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Page frame reclaiming algorithms Solved
Introduction
In this assignment you will explore some of the page frame reclaiming algorithms that you have read about.
1 Pick one by random
To set a standard and to see if we actually do something useful we rst implement the algorithm where we select a frame by random. If we later experiment with very complicated algorithms we hope that they behave better than the random, or why would we else bother to implement something complicated.
Create a le random.c and open up your favorite editor. Don’t use an IDE that hide all the complicated things, use a regular text editor and do the compilation by hand from a command shell.
1.1 a random sequence
The rst thing we shall do is to generate a random sequence of page identiers. This sequence is the sequence of page references that we are going to use in our benchmark of the algorithm. We could have generated these references on the y but we need them when we implement the optimal strategy so we might as well do it now.
Below is you rst go at the random.c program. It will only write out a sequence of random numbers but it’s only a start. Compile the code and do a test run to see that it works.
#include <stdio .h
#include <stdlib .h #include <math .h
#include <assert .h
void i n i t ( int ∗sequence , int refs , int pages ) {
for ( int i = 0; i<refs ; i++) {
sequence [ i ] = rand () %pages ;
}
}
int main( int argc , char ∗argv [ ] ) {
/∗ could be command line arguments ∗/ int refs = 10; int pages = 100; int ∗sequence = ( int ∗) malloc ( refs ∗sizeof ( int ) ) ; i n i t ( sequence , refs , pages );
/∗ a small experiment to show that i t works ∗/ for ( int i = 1; i < refs ; i++) { printf (" , %d" , sequence [ i ] ) ;
} printf ("\n" );
return 0;
}
So we have a random sequence of references but we need to make things a bit more interesting. The more advanced algorithms are based on the fact that page references will probably not be completely randomized. They make use of the fact that some pages are referenced more often compared to other pages etc. To make the sequence look more like a real life sequence we change the initialization so that it will produce a sequence where 20% of the pages are referenced 80% of the time (spatial locality).
/∗ 20% of the pages w i l l have 80% of the references ∗/
#define HIGH 20 #define FREQ 80 void i n i t ( int ∗sequence , int refs , int pages ) { int high = ( int )( pages ∗(( float )HIGH/100));
for ( int i = 0; i<refs ; i++) { if ( rand()%100 < FREQ) { /∗ the frequently case ∗/
sequence [ i ] = rand()%high ;
} else {
/∗ the less frequently case ∗/ sequence [ i ] = high + rand()%( pages − high );
}
}
}
Note that this is a very crude way of approximating a sequence of page references. There is for example no temporal locality that would make it more likely that we reference pages that we have recently referenced. To capture this we would have to include a memory in the generator so that it had a preference for say the last ten pages rather than jumping to a random page.
The model we have will do for our simple experiment but to do a better evaluation one would need a better approximation and of course actual data from real executions.
1.2 keep track of the pages
In our experiments we will not swap pages in our out but simply keep track of how many time we would have to do it if we actually used the algorithms in a real system. We can therefore keep things quite simple and only track if a page is allocated or not. We will keep a page table with one entry per page and each entry, a page table entry, will tell us if a page is in memory or not (the presence bit).
typedef struct pte {
int present ;
} pte ;
In the main procedure we allocate memory for the page table. If we know that we always will use a page table of size 100 we can allocated it on the stack using the following construct (with PAGES de ned as a macro). pte table [PAGES] ;
If we rather have the number of pages being given at run-time we need to allocate the structure on the heap using a call to malloc(). The same
pte ∗ table = ( pte ∗) malloc ( pages∗sizeof ( pte ) ) ;
1.3 the simulation
Now we’re ready to do the simulation and we will simulate what would happen in a system with a given set of frames. As the number of frames increase we will have room for more of the pages and when we have as many frames as we have pages there will be no swapping at all.
