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Page Replacement Algorithm

Page Replacement Algorithm. Performance of Demand Paging. Three Major Components of the Page-Fault Service Time Service the Page-Fault Interrupt. Read in the Page. Restart the Process.  Disk I/O is do expensive. Page Replacement(1). Need for Page Replacement Fig 8.5 Page Replacement

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Page Replacement Algorithm

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  1. Page Replacement Algorithm SunMoon university

  2. Performance of Demand Paging • Three Major Components of the Page-Fault Service Time • Service the Page-Fault Interrupt. • Read in the Page. • Restart the Process.  Disk I/O is do expensive. SunMoon university

  3. Page Replacement(1) • Need for Page Replacement • Fig 8.5 • Page Replacement • fig 8.6 • p.249 • double the page-service time • dirty-bit, (modify) bit • Read-only pages • Reduce I/O time by one-half SunMoon university

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  6. Page Replacement(2) • To implement Demand Paging • frame-allocation algorithm • how many frame to allocate to each process • page replacement algorithm SunMoon university

  7. Page Replacement Algorithm(1) [1] lowest page-fault rate [2] reference string - random number - tracing a given system and recording the addr. to consider only the page number [3] the number of page frames available per processes SunMoon university

  8. Page Replacement Algorithm(2) 1) Random Page Replacement • low overhead • simplest • equal lielihood of being selected for replacement • rarely used SunMoon university

  9. Page Replacement Algorithm(3) 2) FIFO Page Replacement • time-stamp each as it enters primary storage • choose the page that has been in storage the longest • fig 8.8 • advantages • easy, simple to understand and program • disadvantages • Replace heveily used pages SunMoon university

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  11. Page Replacement Algorithm(4) • bad replacement choice ( ex. active pages) • Increase the page fault, solves the process execution. • but, not incorrect execution • FIFO Anomaly or Beladely’s Anomaly • under FIFO page replacement, certain page reference patterns actually cause more page faults when the number of page frames allocated to a process is increased. • fig 8.9 • Dfig 9.1 SunMoon university

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  14. Page Replacement Algorithm(5) 3) The Principle of Optimality( OPT or MIN ) • The page to replace is the one that will not be used again for the furthest time into the future. • fig 8.10 • requires future knowledge of the reference string. SunMoon university

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  16. Page Replacement Algorithm(6) 4) LRU ( Least Recently Used • select the page for replacement that has not been used for the longest time • When the page was referenced. cf.) FIFO : when the page was coming into the memory. • each page be time-stamped whenever it is referenced • substantial overhead • approximate LRU are used • fig 8.11 SunMoon university

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  18. Page Replacement Algorithm(7) • two implementation • counters • a logical clock, require search • stack • fig 8.12 • doubly-linked list, micro-code SunMoon university

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  20. Page Replacement Algorithm(8) 5) LFU (Least Frequently Used ) • that is least frequently used or least intensively referenced • approximation to LRU • counting algorithm • not common • expensive • not approximate on OPT replacement • a possibility to replace the just brought in pages. • Goal  resonable decision  low overhead SunMoon university

  21. Page Replacement Algorithm(9) 6) MFU ( Most Frequently Used ) • the page with smallest count was probably just brought in and has yet to be used. 7) LRU Approximation Algorithm [1] Additional-Reference-Bits Algorithm • reference-bit • additional an “8-bits” byte for each page • at regular interval( say every 100ms ) R 7 … 0  • R is reference bit. • R=1, if referenced. Otherwise, R=0. SunMoon university

  22. Page Replacement Algorithm(10) [2]Sencond-Chance Algorithm - basically, FIFO - When a page has been selected, inspect its reference bit. if 0,  replace if 1,  give a second chance clear reference bit set current time in FIFO queue move on the next FIFO page -fig 8.13 -circular queue : “second chance 를 부여 받은 페이지가 다시 검사되기 이전에 ref. bit가 set된다면?”  never replace ?? -if all bits are clear until second chance same as FIFO replacement worst case SunMoon university

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  24. Page Replacement Algorithm(11) [3] NUR (Not Used Recently), Enhanced Second-Chance Alg. • a page that has not been changed while in primary storage • the addition of two hardware bits per page • reference bit • modified bit (=dirty bit) • low overhead • periodically set all the referenced bits to 0 to get a fresh start. • similar to Page 259 (5). SunMoon university

  25. Working-Set Model (1) •   working-set window  a fixed number of page references Example: 10,000 instruction • WSSi (working set of Process Pi) =total number of pages referenced in the most recent  (varies in time) • if  too small will not encompass entire locality. • if  too large will encompass several localities. • if  =   will encompass entire program. SunMoon university

