorithm for solving k-set consensus in the presence of f failures, if f ≥ k. • Straightforward extensions of consensus impossibility result fail; even proving the existence of an initial bivalent configuration is quite involved. • Original proof of set-consensus impossibility used concepts from algebraic topology • Textbook's proof uses more elementary machinery, but still very involved Set 19: Asynchronous Solvability

  11. Approximate Agreement Motivation • An alternative way to weaken the agreement condition for consensus: • Require that the decisions be close to each other, but not necessarily equal • Seems appropriate for continuous-valued problems (as opposed to discrete) Set 19: Asynchronous Solvability

  12. Approximate Agreement Definition Termination: Eventually, each nonfaulty processor decides. -Agreement: All nonfaulty decisions are within  of each other. Validity: Every nonfaulty decision is within the range of the input values. new new Set 19: Asynchronous Solvability

  13. Approximate Agreement Algorithm • Assume procs know the range from which input values are drawn: • let D be the length of this range • wait-free: up to n - 1 procs can fail • algorithm is structured as a series of "asynchronous rounds": • exchange values via a snapshot object, one per round • compute midpoint for next round • continue until spread of values is within , which requires about log2 D/rounds Set 19: Asynchronous Solvability

  14. Approximate Agreement Algorithm Shared atomic snapshot objects ASO[1], ASO[2],... Initially local variable v = pi's input Initially local variable r = 1 while true do • update pi's segment of ASO[r] to be v • let scan be set of values obtained by scanning ASO[r] • v := midpoint(scan) • if r = log2 (D/) + 1then decide v and terminate • else r++ Set 19: Asynchronous Solvability

  15. Analysis of Approx. Agreement Alg. Definitions w.r.t. a particular execution: • M = log2 (D/) + 1 • U0 = set of input values • Ur = set of all values ever written to ASO[r] Set 19: Asynchronous Solvability

  16. Helpful Lemma Lemma (16.8): Consider any round r < M. Let u be the first value written to ASO[r]. Then the values written to ASO[r+1] are in this range: min(Ur) (min(Ur)+u)/2 u (max(Ur)+u)/2 max(Ur) elements of Ur+1 are in here Set 19: Asynchronous Solvability

  17. Implications of Lemma • The range of values written to the ASO object for round r + 1 is contained within the range of values written to the ASO object for round r. • range(Ur+1) range(Ur) • The spread (max - min) of values written to the ASO object for round r + 1 is at most half the spread of values written to the ASO object for round r. • spread(Ur+1) ≤ spread(Ur)/2 Set 19: Asynchronous Solvability

  18. Correctness of Algorithm • Termination: Each proc executes M asynchronous rounds. • Validity: The range at each round is contained in the range at the previous round. • -Agreement: spread(UM) ≤ spread(U0)/2M ≤ D/2M ≤  Set 19: Asynchronous Solvability

  19. Handling Unknown Input Range • Range might not be known. • Actual range in an execution might be much smaller than maximum possible range. • First idea: have a preprocessing phase in which procs try to determine input range • but asynchrony and possible failures makes this approach problematic Set 19: Asynchronous Solvability

  20. Handling Unknown Input Range • Use just one atomic snapshot object • Dynamically recalculate how many rounds are needed as more inputs are revealed • Skip over rounds to try to catch up to maximum observed round • Only consider values associated with maximum observed round • Still use midpoint Set 19: Asynchronous Solvability

  21. Unknown Input Range Algorithm shared atomic snapshot object A; initially all segments  updatei(A,[x,1,x]), where x is pi's input // [original input, rd#, current estimate] repeat scan A let S be spread of all inputs in non- segments if S = 0 then maxRound := 0 else maxRound := log2(S/) let rmaxbe largest round in non- segments let values be set of candidates in segments with round number rmax update pi's segment in A with [x,rmax+1,midpt(values)] until rmax ≥ maxRound decide midpoint(values) Set 19: Asynchronous Solvability

  22. Analysis of Unknown Input Range Algorithm Definitions w.r.t. a particular execution: • U0 = set of all input values • Ur = set of all values ever written to A with round number r • M = largest r s.t. Ur is not empty With these changes, correctness proof is similar to that for known input range algorithm. Set 19: Asynchronous Solvability

  23. Key Differences in Proof • Why does termination hold? • a proc's local maxRound variable can only increase if another proc wakes up and increases the spread of the observable inputs. This can happen at most n - 1 times. • Why does -agreement hold? • If pi's input is observed by pj the last time pj computes its maxRound, same argument as before. • Otherwise, when piwakes up, it ignores its own input and uses values from maxRound or later. Set 19: Asynchronous Solvability

  24. Renaming • Procs start with unique names from a large domain • Procs should pick new names that are still distinct but that are from a smaller domain • Motivation: Suppose original names are serial numbers (many digits), but we'd like the procs to do some kind of time slicing based on their ids Set 19: Asynchronous Solvability

  25. Renaming Problem Definition Termination:Eventually every nonfaulty proc pidecides on a new name yi Uniqueness:If pi and pj are distinct nonfaulty procs, then yi ≠ yj. We are interested in anonymous algorithms: procs don't have access to their indices, just to their original names. Code depends only on your original name. Set 19: Asynchronous Solvability