This is the structure of the benchmark, we compute a increment, the number of pages divided by the number of data points, and then run the simulation for a growing number of frames. Keep the output as shown, we will later process the data and produce a nice graph. The procedure clear_page_table(int *page_table, int pages) should run through the page table and set the present bits to zero. Complete the code with SAMPLES as a de ned macro set to 20.
printf ("# This is a benchmark of random replacement\n" ); printf ("# %d page references \n" , refs ); printf ("# %d pages \n" , pages ); printf ("#\n#\n#frames\ tratio \n" );
/∗ frames is the size of the memory in frames ∗/ int frames ; int incr = pages/SAMPLES; for ( frames = incr ; frames <= pages ; frames += incr ) {
/∗ clear page tables entries ∗/ clear_page_table ( table , pages ); int hits = simulate ( sequence , table , refs , frames , pages ); float ratio = ( float ) hits / refs ;
printf ("%d\t%.2 f \n" , frames , ratio );
}
The clear_page_table() is easily de ned as below.
void clear_page_table ( pte ∗page_table , int pages ) { for ( int i =0; i < pages ; i++) {
page_table [ i ] . present = 0;
}
}
In the heart of the simulator we read the references one by one to determine if it would have given us a hit in the memory or a page fault. If we have a hit we’re happy, but if not we have to do some more work. If we still have free frames we simply mark the page as being in memory but if the number of allocated pages is equal to the available number of frames we need to evict a page.
int simulate ( int ∗seq , pte ∗table , int refs , int frms , int pgs ) {
int hits = 0; int allocated = 0; int i ;
for ( i = 0; i < refs ; i++) {
int next = seq [ i ] ;
pte ∗entry = &table [ next ] ;
if ( entry−present == 1) {
hits++;
} else { if ( allocated < frms ) { allocated++;
entry−present = 1;
} else {
pte ∗ evict ;
do {
int rnd = rand()%pgs ; evict = &table [ rnd ] ; } while( evict−present != 1);
evict−present = 0; entry−present = 1;
}
}
}
return hits ;
}
In this simulation we will select a page by random. We will not be very e cient in this process (why?) but for our purposes its ne. We simply generate a random page number and check if it’s in memory. When we nd a page we simply mark it as swapped and mark the new page as in memory.
1.4 generating a graph
If you have the program up and running you should be able to able to generate a sequence of data points. This is all ne but we would like to generate a nice graph on the y.
# This is a benchmark of random replacement policy
# 100000 page references
# 100 pages
#
#
#frames ratio
5 0.13
10 0.28
15 0.41 :
The rst thing we do is to write the output to a le. We could have written our program in a way where it opens and writes the data to the le but a simpler way is to use the shell to do it for us. Try the following in your shell:
$ gcc random.c -o random
$ ./random random.dat
$ more random.dat
So now we have a le called random.dat with the data that we want to plot. Now in a shell run the program gnuplot, if it’s not installed it should be a 2 min job to install it. If gnuplot starts, try the following:
gnuplot plot "random.dat" u 1:2
If everything works you should see a window with a nice graph of the data points. You have just told gnuplot to plot the data in the le random.dat using the columns 1 and 2. Gnuplot will ignore all lines starting with a # so this is why we generated the output like we did.
You can explore gnuplot using the command line but after a while you want to write a script that generates the plot that you want to have. Save the following in a le called random.p.
# Gnuplot script for plotting data in file "random.dat" set terminal png set output "random.png" set title "Page replacement using random policy" set key right center
set xlabel "frms in memory" set ylabel "hit ratio"
set xrange [0:100] set yrange [0:1] plot "random.dat" u 1:2 w linespoints title "random"
You can now generate the le random.png by giving the script le to gnuplot as a command line argument.
$ gnuplot random.p
Figure 1: The random data in a nice graph
.
Gnuplot is a very good tool when you want to generate nice graphs from data in text les. It is not a program to do statistical calculations of the data so it requires that you can generate the data that you want to turn into a graph.
