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Transforming Policies into Mechanisms with Infokernel

Transforming Policies into Mechanisms with Infokernel. Andrea C. Arpaci-Dusseau, Remzi H. Arpaci-Dusseau, Nathan C. Burnett, Timothy E. Denehy , Thomas J. Engle, Haryadi S. Gunawi, James A. Nugent, and Florentina I. Popovici Department of Computer Sciences University of Wisconsin - Madison.

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Transforming Policies into Mechanisms with Infokernel

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  1. Transforming Policies into Mechanisms with Infokernel Andrea C. Arpaci-Dusseau, Remzi H. Arpaci-Dusseau, Nathan C. Burnett, Timothy E. Denehy, Thomas J. Engle, Haryadi S. Gunawi, James A. Nugent, and Florentina I. Popovici Department of Computer Sciences University of Wisconsin - Madison

  2. Policies and Mechanisms • Definitions • Policies are schemes for deciding what to do • Mechanisms are tools for implementing policies • From user or application perspective • See only fixed OS policies • Defaults may not be the best policies

  3. Policies and Mechanisms • Definitions • Policies are schemes for deciding what to do • Mechanisms are tools for implementing policies • From user or application perspective • See only fixed OS policies • Defaults may not be the best policies • How can we transform existing OS policies into useful mechanisms for higher-level services?

  4. From Policies to Mechanisms • Information empowers transformation Application OS - Policies

  5. From Policies to Mechanisms • Information empowers transformation Application OS - Policies (infokernel) small changes

  6. Information From Policies to Mechanisms • Information empowers transformation Application algorithms, state OS - Policies (infokernel) small changes

  7. Information From Policies to Mechanisms • Information empowers transformation Application Library algorithms, state OS - Policies (infokernel) small changes

  8. Information From Policies to Mechanisms • Information empowers transformation exploit predict Application Library algorithms, state OS - Policies (infokernel) small changes

  9. Information From Policies to Mechanisms • Information empowers transformation exploit predict adapt Application Library algorithms, state OS - Policies (infokernel) small changes

  10. Information From Policies to Mechanisms • Information empowers transformation exploit predict adapt manipulate Application Library algorithms, state OS - Policies (infokernel) small changes

  11. Building Infokernels • Exploit information to control policies in Linux, BSD • File cache replacement • Convert 2Q into FIFO, LRU, MRU, LFU • File and directory placement • Control placement of data on disk • Disk scheduling • Enable background scheduling and free-block scheduling • Network scheduling • Convert TCP Reno into TCP Vegas congestion control

  12. Infokernel Observations • Policies can be manipulated efficiently • Ideas applicable to diverse policies • Several useful abstractions • Simple to implement

  13. Infokernel Questions • Exporting information • How much information should we export? • How do we access information efficiently? • Controlling policies • How can we emulate other policies? • How complex is the implementation? • Evaluation • How accurate is our policy control? • How much overhead is incurred? • What benefits can we achieve? • Other issues • Why not add new mechanisms for control? • Could greedy processes misbehave?

  14. Information Case Study: InfoReplace • Convert a source OS file cache replacement policy into a different target policy • Manipulate source policy by accessing cache pages favored by the target policy Application InfoReplace File Caching (infokernel)

  15. Repeated Sequential Scans • Imagine a 3 page cache and a 4 page file LRU Read page 1 1 Read page 2 1 2 Read page 3 1 2 3 Read page 4 2 3 4 Read page 1 3 4 1 Read page 2 4 1 2 … 0% Hit Rate

  16. Repeated Sequential Scans • Imagine a 3 page cache and a 4 page file LRU LIFO Read page 1 1 1 Read page 2 1 2 2 1 Read page 3 1 2 3 3 2 1 Read page 4 2 3 4 4 2 1 Read page 1 3 4 1 4 2 1 Read page 2 4 1 2 4 2 1 … 0% Hit Rate 50% Hit Rate

  17. Repeated Sequential Scans • Imagine a 3 page cache and a 4 page file LRU (source) LIFO (target) InfoReplace Read page 1 1 1 1 Read page 2 1 2 2 1 1 2 Read page 3 1 2 3 3 2 1 1 2 3 Read page 4 2 3 4 4 2 1 Read page 1 3 4 1 4 2 1 Read page 2 4 1 2 4 2 1 … 0% Hit Rate 50% Hit Rate