  26. Working-Set Model (2) • D =  WSSi  total demand frames • if D > m  Thrashing • Policy if D > m, then suspend one of the processes. SunMoon university

  27. Keeping Track of the Working Set • Approximate with interval timer + a reference bit • Example:  = 10,000 • Timer interrupts after every 5000 time units. • Keep in memory 2 bits for each page. • Whenever a timer interrupts copy and sets the values of all reference bits to 0. • If one of the bits in memory = 1  page in working set. • Why is this not completely accurate? • Improvement = 10 bits and interrupt every 1000 time units. SunMoon university

  28. Page Replacement Algorithm Based on Locality or Working Set(1) • Locality • Processes tend to reference storage in nonuniform, highly localized patterns. • Temporal locality (Time) • storage locations referenced recently are likely to be referenced in the near future • looping, subroutines, stack SunMoon university

  29. Page Replacement Algorithm Based on Locality or Working Set(2) • Spacial locality (Space) • Storage references tend to be clustered. • Once a location is referenced, it is highly likely that nearby locations will be referenced. • array traversals, sequential code executions, etc. • fig 8.15 SunMoon university

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  31. Page Replacement Algorithm Based on Locality or Working Set(3) • Working set theory of program behavior • By Denning • a collection of pages a process is actively referenced. SunMoon university

  32. Page Replacement Algorithm Based on Locality or Working Set(4) • its working set of pages must be maintained in primary storage. • for a program to run efficiently • minimize page faults • Otherwise, Cause of“Thrashing” • the program repeatedly requests pages from secondary storage • maximize page faults • a process is thrashing if it is spending more time paging than executing. • fig 8.14 SunMoon university

  33. Thrashing (1) • If a process does not have “enough” pages, the page-fault rate is very high. This leads to: • low CPU utilization. • operating system thinks that it needs to increase the degree of multiprogramming. • another process added to the system. • Thrashing a process is busy swapping pages in and out. SunMoon university

  34. Thrashing (2) • Why does paging work?Locality model • Process migrates from one locality to another. • Localities may overlap. • Why does thrashing occur? size of locality > total memory size SunMoon university

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  36. Working Set Storage Management Police(1) • Maintain the working sets of active programs in primary storage. • Working Set W (t, w) at time t • the set of pages referenced by the process during the process time interval t-w to t. • w : working set window size • t : the time during which a process has the CPU SunMoon university

  37. Working Set Storage Management Police(2) • Allocation at t = | w (t,w) | • Replacement : replace page p at t if p  w (t,w) && p  w (t-1,w) • fetch on demand • Placement : don’t care in paging systems SunMoon university

  38. Working Set Storage Management Police(3) • Dfig 9.2 / 9.3 / 9.4 / 9.5 • Example) SunMoon university

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  40. Working-set model SunMoon university

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  43. Working Set Storage Management Police(4) • program 1. window size 2. page fault frequency police 3. optimal police SunMoon university

  44. Other Considerations (1) [1] Page Size • Smaller the page size • the more pages and page frames • the larger page tables  table fragementation • Larger • memory waste • I/O transfers from disk • Need large page SunMoon university

  45. Other Considerations (2) • the property of locality of reference • need samaller  tighter working set • Internal fragementetion • the samaller the page size, the less the internal fragementetion SunMoon university

  46. Other Considerations (3) [2] Global versus Local Allocation SunMoon university

  47. Other Considerations (4) [3] Prepaging vs. Anticipatory Paging • Pure demand-paging system • the larger number of page faults when a process is started • Prepaging • an attempt to prevent this high level page fault of initial paging. • bring into memory at one time all the pages that will be need. SunMoon university

  48. Other Considerations (5) • In working-set model, • suspend a process ( due to I/O, a lack of free frames ) • remember Working Set • when resumed, ( I/O completion, enough free frames ) • Advantages • cost of prepaging < cost of servicing the corresponding page fault • Disadvantages • the cost that many of the prepaged pages are not used. SunMoon university

  49. Other Considerations (6) • Anticipatory Paging • Advantages • if correct decision, reduce execution time. • otherwise, low H/W cost. SunMoon university

  50. Other Considerations (7) [4] Program Structure • Demand paging is designed to be transparent to the user program. • Pascal Program • Stack –“top”, high locality • Hash Table – bad locality • Pointer – tend to randomize access to ` memory.3399 SunMoon university

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