Operating Systems

**4.1.2: Multiprogramming with fixed partitions

Two goals of multiprogramming are to improve CPU utilization, by overlapping CPU and I/O, and to permit short jobs to finish quickly.

• This scheme was used by IBM for system 360 OS/MFT (multiprogramming with a fixed number of tasks).
• Can have a single input queue instead of one for each partition.
  • So that if there are no big jobs, one can use the big partition for little jobs.
  • But I don't think IBM did this.
  • Can think of the input queue(s) as the ready list(s) with a scheduling policy of FCFS in each partition.
• Each partition was monoprogrammed, the multiprogramming occurred across partitions.
• The partition boundaries are not movable (must reboot to move a job).
  • So the partitions are of fixed size.
  • MFT can have large internal fragmentation, i.e., wasted space inside a region of memory assigned to a process.
• Each process has a single “segment” (i.e., the virtual address space is contiguous). We will discuss segments later.
• The physical address space is also contiguous (i.e., the program is stored as one piece).
• No sharing of memory between process.
• No dynamic address translation.
• At load time must “establish addressability”.
  • That is, must set a base register to the location at which the process was loaded (the bottom of the partition).
  • The base register is part of the programmer visible register set.
  • This is an example of address translation during load time.
  • Also called relocation.
• Storage keys are adequate for protection (IBM method).
• Alternative protection method is base/limit registers.
• An advantage of base/limit is that it is easier to move a job.
• But MFT didn't move jobs so this disadvantage of storage keys is moot.
• Tanenbaum says a job was “run until it terminates. This must be wrong as that would mean monoprogramming.
• He probably means that jobs not swapped out and each queue is FCFS without preemption.

4.1.3: Modeling Multiprogramming (crudely)

• Consider a job that is unable to compute (i.e., it is waiting for I/O) a fraction p of the time.
• Then, with monoprogramming, the CPU utilization is 1-p.
• Note that p is often > .5 so CPU utilization is poor.
• But, if the probability that a job is waiting for I/O is p and n jobs are in memory, then the probability that all n are waiting for I/O is approximately pⁿ.
• So, with a multiprogramming level (MPL) of n, the CPU utilization is approximately 1-pⁿ.
• If p=.5 and n=4, then 1-pⁿ = 15/16, which is much better than 1/2, which would occur for monoprogramming (n=1).
• This is a crude model, but it is correct that increasing MPL does increase CPU utilization up to a point.
• The limitation is memory, which is why we discuss it here instead of process management. That is, we must have many jobs loaded at once, which means we must have enough memory for them. There are other issues as well and we will discuss them.
• Some of the CPU utilization is time spent in the OS executing context switches so the gains are not a great as the crude model predicts.

Homework: 1, 2 (typo in book; figure 4.21 seems irrelevant).

4.1.4: Analysis of Multiprogramming System Performance

Skipped

4.1.5: Relocation and Protection

Relocation was discussed as part of linker lab and at the beginning of this chapter. When done dynamically, a simple method is to have a base register whose value is added to every address by the hardware.

Similarly a limit register is checked by the hardware to be sure that the address (before the base register is added) is not bigger than the size of the program.

The base and limit register are set by the OS when the job starts.

4.2: Swapping

Moving the entire processes between disk and memory is called swapping.

Multiprogramming with Variable Partitions

• IBM OS/MVT (multiprogramming with a varying number of tasks).
  
  
• Also early PDP-10 OS.
  
  
• Job still has only one segment (as with MFT). That is, the virtual address space is contiguous.
  
  
• The physical address is also contiguous, that is, the process is stored as one piece in memory.
  
  
• The job can be of any size up to the size of the machine and the job size can change with time.
  
  
• A single ready list.
  
  
• A job can move (might be swapped back in a different place).
  
  
• This is dynamic address translation (during run time).
  
  
• Must perform an addition on every memory reference (i.e. on every address translation) to add the start address of the partition.
  
  
• Eliminates internal fragmentation.
  • Find a region the exact right size (leave a hole for the remainder).
  • Not quite true, can't get a piece with 10A755 bytes. Would get say 10A760. But internal fragmentation is much reduced compared to MFT. Indeed, we say that internal fragmentation has been eliminated.
  
  
• Introduces external fragmentation, i.e., holes outside any region of memory assigned to a process.
  
  
• What do you do if no hole is big enough for the request?
  • Can compactify
    • Transition from bar 3 to bar 4 in diagram below.
    • This is expensive.
    • Not suitable for real time (MIT ping pong).
  • Can swap out one process to bring in another, e.g., bars 5-6 and 6-7 in the diagram.
  