  18. Repeated Sequential Scans • Imagine a 3 page cache and a 4 page file LRU (source) LIFO (target) InfoReplace Read page 1 1 1 1 Read page 2 1 2 2 1 1 2 Read page 3 1 2 3 3 2 1 2 3 1 Read page 4 2 3 4 4 2 1 Read page 1 3 4 1 4 2 1 Read page 2 4 1 2 4 2 1 … 0% Hit Rate 50% Hit Rate

  19. Repeated Sequential Scans • Imagine a 3 page cache and a 4 page file LRU (source) LIFO (target) InfoReplace Read page 1 1 1 1 Read page 2 1 2 2 1 1 2 Read page 3 1 2 3 3 2 1 3 1 2 Read page 4 2 3 4 4 2 1 Read page 1 3 4 1 4 2 1 Read page 2 4 1 2 4 2 1 … 0% Hit Rate 50% Hit Rate

  20. Repeated Sequential Scans • Imagine a 3 page cache and a 4 page file LRU (source) LIFO (target) InfoReplace Read page 1 1 1 1 Read page 2 1 2 2 1 1 2 Read page 3 1 2 3 3 2 1 3 1 2 Read page 4 2 3 4 4 2 1 1 2 4 Read page 1 3 4 1 4 2 1 Read page 2 4 1 2 4 2 1 … 0% Hit Rate 50% Hit Rate

  21. Repeated Sequential Scans • Imagine a 3 page cache and a 4 page file LRU (source) LIFO (target) InfoReplace Read page 1 1 1 1 Read page 2 1 2 2 1 1 2 Read page 3 1 2 3 3 2 1 3 1 2 Read page 4 2 3 4 4 2 1 1 2 4 Read page 1 3 4 1 4 2 1 2 4 1 Read page 2 4 1 2 4 2 1 4 1 2 … 0% Hit Rate 50% Hit Rate 50% Hit Rate

  22. Infokernel Questions • Exporting information • How much information should we export? • How do we access information efficiently? • Controlling policies • How can we emulate other policies? • How complex is the implementation? • Evaluation • How accurate is our policy control? • How much overhead is incurred? • What benefits can we achieve? • Other issues • Why not add new mechanisms for control? • Could greedy processes misbehave?

  23. Amount of Information • InfoReplace • Could export exact file page cache structures • Specific to the source policy and the particular implementation • Kernel changes would force user-level modifications • Instead, export an abstract ordered list of file pages • Order indicates which pages are likely to be evicted • Possible to build a single library for many source policies • General observations • Information must be recast • Hide actual implementations • Export general abstractions applying to a range of policies

  24. Infokernel Questions • Exporting information • How much information should we export? • How do we access information efficiently? • Controlling policies • How can we emulate other policies? • How complex is the implementation? • Evaluation • How accurate is our policy control? • How much overhead is incurred? • What benefits can we achieve? • Other issues • Why not add new mechanisms for control? • Could greedy processes misbehave?

  25. Efficient Abstractions • InfoReplace • Victim list, next N victims from file cache page list • Faster to copy a small portion of the list • Victim counter, tracks additions to the victim list • Faster to determine when manipulation is necessary • General observations • Efficient optimizations for each case study • Reusable abstractions for exporting information • Lists, counters, notifiers

  26. Infokernel Questions • Exporting information • How much information should we export? • How do we access information efficiently? • Controlling policies • How can we emulate other policies? • How complex is the implementation? • Evaluation • How accurate is our policy control? • How much overhead is incurred? • What benefits can we achieve? • Other issues • Why not add new mechanisms for control? • Could greedy processes misbehave?

  27. Emulating Target Policies • InfoReplace • Interpose on file reads and writes • Simulate the target policy and compare its shadow file cache entries to the actual cache • Access pages that may be evicted but are favored under the target policy compare Simulated cache OS cache 1 2 3 3 2 1 manipulate

  28. Infokernel Questions • Exporting information • How much information should we export? • How do we access information efficiently? • Controlling policies • How can we emulate other policies? • How complex is the implementation? • Evaluation • How accurate is our policy control? • How much overhead is incurred? • What benefits can we achieve? • Other issues • Why not add new mechanisms for control? • Could greedy processes misbehave?

  29. Implementation Complexity • InfoReplace • Kernel: 109 lines of code • User-level: 1,361 lines of code • Most due to the simulation framework (720 lines) • About 100 lines of code for each target policy • General observations • Small code sizes for all case studies (< 1,900 lines) • Abstractions easily implemented in Linux and BSD

  30. Infokernel Questions • Exporting information • How much information should we export? • How do we access information efficiently? • Controlling policies • How can we emulate other policies? • How complex is the implementation? • Evaluation • How accurate is our policy control? • How much overhead is incurred? • What benefits can we achieve? • Other issues • Why not add new mechanisms for control? • Could greedy processes misbehave?