The gure 1 includes the graph in this document that is prepared using LATEX. If you have not learned how to use LATEXit’s high time to do so. It’s a bit of a learning curve but well worth it once you start working on your thesis.
2 The optimal solution
In order to estimate how well our random solution works we implement an optimal solution, even though we know that the optimal solution is not something that we will be able to use in practice. The optimal solution will throw out the page that will not be used for the longest period in time. We can determine this since we have the complete sequence of requests (something that we would of course not have in a real situation.
The optimal solution is quite easy to implement but our implementation will not be particularly e cient. When it is time to evict a page we will simply go through all pages in memory and check how far down the sequence of references that it is found. The page with the highest value is selected.
pte ∗ evict ;
/∗ i n i t i a l values ∗/
int sofar = 0; int candidate = pgs ;
for ( int c = 0; c < pgs ; c++) { if ( table [ c ] . present == 1) { /∗ the page is allocated ∗/ int dist = 0; while( seq [ i+dist ] != c && i+dist < refs ) { dist++;
}
if ( dist sofar ) {
candidate = c ;
sofar = dist ;
}
}
}
evict = &table [ candidate ] ;
evict−present = 0; entry−present = 1;
Make a copy of random.c called optimal.c and do the following changes to the simulation() procedure.
It is quite fun challenge to nd a more e cient algorithm to implement the optimal solution. It is obvious that it is a waste of time to calculate the distance of all pages every time we have a page fault. We should be able to keep a list of all the pages in memory, ordered by the next position in the sequence. It’s a bit tricky to gure out how to do it but the solution is quite simple once you get it right. The reward is not so much that we will cut the time for our benchmark in half but the satisfaction of guring out how to do it.
2.1 how to proceed
If we look at the data we see that there is certainly some room for improvement. Even if the optimal policy does not give us a system without page faults the reduced number of faults would result in a dramatic increase in performance. If we can implement something that is only half-way towards the optimal policy much would be gained.
3 Least Recently Used
We will rst implement something that works quite well, the least recently used policy. The only problem with this policy is that it is quite costly and it is important to understand why this is.
3.1 a list of the last referenced pages
Let’s keep a list of the last referenced pages, the ones that are in memory. We will keep the list ordered and have the least recently used page rst in the list and the most recently referenced page last in the list. When we reference a page that is in memory the page is also in the list. We then move the page to the end of the list to keep the list ordered. If the memory is full we simply evict the page that is the rst page in the list since this page is the least recently used.
We could create a new data structure to keep track of the list but we will keep things simple and extend the page table entry to also keep a next and previous pointer. These pointers are used if the page is present in memory and thus linked in the list.
typedef struct pte {
int id ; int present ; struct pte ∗next ; struct pte ∗prev ;
} pte ;
In the simulation we keep track of the rst and last element of the list. Keeping track of the last element will allow us to quickly add a new entry to the end of the list. Keeping the list double linked allows us quickly unlink an entry.
We can change the simulate() procedure and the structure would look something like follows:
int simulate ( int ∗seq , pte ∗table , int refs , int frames , int pgs ) {
int hits = 0; int allocated = 0;
pte ∗ f i r s t = NULL; pte ∗ last = NULL;
for ( int i = 0; i < refs ; i++) {
int next = seq [ i ] ; pte ∗entry = &table [ next ] ;
if ( entry−present == 1) { hits++;
:
/∗ unlink entry and place l a s t ∗/
:
} else { if ( allocated < frames ) {
allocated++;
entry−present = 1; entry−prev = last ; entry−next = NULL;
:
/∗ place entry l a s t ∗/
:
} else {
pte ∗ evict ;
evict = f i r s t ; f i r s t = evict−next ; evict−present = 0;
entry−present = 1; entry−prev = last ; entry−next = NULL;
:
/∗ place entry l a s t ∗/
:
}
}
}
return hits ;
}
The errors you will encounter when you implement the procedure is the corner cases. It’s straight forward to unlink an entry if its in the middle of a list but what happens if its the rst item in the list? What if it’s the only entry in the list? You will get it right but do use a pen and paper and make some drawings of how the pointers have changed.