  
• There are more processes than holes. Why?
  • Because next to a process there might be a process or a hole but next to a hole there must be a process
  • So can have “runs” of processes but not of holes
  • If after a process one is equally likely to have a process or a hole, you get about twice as many processes as holes.
  
  
• Base and limit registers are used.
  • Storage keys not good since compactifying or moving would require changing many keys.
  • Storage keys might need a fine granularity to permit the boundaries to move by small amounts (to reduce internal fragmentation). Hence many keys would need to be changed.

Homework: 3

MVT Introduces the “Placement Question”

That is, which hole (partition) should one choose?

• Best fit, worst fit, first fit, circular first fit, quick fit, Buddy
  • Best fit doesn't waste big holes, but does leave slivers and is expensive to run.
  • Worst fit avoids slivers, but eliminates all big holes so a big job will require compaction. Even more expensive than best fit (best fit stops if it finds a perfect fit).
  • Quick fit keeps lists of some common sizes (but has other problems, see Tanenbaum).
  • Buddy system
    • Round request to next highest power of two (causes internal fragmentation).
    • Look in list of blocks this size (as with quick fit).
    • If list empty, go higher and split into buddies.
    • When returning coalesce with buddy.
    • Do splitting and coalescing recursively, i.e. keep coalescing until can't and keep splitting until successful.
    • See Tanenbaum for more details (or an algorithms book).
  
  
• A current favorite is circular first fit, also known as next fit.
  • Use the first hole that is big enough (first fit) but start looking where you left off last time.
  • Doesn't waste time constantly trying to use small holes that have failed before, but does tend to use many of the big holes, which can be a problem.
  
  
• Buddy comes with its own implementation. How about the others?

Homework: 5.

4.2.1: Memory Management with Bitmaps

Divide memory into blocks and associate a bit with each block, used to indicate if the corresponding block is free or allocated. To find a chunk of size N blocks need to find N consecutive bits indicating a free block.

The only design question is how much memory does one bit represent.

• Big: Serious internal fragmentation.
• Small: Many bits to store and process.

4.2.2: Memory Management with Linked Lists

• Each item on list gives the length and starting location of the corresponding region of memory and says whether it is a Hole or Process.
• The items on the list are not taken from the memory to be used by processes.
• Keep in order of starting address.
• Merge adjacent holes.
• Singly linked.

Memory Management using Boundary Tags

• Use the same memory for list items as for processes.
• Don't need an entry in linked list for blocks in use, just the avail blocks are linked.
• The avail blocks themselves are linked, not a node that points to an avail block.
• When a block is returned, we can look at the boundary tag of the adjacent blocks and see if they are avail. If so they must be merged with the returned block.
• For the blocks currently in use, just need a hole/process bit at each end and the length. Keep this in the block itself.
• We do not need to traverse the list when returning a block can use boundary tags to find predecessor.
• See Knuth, The Art of Computer Programming vol 1.

MVT also introduces the “Replacement Question”

That is, which victim should we swap out? Note that this is an example of the suspend arc mentioned in process scheduling.

We will study this question more when we discuss demand paging in which case we swap out part of a process.

Considerations in choosing a victim

• Cannot replace a job that is pinned, i.e. whose memory is tied down. For example, if Direct Memory Access (DMA) I/O is scheduled for this process, the job is pinned until the DMA is complete.
• Victim selection is a medium term scheduling decision
  • A job that has been in a wait state for a long time is a good candidate.
  • Often choose as a victim a job that has been in memory for a long time.
  • Another question is how long should it stay swapped out.
• For demand paging, where swaping out a page is not as drastic as swapping out a job, choosing the victim is an important memory management decision and we shall study several policies,

1. So far the schemes presented so far have had two properties:
   1. Each job is stored contiguously in memory. That is, the job is contiguous in physical addresses.
   2. Each job cannot use more memory than exists in the system. That is, the virtual addresses space cannot exceed the physical address space.
   
   
2. Tanenbaum now attacks the second item. I wish to do both and start with the first.
   
   
3. Tanenbaum (and most of the world) uses the term “paging” to mean what I call demand paging. This is unfortunate as it mixes together two concepts.
   1. Paging (dicing the address space) to solve the placement problem and essentially eliminate external fragmentation.
   2. Demand fetching, to permit the total memory requirements of all loaded jobs to exceed the size of physical memory.
   
   
4. Tanenbaum (and most of the world) uses the term virtual memory as a synonym for demand paging. Again I consider this unfortunate.
   1. Demand paging is a fine term and is quite descriptive.
   2. Virtual memory “should” be used in contrast with physical memory to describe any virtual to physical address translation.