Chapter 6:

Process Synchronization
 Dr. Zeeshan Rafi
PhD MIS, MPhil IT,
Former Software Engineer
Department of Computing and
Information Systems
Istanbul University, TR
KHAS University, TR
Module 6: Process Synchronization
• Background
• The Critical-Section Problem
• Peterson’s Solution
• Synchronization Hardware
• Semaphores
• Classic Problems of Synchronization
• Monitors
• Synchronization Examples
• Atomic Transactions
 Objectives
• To introduce the critical-section problem,
whose solutions can be used to ensure the
consistency of shared data
• To present both software and hardware
solutions of the critical-section problem
• To introduce the concept of an atomic
transaction and describe mechanisms to
ensure atomicity
 Background
• Concurrent access to shared data may result
in data inconsistency
• Maintaining data consistency requires
mechanisms to ensure the orderly execution
of cooperating processes
• Suppose that we wanted to provide a
solution to the consumer-producer problem
that fills all the buffers. We can do so by
having an integer count that keeps track of
the number of full buffers. Initially, count is
set to 0. It is incremented by the producer
after it produces a new buffer and is
decremented by the consumer after it
consumes a buffer.
 Producer
while (true) {

/* produce an item and put in

nextProduced */
while (count == BUFFER_SIZE)
; // do nothing
buffer [in] = nextProduced;
in = (in + 1) % BUFFER_SIZE;
count++;
}
 Consumer
while (true) {
while (count == 0)
; // do nothing
nextConsumed = buffer[out];
out = (out + 1) % BUFFER_SIZE;
count--;

/* consume the item in

nextConsumed
}
 Race Condition
• count++ could be implemented as
register1 = count
register1 = register1 + 1
count = register1
• count-- could be implemented as
register2 = count
register2 = register2 - 1
count = register2
• Consider this execution interleaving with “count = 5” initially:
S0: producer execute register1 = count {register1 =
5}
S1: producer execute register1 = register1 + 1
{register1 = 6}
S2: consumer execute register2 = count {register2 =
5}
S3: consumer execute register2 = register2 - 1
{register2 = 4}
S4: producer execute count = register1 {count = 6 }
S5: consumer execute count = register2 {count = 4}
Solution to Critical-Section Problem
1. Mutual Exclusion - If process Pi is executing in its critical section,
then no other processes can be executing in their critical sections
2. Progress - If no process is executing in its critical section and there
exist some processes that wish to enter their critical section, then
the selection of the processes that will enter the critical section
next cannot be postponed indefinitely
3. Bounded Waiting - A bound must exist on the number of times
that other processes are allowed to enter their critical sections
after a process has made a request to enter its critical section and
before that request is granted
 Assume that each process executes at a nonzero speed
 No assumption concerning relative speed of the N processes
 Peterson’s Solution
• Two process solution
• Assume that the LOAD and STORE
instructions are atomic; that is, cannot be
interrupted.
• The two processes share two variables:
– int turn;
– Boolean flag[2]
• The variable turn indicates whose turn it is
to enter the critical section.
• The flag array is used to indicate if a process
is ready to enter the critical section. flag[i] =
true implies that process Pi is ready!
Algorithm for Process Pi
do {
flag[i] = TRUE;
turn = j;
while (flag[j] && turn == j);
critical section
flag[i] = FALSE;
remainder section
} while (TRUE);
 Synchronization Hardware
• Many systems provide hardware support for
critical section code
• Uniprocessors – could disable interrupts
– Currently running code would execute without
preemption
– Generally too inefficient on multiprocessor systems
• Operating systems using this not broadly scalable
• Modern machines provide special atomic
hardware instructions
• Atomic = non-interruptable
– Either test memory word and set value
– Or swap contents of two memory words
 Solution to Critical-section Problem Using Locks

do {
acquire lock
critical section
release lock
remainder section
} while (TRUE);
 TestAndndSet Instruction
• Definition:

boolean TestAndSet (boolean *target)

{
boolean rv = *target;
*target = TRUE;
return rv:
}
 Solution using TestAndSet
• Shared boolean variable lock., initialized to false.
• Solution:

do {
while ( TestAndSet (&lock ))
; // do nothing

// critical section

lock = FALSE;

// remainder section

} while (TRUE);
 Swap Instruction
• Definition:

void Swap (boolean *a, boolean *b)

{
boolean temp = *a;
*a = *b;
*b = temp:
}
 Solution using Swap
• Shared Boolean variable lock initialized to FALSE; Each
process has a local Boolean variable key
• Solution:
do {
key = TRUE;
while ( key == TRUE)
Swap (&lock, &key );

