177 lines
7.7 KiB
Markdown
177 lines
7.7 KiB
Markdown
13/11/20
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## Virtual Memory & Potential Problems
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#### Page Replacement
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##### Second chance
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> * If a page at the front of the list has **not been referenced** it is **evicted**
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> * If the reference bit is set, the page is **placed at the end** of the list and it's reference bit is unset.
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> * This works better than FIFO and is relatively simple
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> * **Costly to implement** as the list is constantly changing.
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> * Can degrade to FIFO if all pages were initially referenced.
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##### Clock Replacement Algorithm
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> The second chance implementation can be improved by **maintaining the page list as a circle**
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> * A **pointer** points to the last visited page.
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> * In this form the algorithm is called the one handed clock
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> * It is faster, but can still be **slow if the list is long**.
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> * The **time spent** on **maintaining** the list is **reduced**.
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##### Not Recently Used (NRU)
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> For NRU, **referenced** and **modified** bits are kept in the page table
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> * Referenced bits are set to 0 at the start, and **reset periodically**
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>
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> There are four different **page types** in NRU:
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> 1. Not referenced recently, not modified
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> 2. Not referenced recently, modified
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> 3. Referenced recently, not modified
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> 4. Referenced recently, modified
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> **Page table entries** are inspected upon every **page fault**. This could be implemented in the following way
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>
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> 1. Find a page from **class 0** to be removed.
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> 2. If step 1 fails, scan again looking for **class 1**. During this scan we set the reference bit to 0 on each page that is bypassed
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> 3. If step 2 fails, start again from step 1 (Now we should find pages from class 2&3 have been moved to class 0 or 1)
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>
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> The NRU algorithm provides a **reasonable performance** and is easy to understand and implement.
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##### Least Used Recently
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> Least recently used **evicts the page** that has **not be used for the longest**
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>
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> * The OS must keep track of when a page was last used.
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> * Every page table entry contains a field for the counter
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> * This is **not cheap to implement** as we need to maintain a **list of pages** which are **sorted** in the order in which they have been used.
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>
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> This algorithm can be **implemented in hardware** using a **counter** that is incremented after each instruction ...
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This will look familiar to the FIFO algorithm however, when a page is used, that is like its just come in.
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### Resident Set
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How many pages should be allocated to individual processes:
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* **Small resident sets** enable to store **more processes in memory** => improved CPU utilisation.
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* **Small resident sets** may result in **more page faults**
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* **Large resident sets** may **no longer reduce** the **page fault rate** (**diminishing returns**)
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A trade-off exists between the **sizes of the resident sets** and **system utilisation**.
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Resident set sizes may be **fixed** or **variable** (adjusted at run-time)
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* For **variable sized** resident sets, **replacement policies** can be:
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* **Local**: a page of the same process is replaced
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* **Global**: a page can be taken away from a **different process**
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* Variable sized sets require **careful evaluation of their size** when a **local scope** is used (often based on the **working set** or the **page fault rate**)
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### Working Set
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The **resident set** comprises the set of pages of the process that are in memory (they have a corresponding frame)
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The **working set** is a subset of the resident set that is actually needed for execution.
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* The **working set** $W(t, k)$ comprises the set of referenced pages in the last $k$ (working set window) **virtual time units for the process**.
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* $k$ can be defined as **memory references** or as **actual process time**
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* The set of most recent used pages
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* The set of pages used within a pre-specified time interval
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* The **working set size** can be used as a guide for the number of frames that should be allocated to a process.
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The working set is a **function of time** $t$:
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* Processes **move between localities**, hence, the pages that are included in the working set **change over time**
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* **Stable** intervals alternate with intervals of **rapid change**
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$|W(t,k)|$ is then a variable in time. Specifically:
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$$
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1\le |W(t,k)| \le min(k, N)
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$$
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where $N$ is the total number of pages of the process. All the maths is saying is that the size of the working set can be as small as **one** or as large as **all the pages in the process**.
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Choosing the right value for $k$ is important:
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* Too **small**: inaccurate, pages are missing
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* Too **large**: too many unused pages present
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* **Infinity**: all pages of the process are in the working set
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Working sets can be used to guide the **size of the resident sets**
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* Monitor the working set
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* Remove pages from the resident set that are not in the working set
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The working set is costly to maintain => **page fault frequency (PFF)** can be used as an approximation: $PFF\space\alpha\space k$
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* If the PFF is increased -> we need to increase $k$
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* If PFF is very low -> we could decrease $k$ to allow more processes to have more pages.
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#### Global Replacement
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> Global replacement policies can select frames from the entire set (they can be taken from other processes)
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>
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> * Frames are **allocated dynamically** to processes
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> * Processes cannot control their own page fault frequency. The PFF of one process is **influenced by other processes**.
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#### Local Replacement
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> Local replacement policies can only select frames that are allocated to the current process
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>
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> * Every process has a **fixed fraction of memory**
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> * The **locally oldest page** is not necessarily the **globally oldest page**
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Windows uses a variable approach with local replacement. Page replacement algorithms can use both policies.
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### Paging Daemon
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It is more efficient to **proactively** keep a number of **free pages** for **future page faults**
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* If not, we may have to **find a page** to evict and we **write it to the drive** (if its been modified) first when a page fault occurs.
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Many systems have a background process called a **paging daemon**.
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* This process **runs at periodic intervals**
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* It inspects the state of the frames and if too few frames are free, it **selects pages to evict** (using page replacement algorithms)
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Paging daemons can be combined with **buffering** (free and modified lists) => write the modified pages **but keep them in main memory** when possible.
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**Buffering**: a process that preemptively writes modified pages to the disk. That way when there's a page fault we don't lose the time taken to write to disk
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### Thrashing
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Assume **all available pages are in active use** and a new page needs to be loaded:
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* The page that will be evicted will have to be **reloaded soon afterwards**
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**Thrashing** occurs when pages are **swapped out** and then **loaded back in immediately**
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#### Causes of thrashing include:
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* The degree of multi-programming is too high i.e the total **demand** (the sum of all working sets sizes) **exceeds supply** (the available frames)
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* An individual process is allocated **too few pages**
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This can be prevented by **using good page replacement algorithms**, reducing the **degree of multi-programming** or adding more memory.
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The **page fault frequency** can be used to detect that a system is thrashing.
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> * CPU utilisation is too low => scheduler **increases degree of multi-programming**
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> * Frames are allocated to new processes and taken away from existing processes
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> * I/O requests are queued up as a consequence of page faults
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> This is a positive reinforcement cycle.
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And when all this comes together, its how memory management working in modern computers.
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