3.2 much better but costly
If you get the data from the LRU implementation you should see that it is better than the random selection. It could be even better if the sequence of references had shown more temporal locality. The problem of the LRU algorithm is that it will cost you even if you don’t have a page fault. If you look at the implementation of the random algorithm you will see that hardly anything is done when we have a page hit, we increment a counter but this is only for our purposes of collecting some statistics. Compare this with the LRU implementation where a double linked list must be manipulated.
We can not expect to reduce the number of page faults much more than what we have with the LRU algorithm (apart from having good luck) but our hope is to stay close to LRU as possible even if we reduce the amount of work done when we have page hit.
4 The clock algorithm
The clock algorithm is a clever algorithm that could be explained in terms of a list similar to the one used in the LRU algorithm. The di erence is that the list is not updated with each reference. It becomes a simple FIFO queue of entries which does not help us very much.
The trick is that we keep an extra ag in the page table entry that keeps track of if the page has been referenced since it was last cleared. If the page that we are about to evict has the reference ag set it is placed last in the list so it is given a second chance.
If we will frequently will move entries from the beginning of the list to the end of the list we might as well let the entries form a ring where wee keep track of where the list starts. This is why it is called the clock algorithm.
4.1 a single linked list
It turns out that we will be ne with a single linked circular list. We will not unlink a random entry but only the rst entry in the ring and this is much easier if the list is only single linked. typedef struct pte {
int id ; int present ; int referenced ; struct pte ∗next ;
} pte ;
Our clock handle will point to the last entry in the circular list so the entry which is a candidate for eviction will be one entry ahead. The simulator now has the following structure.
int simulate ( int ∗seq , pte ∗table , int refs , int frms , int pgs ) {
int hits = 0; int allocated = 0; pte ∗ last = NULL; for ( int i = 0; i < refs ; i++) {
int next = seq [ i ] ; pte ∗entry = &table [ next ] ;
if ( entry−present == 1) {
entry−referenced = 1;
hits++;
} else { if ( allocated < frms ) { allocated++;
entry−present = 1;
:
/∗ place the entry l a s t in the l i s t ∗/
:
} else { pte ∗cand = last −next ;
while(cand−referenced != 0 ) {
cand−referenced = 0; last = cand ;
cand = cand−next ;
}
cand−present = 0; cand−referenced = 0;
entry−present = 1;
entry−referenced = 0;
:
/∗ place the entry l a s t in the l i s t ∗/
:
}
}
}
return hits ;
}
As you see there are only one tricky case - where we are still building the ring. We have to cover the corner case were there are no entries in the ring and we’re adding the rst one. Apart from this the implementation is quite simple. If you look at the gures generated you will see that it sometimes even outperforms LRU (but this is more based on luck).
Take a look at the code - what do we need to do when we have a page hit? As you see we are in pair with LRU but with a very limited amount of extra work. If the updating of the page table can be done in hardware the overhead is negligible.
4.2 problems and extensions
All though the clock algorithms works ne in our benchmark, reality is often more complicated. What will happen if all of the pages are referenced? We will of course traverse the ring and reset the reference ags and then evict a page that was actually referenced since the last page fault. If we would have a counter we would be able to at least choose a page that had been referenced the least number of times. A time stamp would be able to give us information on when the page was last used etc.
Using only one bit to keep track of frequently and recently used pages is of course an approximation. There are more advanced implementations but the problem is that wee must keep the overhead for page hits to a minimum. You can take some penalty when we have a page fault but we do not want to examine all frames in memory to decide which one to evict.
What ever we do we need to remember that real life is more complicated than benchmarks. If we lure ourselves into thinking that the benchmark sequence is what real life sequences look like then we might optimize our system for the benchmarks rather than reality. To improve the implementation it might be more important to make the frequent case as fast as possible rather than trying to avoid a page fault at all costs.