// critical section

lock = FALSE;

// remainder section

} while (TRUE);
 Bounded-waiting Mutual Exclusion with TestandSet()

do {
waiting[i] = TRUE;
key = TRUE;
while (waiting[i] && key)
key = TestAndSet(&lock);
waiting[i] = FALSE;
// critical section
j = (i + 1) % n;
while ((j != i) && !waiting[j])
j = (j + 1) % n;
if (j == i)
lock = FALSE;
else
waiting[j] = FALSE;
// remainder section
} while (TRUE);
 Semaphore
• Synchronization tool that does not require busy waiting
• Semaphore S – integer variable
• Two standard operations modify S: wait() and signal()
– Originally called P() and V()
• Less complicated
• Can only be accessed via two indivisible (atomic) operations
– wait (S) {
while S <= 0
; // no-op
S--;
}
– signal (S) {
S++;
}
 Semaphore as General Synchronization Tool

• Counting semaphore – integer value can range over an unrestricted domain

• Binary semaphore – integer value can range only between 0
and 1; can be simpler to implement
– Also known as mutex locks
• Can implement a counting semaphore S as a binary semaphore
• Provides mutual exclusion
Semaphore mutex; // initialized to 1
do {
wait (mutex);
// Critical Section
signal (mutex);
// remainder section
} while (TRUE);
 Semaphore Implementation
• Must guarantee that no two processes can execute wait
() and signal () on the same semaphore at the same
time
• Thus, implementation becomes the critical section
problem where the wait and signal code are placed in
the crtical section.
– Could now have busy waiting in critical section
implementation
• But implementation code is short
• Little busy waiting if critical section rarely occupied
• Note that applications may spend lots of time in critical
sections and therefore this is not a good solution.
 Semaphore Implementation with no Busy waiting

• With each semaphore there is an associated

waiting queue. Each entry in a waiting queue
has two data items:
– value (of type integer)
– pointer to next record in the list

• Two operations:
– block – place the process invoking the operation
on the appropriate waiting queue.
– wakeup – remove one of processes in the
waiting queue and place it in the ready queue.
 Semaphore Implementation with no Busy waiting (Cont.)

• Implementation of wait:
wait(semaphore *S) {
S->value--;
if (S->value < 0) {
add this process to S->list;
block();
}
}
• Implementation of signal:

signal(semaphore *S) {
S->value++;
if (S->value <= 0) {
remove a process P from S->list;
wakeup(P);
}
}
 Deadlock and Starvation
• Deadlock – two or more processes are waiting indefinitely
for an event that can be caused by only one of the waiting
processes
• Let S and Q be two semaphores initialized to 1
P0 P1
wait (S); wait (Q);
wait (Q); wait (S);
. .
. .
. .
signal (S); signal (Q);
signal (Q); signal (S);

• Starvation – indefinite blocking. A process may never be

removed from the semaphore queue in which it is
suspended
• Priority Inversion - Scheduling problem when lower-
priority process holds a lock needed by higher-priority
process
 Classical Problems of Synchronization

• Bounded-Buffer Problem
• Readers and Writers Problem
• Dining-Philosophers Problem
 Bounded-Buffer Problem
• N buffers, each can hold one item
• Semaphore mutex initialized to the
value 1
• Semaphore full initialized to the value 0
• Semaphore empty initialized to the
value N.
Bounded Buffer Problem (Cont.)
• The structure of the producer process

do {

// produce an item in nextp

wait (empty);
wait (mutex);

// add the item to the buffer

signal (mutex);
signal (full);
} while (TRUE);
Bounded Buffer Problem (Cont.)
• The structure of the consumer process

do {
wait (full);
wait (mutex);

// remove an item from buffer to nextc

signal (mutex);
signal (empty);

// consume the item in nextc

} while (TRUE);
 Readers-Writers Problem
• A data set is shared among a number of
concurrent processes
– Readers – only read the data set; they do
not perform any updates
– Writers – can both read and write

• Problem – allow multiple readers to

read at the same time. Only one single
writer can access the shared data at the
same time
Readers-Writers Problem (Cont.)
• The structure of a writer process

do {
wait (wrt) ;

// writing is performed

signal (wrt) ;
} while (TRUE);
Readers-Writers Problem (Cont.)
• The structure of a reader process

do {
wait (mutex) ;
readcount ++ ;
if (readcount == 1)
wait (wrt) ;
signal (mutex)

// reading is performed

wait (mutex) ;
readcount - - ;
if (readcount == 0)
signal (wrt) ;
signal (mutex) ;
} while (TRUE);
 Dining-Philosophers Problem

