Operating Systems CMPSCI 377 Lecture 13: Demand Paging

1. Operating SystemsCMPSCI 377Lecture 13: Demand Paging Emery Berger University of Massachusetts, Amherst

2. Last Time: Paging • Motivation • Fragmentation • Page Tables • Hardware Support • Other Benefits

3. Today: Demand-Paged VM • Reading pages • Writing pages • Swap space • Page eviction • Cost of paging • Page replacement algorithms • Evaluation

4. Demand-Paged Virtual Memory • Key idea: use RAM as cache for disk • OS transparently moves pages • Page table: page on disk, in memory • OS updates whenever pages change state • Requires locality: • Working set(pages referenced recently)must fit in memory • If not: thrashing (nothing but disk traffic)

5. Demand-Paging Diagram

6. Key Policy Decisions • Two key questions: (for any cache): • When do we read page from disk? • When do we write page to disk?

7. Reading Pages • Read on-demand: • OS loads page on its first reference • May force an eviction of page in RAM • Pause while loading page = page fault • Can also perform pre-paging: • OS guesses which page will next be needed, and begins loading it • Advantages? Disadvantages? • Most systems just do demand paging

8. Demand Paging • On every reference, check if page is in memory (valid bit in page table) • If not: trap to OS • OS checks address validity, and • Selects victim page to be replaced • Invalidates old page in page table • Begins loading new page from disk • Switches to other process(paging = implicit I/O) • Note: must restart instruction later

9. Demand Paging, Continued • Interrupt signals page arrival, then: • OS updates page table entry • Continues faulting process • Stops current process • We could continue currently executing process – but why not? • And where does the victim page go?

10. Demand Paging, Continued • Interrupt signals page arrival, then: • OS updates page table entry • Continues faulting process • Stops current process • We could continue currently executing process – but why not? • Page just brought in could get paged out... • And where does the victim page go?

11. Swap Space • Swap space = where victim pages go • Partition or special file reserved on disk • Sometimes: special filesystem (“tmpfs”) • What kind of victim pages can be evicted without going to swap? • Size of reserved swap space limits what?

12. Swap Space • Swap space = where victim pages go • Partition or special file reserved on disk • Sometimes: special filesystem (“tmpfs”) • What kind of victim pages can be evicted without going to swap? • Read-only (“text”), untouched anonymous pages • Size of reserved swap space limits what?

13. Swap Space • Swap space = where victim pages go • Partition or special file reserved on disk • Sometimes: special filesystem (“tmpfs”) • What kind of victim pages can be evicted without going to swap? • Read-only (“text”), untouched anonymous pages • Size of reserved swap space limits what? • Total virtual memory

14. Virtual Memory Locations • VM pages can now exist in one or more of following places: • Physical memory (in RAM) • Swap space (victim page) • Filesystem (why?) • Requires more sophisticated page table • Slightly more expensive, but real expense...

15. Cost of Paging • Worst-case analysis – useless • Easy to construct adversary example:every page requires page fault A, B, C, D, E, F, G, H, I, J, A... size of available memory

16. Cost of Paging, continued • “Average-case” • No such thing (what’s “average” reference behavior?) • But: processes exhibit locality,so performance generally not bad • Temporal locality: processes tend to reference same items repeatedly • Spatial locality: processes tend to reference items near each other (e.g., on same page)

17. Effective Access Time • Let p = probability of page fault (0 ·p· 1)ma = memory access time • Effective access time =(1 – p) * ma + p * page fault service time • Memory access = 200ns, page fault = 25ms:effective access time = (1-p)*200 + p*25,000,000 • Desired effective access time = 10% slower than memory access time: p = ?

18. Effective Access Time • Let p = probability of page fault (0 ·p· 1)ma = memory access time • Effective access time =(1 – p) * ma + p * page fault service time • Memory access = 200ns, page fault = 25ms:effective access time = (1-p)*200 + p*25,000,000 • Desired effective access time = 10% slower than memory access time: p = 8*10-7

19. Evaluating Page Replacement Algorithms • Worst-case – all just as bad • Average-case – what does that mean? • Empirical studies – real application behavior • Theory: competitive analysis • Can’t do better than optimal • How far (in terms of faults) is algorithm from optimal in worst-case? • Competitive ratio • If algorithm can’t do worse than 2x optimal,it’s 2-competitive

20. Page Replacement Algorithms • MIN, OPT (optimal) • RANDOM • evict random page • FIFO (first-in, first-out) • give every page equal residency • LRU (least-recently used) • MRU (most-recently used)

21. MIN/OPT • Invented by Belady (“MIN”), now known as “OPT”: optimal page replacement • Evict page to be accessed furthest in the future • Provably optimal policy • Just one small problem... • Requires predicting the future • Useful point of comparison

22. MIN/OPT example • Page faults: 5

23. RANDOM • Evict any page • Works surprisingly well • Theoretically: very good • 2 * Hk competitive (Hk¼ ln k) • Not used in practice:takes no advantage of locality

24. LRU • Evict page that has not been used in longest time (least-recently used) • Approximation of MIN if recent past is good predictor of future • A variant of LRU used in all real operating systems • Competitive ratio: k, (k = # of page frames) • Best possible for deterministic algs.

25. LRU example • Page faults: ?

26. LRU example • Page faults: 5

27. LRU, example II • Page faults: ?

28. LRU, example II • Page faults: 12!

29. FIFO • First-in, first-out: evict oldest page • Also has competitive ratio k • But: performs miserably in practice! • LRU takes advantage of locality • FIFO does not • Suffers from Belady’s anomaly: • More memory can mean more paging! • LRU & other “stack” algs. do not

30. FIFO & Belady’s Anomaly

31. LRU: No Belady’s Anomaly • Why no anomaly for LRU?

32. MRU • Evict most-recently used page • Shines for LRU’s worst-case: loop that exceeds RAM size • What we really want: adaptive algorithms(e.g., EELRU – Kaplan & Smaragdakis) A, B, C, D, A, B, C, D, ... size of available memory

33. Summary • Reading pages • Writing pages • Swap space • Page eviction • Cost of paging • Page replacement algorithms • Evaluation

34. Next Time • Virtual memory implementation • Interaction with multiprogramming