In this assignment you will explore some of the page frame reclaiming algorithms that you have read about.
1 Pick one by random
To set a standard and to see if we actually do something useful we rst implement the algorithm where we select a frame by random. If we later experiment with very complicated algorithms we hope that they behave better than the random, or why would we else bother to implement something complicated.
Create a le random.c and open up your favorite editor. Don’t use an IDE that hide all the complicated things, use a regular text editor and do the compilation by hand from a command shell.
1.1 a random sequence
The rst thing we shall do is to generate a random sequence of page identiers. This sequence is the sequence of page references that we are going to use in our benchmark of the algorithm. We could have generated these references on the y but we need them when we implement the optimal strategy so we might as well do it now.
Below is you rst go at the random.c program. It will only write out a sequence of random numbers but it’s only a start. Compile the code and do a test run to see that it works.
#include <stdio .h
#include <stdlib .h #include <math .h
#include <assert .h
void i n i t ( int ∗sequence , int refs , int pages ) {
for ( int i = 0; i<refs ; i++) {
sequence [ i ] = rand () %pages ;
}
}
int main( int argc , char ∗argv [ ] ) {
/∗ could be command line arguments ∗/ int refs = 10; int pages = 100; int ∗sequence = ( int ∗) malloc ( refs ∗sizeof ( int ) ) ; i n i t ( sequence , refs , pages );
/∗ a small experiment to show that i t works ∗/ for ( int i = 1; i < refs ; i++) { printf (" , %d" , sequence [ i ] ) ;
} printf ("\n" );
return 0;
}
So we have a random sequence of references but we need to make things a bit more interesting. The more advanced algorithms are based on the fact that page references will probably not be completely randomized. They make use of the fact that some pages are referenced more often compared to other pages etc. To make the sequence look more like a real life sequence we change the initialization so that it will produce a sequence where 20% of the pages are referenced 80% of the time (spatial locality).
/∗ 20% of the pages w i l l have 80% of the references ∗/
#define HIGH 20 #define FREQ 80 void i n i t ( int ∗sequence , int refs , int pages ) { int high = ( int )( pages ∗(( float )HIGH/100));
for ( int i = 0; i<refs ; i++) { if ( rand()%100 < FREQ) { /∗ the frequently case ∗/
sequence [ i ] = rand()%high ;
} else {
/∗ the less frequently case ∗/ sequence [ i ] = high + rand()%( pages − high );
}
}
}
Note that this is a very crude way of approximating a sequence of page references. There is for example no temporal locality that would make it more likely that we reference pages that we have recently referenced. To capture this we would have to include a memory in the generator so that it had a preference for say the last ten pages rather than jumping to a random page.
The model we have will do for our simple experiment but to do a better evaluation one would need a better approximation and of course actual data from real executions.
1.2 keep track of the pages
In our experiments we will not swap pages in our out but simply keep track of how many time we would have to do it if we actually used the algorithms in a real system. We can therefore keep things quite simple and only track if a page is allocated or not. We will keep a page table with one entry per page and each entry, a page table entry, will tell us if a page is in memory or not (the presence bit).
typedef struct pte {
int present ;
} pte ;
In the main procedure we allocate memory for the page table. If we know that we always will use a page table of size 100 we can allocated it on the stack using the following construct (with PAGES de ned as a macro). pte table [PAGES] ;
If we rather have the number of pages being given at run-time we need to allocate the structure on the heap using a call to malloc(). The same
pte ∗ table = ( pte ∗) malloc ( pages∗sizeof ( pte ) ) ;
1.3 the simulation
Now we’re ready to do the simulation and we will simulate what would happen in a system with a given set of frames. As the number of frames increase we will have room for more of the pages and when we have as many frames as we have pages there will be no swapping at all.