• Shared data
– Bowl of rice (data set)
– Semaphore chopstick [5] initialized to 1
 Dining-Philosophers Problem
(Cont.)
• The structure of Philosopher i:

do {
wait ( chopstick[i] );
wait ( chopStick[ (i + 1) % 5] );

// eat

signal ( chopstick[i] );
signal (chopstick[ (i + 1) % 5] );

// think
 Problems with Semaphores
• Correct use of semaphore operations:

– signal (mutex) …. wait (mutex)

– wait (mutex) … wait (mutex)

– Omitting of wait (mutex) or signal

(mutex) (or both)
 Monitors
• A high-level abstraction that provides a convenient and effective mechanism
for process synchronization
• Only one process may be active within the monitor at a time

monitor monitor-name
{
// shared variable declarations
procedure P1 (…) { …. }
…

procedure Pn (…) {……}

Initialization code ( ….) { … }

…
}
}
Schematic view of a Monitor
 Condition Variables
• condition x, y;

• Two operations on a condition

variable:
– x.wait () – a process that invokes the
operation is
suspended.
– x.signal () – resumes one of processes (if
any) that
invoked x.wait ()
Monitor with Condition Variables
 Solution to Dining Philosophers

monitor DP
{
enum { THINKING; HUNGRY, EATING) state [5] ;
condition self [5];

void pickup (int i) {

state[i] = HUNGRY;
test(i);
if (state[i] != EATING) self [i].wait;
}

void putdown (int i) {

state[i] = THINKING;
// test left and right neighbors
test((i + 4) % 5);
test((i + 1) % 5);
}
 Solution to Dining Philosophers (cont)

void test (int i) {

if ( (state[(i + 4) % 5] != EATING) &&
(state[i] == HUNGRY) &&
(state[(i + 1) % 5] != EATING) ) {
state[i] = EATING ;
self[i].signal () ;
}
}

initialization_code() {
for (int i = 0; i < 5; i++)
state[i] = THINKING;
}
}
 Solution to Dining Philosophers (cont)

• Each philosopher I invokes the operations

pickup()
and putdown() in the following sequence:

DiningPhilosophters.pickup (i);

EAT

DiningPhilosophers.putdown (i);
 Monitor Implementation Using Semaphores

• Variables
semaphore mutex; // (initially = 1)
semaphore next; // (initially = 0)
int next-count = 0;

• Each procedure F will be replaced by

wait(mutex);
…
body of F;

…
if (next_count > 0)
signal(next)
else
signal(mutex);

• Mutual exclusion within a monitor is ensured.

 Monitor Implementation
• For each condition variable x, we have:

semaphore x_sem; // (initially = 0)

int x-count = 0;

• The operation x.wait can be implemented as:

x-count++;
if (next_count > 0)
signal(next);
else
signal(mutex);
wait(x_sem);
x-count--;
 Monitor Implementation
• The operation x.signal can be implemented as:

if (x-count > 0) {
next_count++;
signal(x_sem);
wait(next);
next_count--;
}
 A Monitor to Allocate Single
Resource
monitor ResourceAllocator
{
boolean busy;
condition x;
void acquire(int time) {
if (busy)
x.wait(time);
busy = TRUE;
}
void release() {
busy = FALSE;
x.signal();
}
initialization code() {
busy = FALSE;
}
}
 Synchronization Examples
• Solaris
• Windows XP
• Linux
• Pthreads
 Solaris Synchronization
• Implements a variety of locks to support
multitasking, multithreading (including real-
time threads), and multiprocessing
• Uses adaptive mutexes for efficiency when
protecting data from short code segments
• Uses condition variables and readers-writers
locks when longer sections of code need
access to data
• Uses turnstiles to order the list of threads
waiting to acquire either an adaptive mutex or
 Windows XP Synchronization
• Uses interrupt masks to protect access to
global resources on uniprocessor systems
• Uses spinlocks on multiprocessor systems
• Also provides dispatcher objects which may
act as either mutexes and semaphores
• Dispatcher objects may also provide events
– An event acts much like a condition variable
 Linux Synchronization
• Linux:
– Prior to kernel Version 2.6, disables interrupts to
implement short critical sections
– Version 2.6 and later, fully preemptive

• Linux provides:
– semaphores
– spin locks
 Pthreads Synchronization
• Pthreads API is OS-
independent
• It provides:
– mutex locks
– condition variables