  26. Performance of Renaming Algorithm • New names should be drawn from {1,2,…,M}. • We would like M to be as small as possible. • Uniqueness implies M must be at least n. • Due to the possibility of failures, M will actually be larger than n. Set 19: Asynchronous Solvability

  27. Renaming Results • Algorithm for wait-free case (f = n –1) with M = n + f = 2n – 1. • Algorithm for general f with M = n + f. • Lower bound that M must be at least n + 1, for wait-free case. • Proof similar to impossibility of wait-free consensus • Stronger lower bound that M must be at least 2n – 1, for wait-free case if n satisfies a certain number-theoretic property • If n does not satisfy the property, there is a wait-free algorithm with M = 2n – 2. (includes n = 6, 10, 14,...) Set 19: Asynchronous Solvability

  28. Wait-Free Renaming Algorithm Shared atomic snapshot object A; initially all segments  s := 1 // suggestion for my new name while true do update pi's segment of A to be [x,s], where x is pi’s original name scan A if s is also someone else's suggestion then let r be rank of x among original names of non- segments let s be r-th smallest positive integer not currently suggested by another proc else decide on s for new name and terminate Set 19: Asynchronous Solvability

  29. Analysis of Renaming Algorithm Uniqueness:Suppose in contradiction piand pj choose same new name, s. pi's last update before deciding: suggests s pj's last scan before deciding s pi's last scan before deciding s sees s as pi's suggestion and doesn't decide s contradiction! Set 19: Asynchronous Solvability

  30. Analysis of Renaming Algorithm • New name space is {1, …, 2n– 1}. • Why? • rank of a proc pi's original name is at most n (the largest one) • worst case is when each of the n – 1 other procs has suggested a different new name for itself, so suggested names are {1, …, n– 1}. • Then pi suggests n + n – 1 = 2n – 1. Set 19: Asynchronous Solvability

  31. Analysis of Renaming Algorithm Termination:Suppose in contradiction some set T of nonfaulty procs never decide in some execution. • Consider the suffix  of the execution in which • each proc in T has already done at least one update and • only procs in T take steps (others have either already crashed or decided). Set 19: Asynchronous Solvability

  32. Analysis of Renaming Algorithm • Let F be the set of new names that are free (not suggested at the beginning of  by any proc not in T) • the trying procs need to choose new names from this set. • Let z1, z2,… be the names in F in order. • By the definition of , no proc wakes up during  and reveals an additional original name, so all procs in T are working with the same set of original names during . • Let pi be proc whose original name has smallest rank (among this set of original names). Let r be this rank. Set 19: Asynchronous Solvability

  33. Analysis of Renaming Algorithm • Eventually procs other than pistop suggesting zr as a new name: • After  starts, every scan indicates a set of free names that is no larger than F. • Every trying proc other than pi has a larger rank and thus continually suggests a new name for itself that is larger than zr, once it does the first scan in . Set 19: Asynchronous Solvability

  34. Analysis of Renaming Algorithm • Eventually pidoes suggest zr as its new name: • By choice of zr as r-thsmallest free new name, and fact that eventually other trying procs stop suggesting z1 through zr, eventually pisees zr as free name with r-th smallest rank. • Contradicts assumption that piis trying (i.e., stuck). • So termination holds. Set 19: Asynchronous Solvability

  35. General Renaming • Suppose we know that at most f procs will fail, where f is not necessarily n - 1. • We can use the wait-free algorithm, but it is wasteful in the size of the new name space, 2n – 1, if f < n – 1. • We can do better (if f < n – 1) with a slightly different algorithm: • keep track in the snapshot object of whether you have decided • an undecided proc suggests a new name only if its original name is among the f + 1 lowest names of procs that have not yet decided. Set 19: Asynchronous Solvability

  36. k-Exclusion Problem • A fault-tolerant version of mutual exclusion. • Processors can fail by crashing, even in the critical section (stay there forever). • Allow up to k processors to be in the critical section simultaneously. • If < k processors fail, then any nonfaulty processor that wishes to enter the critical section eventually does so. Set 19: Asynchronous Solvability

  37. k-Exclusion Algorithm cf. paper by Afek et al. [5]. Set 19: Asynchronous Solvability

  38. k-Assignment Problem • A specialization of k-Exclusion to include: • Uniqueness: Each proc in the critical section has a variable called slot, which is an integer between 1 and m. If piand pj are in the C.S. concurrently, then they have different slots. • Models situation when there is a pool of identical resources, each of which must be used exclusively: • k is number of procs that can be in the pool concurrently • m is the number of resources • To handle failures, m should be larger than k Set 19: Asynchronous Solvability

  39. k-exclusion exit section k-Assignment Algorithm Schema k-assignment entry section k-exclusion entry section renaming using m = 2k-1 names what about repeated invocations? k-assignment exit section Set 19: Asynchronous Solvability

  40. request-name for long-lived renaming using m = 2k-1 names release-name for long-lived renaming using m = 2k-1 names k-Assignment Algorithm Schema k-assignment entry section k-exclusion entry section k-assignment exit section k-exclusion exit section Set 19: Asynchronous Solvability

More Related