This is the structure of the benchmark, we compute a increment, the number of pages divided by the number of data points, and then run the simulation for a growing number of frames. Keep the output as shown, we will later process the data and produce a nice graph. The procedure clear_page_table(int *page_table, int pages) should run through the page table and set the present bits to zero. Complete the code with SAMPLES as a de ned macro set to 20.
printf ("# This is a benchmark of random replacement\n" ); printf ("# %d page references \n" , refs ); printf ("# %d pages \n" , pages ); printf ("#\n#\n#frames\ tratio \n" );
/∗ frames is the size of the memory in frames ∗/ int frames ; int incr = pages/SAMPLES; for ( frames = incr ; frames <= pages ; frames += incr ) {
/∗ clear page tables entries ∗/ clear_page_table ( table , pages ); int hits = simulate ( sequence , table , refs , frames , pages ); float ratio = ( float ) hits / refs ;
printf ("%d\t%.2 f \n" , frames , ratio );
}
The clear_page_table() is easily de ned as below.
void clear_page_table ( pte ∗page_table , int pages ) { for ( int i =0; i < pages ; i++) {
page_table [ i ] . present = 0;
}
}
In the heart of the simulator we read the references one by one to determine if it would have given us a hit in the memory or a page fault. If we have a hit we’re happy, but if not we have to do some more work. If we still have free frames we simply mark the page as being in memory but if the number of allocated pages is equal to the available number of frames we need to evict a page.
int simulate ( int ∗seq , pte ∗table , int refs , int frms , int pgs ) {
int hits = 0; int allocated = 0; int i ;
for ( i = 0; i < refs ; i++) {
int next = seq [ i ] ;
pte ∗entry = &table [ next ] ;
if ( entry−present == 1) {
hits++;
} else { if ( allocated < frms ) { allocated++;
entry−present = 1;
} else {
pte ∗ evict ;
do {
int rnd = rand()%pgs ; evict = &table [ rnd ] ; } while( evict−present != 1);
evict−present = 0; entry−present = 1;
}
}
}
return hits ;
}
In this simulation we will select a page by random. We will not be very e cient in this process (why?) but for our purposes its ne. We simply generate a random page number and check if it’s in memory. When we nd a page we simply mark it as swapped and mark the new page as in memory.
1.4 generating a graph
If you have the program up and running you should be able to able to generate a sequence of data points. This is all ne but we would like to generate a nice graph on the y.
# This is a benchmark of random replacement policy
# 100000 page references
# 100 pages
#
#
#frames ratio
5 0.13
10 0.28
15 0.41 :
The rst thing we do is to write the output to a le. We could have written our program in a way where it opens and writes the data to the le but a simpler way is to use the shell to do it for us. Try the following in your shell:
$ gcc random.c -o random
$ ./random random.dat
$ more random.dat
So now we have a le called random.dat with the data that we want to plot. Now in a shell run the program gnuplot, if it’s not installed it should be a 2 min job to install it. If gnuplot starts, try the following:
gnuplot plot "random.dat" u 1:2
If everything works you should see a window with a nice graph of the data points. You have just told gnuplot to plot the data in the le random.dat using the columns 1 and 2. Gnuplot will ignore all lines starting with a # so this is why we generated the output like we did.
You can explore gnuplot using the command line but after a while you want to write a script that generates the plot that you want to have. Save the following in a le called random.p.
# Gnuplot script for plotting data in file "random.dat" set terminal png set output "random.png" set title "Page replacement using random policy" set key right center
set xlabel "frms in memory" set ylabel "hit ratio"
set xrange [0:100] set yrange [0:1] plot "random.dat" u 1:2 w linespoints title "random"
You can now generate the le random.png by giving the script le to gnuplot as a command line argument.
$ gnuplot random.p
Figure 1: The random data in a nice graph
.
Gnuplot is a very good tool when you want to generate nice graphs from data in text les. It is not a program to do statistical calculations of the data so it requires that you can generate the data that you want to turn into a graph.
The gure 1 includes the graph in this document that is prepared using LATEX. If you have not learned how to use LATEXit’s high time to do so. It’s a bit of a learning curve but well worth it once you start working on your thesis.
