Operating Systems

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2.3.4: Sleep and Wakeup

Remark: Tanenbaum does both busy waiting (as above) and blocking (process switching) solutions. We will only do busy waiting, which is easier. Sleep and Wakeup are the simplest blocking primitives. Sleep voluntarily blocks the process and wakeup unblocks a sleeping process. We will not cover these.

Homework: Explain the difference between busy waiting and blocking process synchronization.

2.3.5: Semaphores

Remark: Tannenbaum use the term semaphore only for blocking solutions. I will use the term for our busy waiting solutions. Others call our solutions spin locks.

P and V and Semaphores

The entry code is often called P and the exit code V. Thus the critical section problem is to write P and V so that

loop forever
  1. Mutual exclusion.
  2. No speed assumptions.
  3. No blocking by processes in NCS.
  4. Forward progress (my weakened version of Tanenbaum's last condition).

Note that I use indenting carefully and hence do not need (and sometimes omit) the braces {} used in languages like C or java.

A binary semaphore abstracts the TAS solution we gave for the critical section problem.

The above code is not real, i.e., it is not an implementation of P. It is, instead, a definition of the effect P is to have.

To repeat: for any number of processes, the critical section problem can be solved by

loop forever

The only specific solution we have seen for an arbitrary number of processes is the one just above with P(S) implemented via test and set.

Remark: Peterson's solution requires each process to know its processor number. The TAS soluton does not. Moreover the definition of P and V does not permit use of the processor number. Thus, strictly speaking Peterson did not provide an implementation of P and V. He did solve the critical section problem.

To solve other coordination problems we want to extend binary semaphores.

Both of the shortcomings can be overcome by not restricting ourselves to a binary variable, but instead define a generalized or counting semaphore.

These counting semaphores can solve what I call the semi-critical-section problem, where you premit up to k processes in the section. When k=1 we have the original critical-section problem.

initially S=k

loop forever
    SCS   <== semi-critical-section

Producer-consumer problem

Initially e=k, f=0 (counting semaphore); b=open (binary semaphore)

Producer                         Consumer

loop forever                     loop forever
    produce-item                     P(f)
    P(e)                             P(b); take item from buf; V(b)
    P(b); add item to buf; V(b)      V(e)
    V(f)                             consume-item

2.3.6: Mutexes

Remark: Whereas we use the term semaphore to mean binary semaphore and explicitly say generalized or counting semaphore for the positive integer version, Tanenbaum uses semaphore for the positive integer solution and mutex for the binary version. Also, as indicated above, for Tanenbaum semaphore/mutex implies a blocking primitive; whereas I use binary/counting semaphore for both busy-waiting and blocking implementations. Finally, remember that in this course we are studying only busy-waiting solutions.

My Terminology
Busy waitblock/switch
critical(binary) semaphore(binary) semaphore
semi-criticalcounting semaphorecounting semaphore
Tanenbaum's Terminology
Busy waitblock/switch
criticalenter/leave regionmutex
semi-criticalno namesemaphore

2.3.7: Monitors


2.3..8: Message Passing

Skipped. You can find some information on barriers in my lecture notes for a follow-on course (see in particular lecture #16).

2.4: Classical IPC Problems

2.4.1: The Dining Philosophers Problem

A classical problem from Dijkstra

What algorithm do you use for access to the shared resource (the forks)?

The purpose of mentioning the Dining Philosophers problem without giving the solution is to give a feel of what coordination problems are like. The book gives others as well. We are skipping these (again this material would be covered in a sequel course). If you are interested look, for example, here.

Homework: 31 and 32 (these have short answers but are not easy). Note that the problem refers to fig. 2-20, which is incorrect. It should be fig 2-33.

2.4.2: The Readers and Writers Problem

Quite useful in multiprocessor operating systems and database systems. The “easy way out” is to treat all processes as writers in which case the problem reduces to mutual exclusion (P and V). The disadvantage of the easy way out is that you give up reader concurrency. Again for more information see the web page referenced above.

