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Silberschatz, Galvin and Gagne ©2009
Operating System Concepts – 8th Edition
Chapter 6: Process
Synchronization

6.2 Silberschatz, Galvin and Gagne ©2009
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
Requirements:
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

do {
flag[i] = TRUE;
turn = j;
while (flag[j] && turn == j);
critical section
flag[i] = FALSE;
remainder section
} while (TRUE);
Algorithm for Process Pi

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-interruptible
 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);

TestAndSet Instruction
 Definition:
boolean TestAndSet (boolean *target)
{
boolean rv = *target;
*target = TRUE;
return rv;
}
Must be executed atomically

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;
} 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;
} 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);
} 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 critical 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

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

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.
 Shared Data
 Data set
 Semaphore mutex initialized to 1 (controls access to
readcount)
 Semaphore wrt initialized to 1 (writer access)
 Integer readcount initialized to 0 (how many processes are
reading object)

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
} while (TRUE);
What is the problem with the above?

More Problems with Semaphores
 Relies too much on programmers not
making mistakes (accidental or
deliberate)
 Incorrect 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;
}
}

 Each philosopher I invokes the operations
pickup()
and putdown() in the following sequence:
DiningPhilosophters.pickup (i);
EAT
DiningPhilosophers.putdown (i);
Solution to Dining Philosophers (Cont.)

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 reader-writer lock

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 stable storage – disk
 Transaction is series of read and write operations
 Terminated by commit (transaction successful) or abort
(transaction failed) operation
 Aborted transaction must be rolled back to undo any changes it
performed

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
 Stable storage – Information never lost
 Not actually possible, so approximated via replication or RAID to
devices with independent failure modes
Goal is to assure transaction atomicity where failures cause loss of
information on volatile storage

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
 <Ti commits> written when Ti commits
 Log entry must reach stable storage before operation on data occurs

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
 If log contains <Ti starts> without <Ti commits>, undo(Ti)
 If log contains <Ti starts> and <Ti commits>, redo(Ti)

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
 Now recovery only includes Ti, such that Ti started executing before
the most recent checkpoint, and all transactions after Ti All other
transactions already 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 operations
 S is conflict serializable

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
 If lock already held, new request may have to wait
 Similar to readers-writers algorithm

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 schedule
equivalent to serial schedule where Ti appears before Tj

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 of Q that was
already overwritten
 read operation rejected and Ti rolled back
 If TS(Ti) ≥ W-timestamp(Q)
 read executed, R-timestamp(Q) set to max(R-timestamp(Q),
TS(Ti))

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
 Algorithm ensures conflict serializability and freedom from deadlock

Schedule Possible Under
Timestamp Protocol

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