2 The optimal solution
In order to estimate how well our random solution works we implement an optimal solution, even though we know that the optimal solution is not something that we will be able to use in practice. The optimal solution will throw out the page that will not be used for the longest period in time. We can determine this since we have the complete sequence of requests (something that we would of course not have in a real situation.
The optimal solution is quite easy to implement but our implementation will not be particularly e cient. When it is time to evict a page we will simply go through all pages in memory and check how far down the sequence of references that it is found. The page with the highest value is selected.
pte ∗ evict ;
/∗ i n i t i a l values ∗/
int sofar = 0; int candidate = pgs ;
for ( int c = 0; c < pgs ; c++) { if ( table [ c ] . present == 1) { /∗ the page is allocated ∗/ int dist = 0; while( seq [ i+dist ] != c && i+dist < refs ) { dist++;
}
if ( dist sofar ) {
candidate = c ;
sofar = dist ;
}
}
}
evict = &table [ candidate ] ;
evict−present = 0; entry−present = 1;
Make a copy of random.c called optimal.c and do the following changes to the simulation() procedure.
It is quite fun challenge to nd a more e cient algorithm to implement the optimal solution. It is obvious that it is a waste of time to calculate the distance of all pages every time we have a page fault. We should be able to keep a list of all the pages in memory, ordered by the next position in the sequence. It’s a bit tricky to gure out how to do it but the solution is quite simple once you get it right. The reward is not so much that we will cut the time for our benchmark in half but the satisfaction of guring out how to do it.
2.1 how to proceed
If we look at the data we see that there is certainly some room for improvement. Even if the optimal policy does not give us a system without page faults the reduced number of faults would result in a dramatic increase in performance. If we can implement something that is only half-way towards the optimal policy much would be gained.
3 Least Recently Used
We will rst implement something that works quite well, the least recently used policy. The only problem with this policy is that it is quite costly and it is important to understand why this is.
3.1 a list of the last referenced pages
Let’s keep a list of the last referenced pages, the ones that are in memory. We will keep the list ordered and have the least recently used page rst in the list and the most recently referenced page last in the list. When we reference a page that is in memory the page is also in the list. We then move the page to the end of the list to keep the list ordered. If the memory is full we simply evict the page that is the rst page in the list since this page is the least recently used.
We could create a new data structure to keep track of the list but we will keep things simple and extend the page table entry to also keep a next and previous pointer. These pointers are used if the page is present in memory and thus linked in the list.
typedef struct pte {
int id ; int present ; struct pte ∗next ; struct pte ∗prev ;
} pte ;
In the simulation we keep track of the rst and last element of the list. Keeping track of the last element will allow us to quickly add a new entry to the end of the list. Keeping the list double linked allows us quickly unlink an entry.
We can change the simulate() procedure and the structure would look something like follows:
int simulate ( int ∗seq , pte ∗table , int refs , int frames , int pgs ) {
int hits = 0; int allocated = 0;
pte ∗ f i r s t = NULL; pte ∗ last = NULL;
for ( int i = 0; i < refs ; i++) {
int next = seq [ i ] ; pte ∗entry = &table [ next ] ;
if ( entry−present == 1) { hits++;
:
/∗ unlink entry and place l a s t ∗/
:
} else { if ( allocated < frames ) {
allocated++;
entry−present = 1; entry−prev = last ; entry−next = NULL;
:
/∗ place entry l a s t ∗/
:
} else {
pte ∗ evict ;
evict = f i r s t ; f i r s t = evict−next ; evict−present = 0;
entry−present = 1; entry−prev = last ; entry−next = NULL;
:
/∗ place entry l a s t ∗/
:
}
}
}
return hits ;
}
The errors you will encounter when you implement the procedure is the corner cases. It’s straight forward to unlink an entry if its in the middle of a list but what happens if its the rst item in the list? What if it’s the only entry in the list? You will get it right but do use a pen and paper and make some drawings of how the pointers have changed.