  31. InfoReplace Accuracy • Convert Linux 2Q into FIFO, LRU, MRU, LFU • Workload varies recency and frequency of access to a file • Compare pages in actual and simulated caches • Accuracy is high except for MRU, which is opposed to 2Q

  32. Infokernel Questions • Exporting information • How much information should we export? • How do we access information efficiently? • Controlling policies • How can we emulate other policies? • How complex is the implementation? • Evaluation • How accurate is our policy control? • How much overhead is incurred? • What benefits can we achieve? • Other issues • Why not add new mechanisms for control? • Could greedy processes misbehave?

  33. InfoReplace Overhead • Convert Linux 2Q into FIFO, LRU, MRU, LFU • Overheads are small except LFU which is expensive to simulate • Even LFU overhead is acceptable if we can gain just 1% in hit rate

  34. Infokernel Questions • Exporting information • How much information should we export? • How do we access information efficiently? • Controlling policies • How can we emulate other policies? • How complex is the implementation? • Evaluation • How accurate is our policy control? • How much overhead is incurred? • What benefits can we achieve? • Other issues • Why not add new mechanisms for control? • Could greedy processes misbehave?

  35. InfoReplace Workload Benefits • Convert Linux 2Q policy into PinRange • Pin the upper levels of a database index tree in memory • Workload of random index lookups • Level 6 hit rate increases under PinRange • Run time decreases 22%

  36. InfoReplace Workload Benefits • Convert Linux 2Q policy into PinRange • Pin the upper levels of a database index tree in memory • Workload of random index lookups • Level 6 hit rate increases under PinRange • Run time decreases 22%

  37. Infokernel Questions • Exporting information • How much information should we export? • How do we access information efficiently? • Controlling policies • How can we emulate other policies? • How complex is the implementation? • Evaluation • How accurate is our policy control? • How much overhead is incurred? • What benefits can we achieve? • Other issues • Why not add new mechanisms for control? • Could greedy processes misbehave?

  38. Adding Mechanisms • InfoReplace • Could build several cache policies into the OS • Difficult to supply the right policy for every application • General observations • Infeasible to deliver all policies for all applications • Provide abstract tools for policy customization

  39. Infokernel Questions • Exporting information • How much information should we export? • How do we access information efficiently? • Controlling policies • How can we emulate other policies? • How complex is the implementation? • Evaluation • How accurate is our policy control? • How much overhead is incurred? • What benefits can we achieve? • Other issues • Why not add new mechanisms for control? • Could greedy processes misbehave?

  40. Greedy Processes • InfoReplace • A process could monopolize the file cache • Such activity was possible before • General observations • Stresses the role of the OS to arbitrate resources • No new mechanisms, so no new OS responsibilities

  41. Information Case Study: InfoFreeSched • Provide free-block scheduling on a source disk scheduling policy • Manipulate source policy by carefully introducing background requests Application InfoFreeSched Disk Sched (infokernel)

  42. InfoFreeSched Abstractions • Name of disk scheduling policy • Determine where new requests will be placed • Disk request list • Notifier for changes to disk request list • Faster to recognize new scheduling opportunities • Timers for disk accesses • Build an approximate disk model based on logical block distances

  43. InfoFreeSched Operation • Lack complete control over disk request queue • Limited by source policy and existing requests

  44. InfoFreeSched Operation • Lack complete control over disk request queue • Limited by source policy and existing requests • Obtain current list of disk requests

  45. InfoFreeSched Operation • Lack complete control over disk request queue • Limited by source policy and existing requests • Obtain current list of disk requests • For each background request

  46. InfoFreeSched Operation • Lack complete control over disk request queue • Limited by source policy and existing requests • Obtain current list of disk requests • For each background request • Determine its queue position

  47. InfoFreeSched Operation • Lack complete control over disk request queue • Limited by source policy and existing requests • Obtain current list of disk requests • For each background request • Determine its queue position • Calculate effect on foreground using disk model

  48. InfoFreeSched Operation • Lack complete control over disk request queue • Limited by source policy and existing requests • Obtain current list of disk requests • For each background request • Determine its queue position • Calculate effect on foreground using disk model • Insert request if impact is minimal

  49. InfoFreeSched Workload Benefits • Workload of foreground and background random read requests • Increase overall bandwidth with minimal impact on foreground

  50. Conclusions • Infokernels • Can efficiently manipulate OS polices • Apply to a diverse set of policies • Can be built from several useful abstractions • Are simple to implement

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