• Non-portable extensions
include:
– read-write locks
– spin locks
 Atomic Transactions
• System Model
• Log-based Recovery
• Checkpoints
• Concurrent Atomic Transactions
 System Model
• Assures that operations happen as a
single logical unit of work, in its entirety,
or not at all
• Related to field of database systems
• Challenge is assuring atomicity despite
computer system failures
• Transaction - collection of instructions or
operations that performs single logical
function
– Here we are concerned with changes to
 Types of Storage Media
• Volatile storage – information stored here
does not survive system crashes
– Example: main memory, cache
• Nonvolatile storage – Information usually
survives crashes
– Example: disk and tape
Goal is to assure transaction atomicity where failures cause loss of
• Stable storage – Information never lost
information on volatile storage

– Not actually possible, so approximated via

replication or RAID to devices with independent
failure modes
 Log-Based Recovery
• Record to stable storage information about all
modifications by a transaction
• Most common is write-ahead logging
– Log on stable storage, each log record describes
single transaction write operation, including
• Transaction name
• Data item name
• Old value
• New value
– <Ti starts> written to log when transaction Ti starts
– <T commits> written when T commits
 Log-Based Recovery Algorithm
• Using the log, system can handle any volatile
memory errors
– Undo(Ti) restores value of all data updated by Ti
– Redo(Ti) sets values of all data in transaction Ti to
new values
• Undo(Ti) and redo(Ti) must be idempotent
– Multiple executions must have the same result as
one execution
• If system fails, restore state of all updated data
via log
 Checkpoints
• Log could become long, and recovery could
take long
• Checkpoints shorten log and recovery time.
• Checkpoint scheme:
1. Output all log records currently in volatile storage
to stable storage
2. Output all modified data from volatile to stable
storage
3. Output a log record <checkpoint> to the log on
stable storage
 Concurrent Transactions
• Must be equivalent to serial execution –
serializability
• Could perform all transactions in critical
section
– Inefficient, too restrictive
• Concurrency-control algorithms provide
serializability
 Serializability
• Consider two data items A and B
• Consider Transactions T0 and T1
• Execute T0, T1 atomically
• Execution sequence called schedule
• Atomically executed transaction order called
serial schedule
• For N transactions, there are N! valid serial
schedules
Schedule 1: T0 then T1
 Nonserial Schedule
• Nonserial schedule allows overlapped execute
– Resulting execution not necessarily incorrect
• Consider schedule S, operations Oi, Oj
– Conflict if access same data item, with at least one
write
• If Oi, Oj consecutive and operations of
different transactions & Oi and Oj don’t
conflict
– Then S’ with swapped order Oj Oi equivalent to S
• If S can become S’ via swapping nonconflicting
Schedule 2: Concurrent Serializable Schedule
 Locking Protocol

• Ensure serializability by associating lock with

each data item
– Follow locking protocol for access control
• Locks
– Shared – Ti has shared-mode lock (S) on item Q, Ti
can read Q but not write Q
– Exclusive – Ti has exclusive-mode lock (X) on Q, Ti
can read and write Q
• Require every transaction on item Q acquire
appropriate lock
 Two-phase Locking Protocol
• Generally ensures conflict serializability
• Each transaction issues lock and unlock
requests in two phases
– Growing – obtaining locks
– Shrinking – releasing locks
• Does not prevent deadlock
 Timestamp-based Protocols
• Select order among transactions in advance –
timestamp-ordering
• Transaction Ti associated with timestamp
TS(Ti) before Ti starts
– TS(Ti) < TS(Tj) if Ti entered system before Tj
– TS can be generated from system clock or as
logical counter incremented at each entry of
transaction
• Timestamps determine serializability order
– If TS(Ti) < TS(Tj), system must ensure produced
 Timestamp-based Protocol Implementation

• Data item Q gets two timestamps

– W-timestamp(Q) – largest timestamp of any
transaction that executed write(Q) successfully
– R-timestamp(Q) – largest timestamp of successful
read(Q)
– Updated whenever read(Q) or write(Q) executed
• Timestamp-ordering protocol assures any
conflicting read and write executed in
timestamp order
• Suppose Ti executes read(Q)
– If TS(Ti) < W-timestamp(Q), Ti needs to read value
 Timestamp-ordering Protocol
• Suppose Ti executes write(Q)
– If TS(Ti) < R-timestamp(Q), value Q produced by Ti
was needed previously and Ti assumed it would
never be produced
• Write operation rejected, Ti rolled back
– If TS(Ti) < W-tiimestamp(Q), Ti attempting to write
obsolete value of Q
• Write operation rejected and Ti rolled back
– Otherwise, write executed
• Any rolled back transaction Ti is assigned new
timestamp and restarted
Schedule Possible Under Timestamp Protocol
End of Chapter 6