3.2 much better but costly
If you get the data from the LRU implementation you should see that it is better than the random selection. It could be even better if the sequence of references had shown more temporal locality. The problem of the LRU algorithm is that it will cost you even if you don’t have a page fault. If you look at the implementation of the random algorithm you will see that hardly anything is done when we have a page hit, we increment a counter but this is only for our purposes of collecting some statistics. Compare this with the LRU implementation where a double linked list must be manipulated.
We can not expect to reduce the number of page faults much more than what we have with the LRU algorithm (apart from having good luck) but our hope is to stay close to LRU as possible even if we reduce the amount of work done when we have page hit.
4 The clock algorithm
The clock algorithm is a clever algorithm that could be explained in terms of a list similar to the one used in the LRU algorithm. The di erence is that the list is not updated with each reference. It becomes a simple FIFO queue of entries which does not help us very much.
The trick is that we keep an extra ag in the page table entry that keeps track of if the page has been referenced since it was last cleared. If the page that we are about to evict has the reference ag set it is placed last in the list so it is given a second chance.
If we will frequently will move entries from the beginning of the list to the end of the list we might as well let the entries form a ring where wee keep track of where the list starts. This is why it is called the clock algorithm.
4.1 a single linked list
It turns out that we will be ne with a single linked circular list. We will not unlink a random entry but only the rst entry in the ring and this is much easier if the list is only single linked. typedef struct pte {
int id ; int present ; int referenced ; struct pte ∗next ;
} pte ;
Our clock handle will point to the last entry in the circular list so the entry which is a candidate for eviction will be one entry ahead. The simulator now has the following structure.
int simulate ( int ∗seq , pte ∗table , int refs , int frms , int pgs ) {
int hits = 0; int allocated = 0; pte ∗ last = NULL; for ( int i = 0; i < refs ; i++) {
int next = seq [ i ] ; pte ∗entry = &table [ next ] ;
if ( entry−present == 1) {
entry−referenced = 1;
hits++;
} else { if ( allocated < frms ) { allocated++;
entry−present = 1;
:
/∗ place the entry l a s t in the l i s t ∗/
:
} else { pte ∗cand = last −next ;
while(cand−referenced != 0 ) {
cand−referenced = 0; last = cand ;
cand = cand−next ;
}
cand−present = 0; cand−referenced = 0;
entry−present = 1;
entry−referenced = 0;
:
/∗ place the entry l a s t in the l i s t ∗/
:
}
}
}
return hits ;
}
As you see there are only one tricky case - where we are still building the ring. We have to cover the corner case were there are no entries in the ring and we’re adding the rst one. Apart from this the implementation is quite simple. If you look at the gures generated you will see that it sometimes even outperforms LRU (but this is more based on luck).
Take a look at the code - what do we need to do when we have a page hit? As you see we are in pair with LRU but with a very limited amount of extra work. If the updating of the page table can be done in hardware the overhead is negligible.
4.2 problems and extensions
All though the clock algorithms works ne in our benchmark, reality is often more complicated. What will happen if all of the pages are referenced? We will of course traverse the ring and reset the reference ags and then evict a page that was actually referenced since the last page fault. If we would have a counter we would be able to at least choose a page that had been referenced the least number of times. A time stamp would be able to give us information on when the page was last used etc.
Using only one bit to keep track of frequently and recently used pages is of course an approximation. There are more advanced implementations but the problem is that wee must keep the overhead for page hits to a minimum. You can take some penalty when we have a page fault but we do not want to examine all frames in memory to decide which one to evict.
What ever we do we need to remember that real life is more complicated than benchmarks. If we lure ourselves into thinking that the benchmark sequence is what real life sequences look like then we might optimize our system for the benchmarks rather than reality. To improve the implementation it might be more important to make the frequent case as fast as possible rather than trying to avoid a page fault at all costs.
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