2.4.3: The Sleeping Barber Problem


2.4A: Summary of 2.3 and 2.4

We began with a problem (wrong answer for x++ and x--) and used it to motivate the Critical Section Problem for which we provided a (software) solution.

We then defined (binary) Semaphores and showed that a Semaphore easily solves the critical section problem and doesn't require knowledge of how many processes are competing for the critical section. We gave an implementation using Test-and-Set.

We then gave an operational definition of Semaphore (which is not an implementation) and morphed this definition to obtain a Counting (or Generalized) Semaphore, for which we gave NO implementation. I asserted that a counting semaphore can be implemented using 2 binary semaphores and gave a reference.

We defined the Readers/Writers (or Bounded Buffer) Problem and showed that it can be solved using counting semaphores (and binary semaphores, which are a special case).

Finally we briefly discussed some classical problem, but did not give (full) solutions.

2.5: Process Scheduling

Scheduling processes on the processor is often called “process scheduling” or simply “scheduling”.

The objectives of a good scheduling policy include

Recall the basic diagram describing process states

For now we are discussing short-term scheduling, i.e., the arcs connecting running <--> ready.

Medium term scheduling is discussed later.


It is important to distinguish preemptive from non-preemptive scheduling algorithms.

Deadline scheduling

This is used for real time systems. The objective of the scheduler is to find a schedule for all the tasks (there are a fixed set of tasks) so that each meets its deadline. The run time of each task is known in advance.

Actually it is more complicated.

We do not cover deadline scheduling in this course.

The name game

There is an amazing inconsistency in naming the different (short-term) scheduling algorithms. Over the years I have used primarily 4 books: In chronological order they are Finkel, Deitel, Silberschatz, and Tanenbaum. The table just below illustrates the name game for these four books. After the table we discuss each scheduling policy in turn.

Finkel  Deitel  Silbershatz Tanenbaum
FCFS    FIFO    FCFS        FCFS
RR      RR      RR          RR
PS      **      PS          PS
SRR     **      SRR         **    not in tanenbaum
SPN     SJF     SJF         SJF
PSPN    SRT     PSJF/SRTF   --    unnamed in tanenbaum
HPRN    HRN     **          **    not in tanenbaum
**      **      MLQ         **    only in silbershatz
FB      MLFQ    MLFQ        MQ

Remark: For an alternate organization of the scheduling algorithms (due to Eric Freudenthal and presented by him Fall 2002) click here.

First Come First Served (FCFS, FIFO, FCFS, --)

If the OS “doesn't” schedule, it still needs to store the list of ready processes in some manner. If it is a queue you get FCFS. If it is a stack (strange), you get LCFS. Perhaps you could get some sort of random policy as well.

Round Robin (RR, RR, RR, RR)

Homework: 26, 35, 38.

Homework: Give an argument favoring a large quantum; give an argument favoring a small quantum.

ProcessCPU TimeCreation Time
Homework: (Remind me to discuss this last one in class next time): Consider the set of processes in the table below. When does each process finish if RR scheduling is used with q=1, if q=2, if q=3, if q=100. First assume (unrealistically) that context switch time is zero. Then assume it is .1. Each process performs no I/O (i.e., no process ever blocks). All times are in milliseconds. The CPU time is the total time required for the process (excluding any context switch time). The creation time is the time when the process is created. So P1 is created when the problem begins and P3 is created 5 milliseconds later. If two processes have equal priority (in RR this means if thy both enter the ready state at the same cycle), we give priority (in RR this means place first on the queue) to the process with the earliest creation time. If they also have the same creation time, then we give priority to the process with the lower number.

Note: Do the homework problem assigned at the end of last lecture.

Processor Sharing (PS, **, PS, PS)

Merge the ready and running states and permit all ready jobs to be run at once. However, the processor slows down so that when n jobs are running at once, each progresses at a speed 1/n as fast as it would if it were running alone.

Homework: 34.

Variants of Round Robin

Priority Scheduling

Each job is assigned a priority (externally, perhaps by charging more for higher priority) and the highest priority ready job is run.

Priority aging

As a job is waiting, raise its priority so eventually it will have the maximum priority.