This paper discusses deadlock detection in homogeneous distributed database systems, where transactions are processed across multiple machines leading to potential deadlocks. The author presents a method using process termination to detect and manage deadlocks by assessing transaction requests and employing a stack-based approach to identify cycles in transactions. The conclusion emphasizes the need for effective techniques to resolve deadlocks by terminating transactions with the lowest timestamps.

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Deadlock Detection in Homogeneous Distributed Da-
tabase Systems
Ashwini K.C
Dept of Computer Science and Engineering
BTL Institute of Technology
Bangalore, India
kcashwini.ashwini@gmail.com
Abstract
In distributed database systems, data does not re-
side in one single location, it may be stored in mul-
tiple computers, located in the same physical loca-
tion; or may be disseminated over a network of in-
terlinked computers. Distributed databases can
amend the performance at end-user worksites by
allowing transactions to be processed on many ma-
chines, instead of being limited to one. These
transactions may impose troubles like deadlock.
This paper makes an attempt to detect deadlock in
homogeneous distributed database systems i.e; lo-
cal transactions using process termination method.
Keywords: Stabile, Transaction wait for
graph, local scheduler.
Introduction
Distributed database system is network of individ-
ual systems or sites where data is distributed
among various locations which are interlinked by
communication network. Users interact with data-
base via transactions which includes actions like
read, write, lock, unlock operations on data. If ac-
tions of transactions are distributed within same lo-
cation or site then it is known as local transaction
or homogeneous distributed system. On the other
hand, actions of transactions distributed over many
locations are global transactions or heterogeneous
distributed system.
In homogeneous distributed environment the same
database is used at each node. Due to distributed
nature of database, there must be proper structure
for effective working of database. If the data are al-
located in random fashion, then it may lead to unu-
sual conditions like concurrency of transactions,
synchronizing of events, and deadlocks.
Deadlock is a stabile condition that occurs when
several user transactions compete for same data,
where a user wishes to perform transaction opera-
tions on data which is already held by other user
transaction. It is part of concurrency control in
which anomalies arise while coordinating the ac-
tions of processes that operate in parallel. In such
situations, none of the user transactions can get
chance to perform transaction operations on re-
quired data, hence results in waiting cycle.
Once deadlock is formed, it will remain there until
it is detected or broken because, deadlock or cycle
detection can proceed concurrently with normal ac-
tivities of system, it will not have great effect on
system throughput.
Waiting cycle situation can arise if all of the fol-
lowing circumstance hold at the same time in a
system :
1. Mutual Exclusion: There should be at least
one data such that it is held in a non-
sharable manner and that can be used by on-
ly one transaction at any given instant of
time.[7]

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ISSN: XXXX-XXXX Volume X, Issue X, Month Year 18
2. Hold and Wait or Resource Holding: A us-
er transaction is currently holding at least
one data and requesting additional data
which are being held by other user
transactions.[7]
3. No Preemption: The permission to release
the data voluntarily is only for the transac-
tion that is holding it at any given instant of
time and the operating system should not de-
allocate the data.[7]
4. Circular Wait: A user transaction is waiting
for a data which is being held by another
user transaction, which in turn is waiting for
the first transaction to release the data. On
the whole, there is a set of waiting transac-
tions, T = {T1, T2, ..., TN}, such that T1 is
waiting for a data held by T2, T2 is waiting
for a data held by T3 and so on until TN is
waiting for a data held by T1.[7]
Consider a real world example of traffic that illus-
trates deadlock state .
All cars in four directions are moving in parallel, at
certain point of time when signal occurs all of them
stop at a circle occupying all directions in such a
way that none of the cars would be able to move in
any of the direction resulting in waiting cycle for
any one car to move and create space for others to
move.[6]
Fig:1 Deadlock State
So to overcome this deadlock state we use dead-
lock detection technique. In this technique dead-
lock would have already been occurred and several
deadlock detection techniques tries to detect it and
gives solution for the problem to be cleared. This
technique requires periodic checking of transac-
tions and selects a dupe transaction to break the
deadlock which is later deceased to make the trans-
actions happen without any deadlock.
Local Transaction Model In
Distributed Systems:
There are diverse modules: Transaction manag-
er(TM), Data manager(DM), local Scheduler (S),
User transaction process. The user transactions
communicate with TM’s and in turn TM’s com-
municate with DM’s. The transaction manager
controls execution of user transactions by provid-
ing necessary data required by the user transaction.
It does so by contacting with the data manager pre-
sent at that particular site. But if the transaction
process requires a data item which is not present at
the site where it arises, the transaction manager
contacts the data manager of the other site where
the required data item actually resides. The sched-
uler in turn, at each site, synchronizes the transac-
tion requests and performs deadlock detection. A
transaction may request multiple data objects sim-
ultaneously [1].

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Fig 2: Distributed Transaction Model
Deadlock Detection Methods:
For detecting deadlock state there are two meth-
ods:
1. Process Termination: One or more user
transactions involved in the deadlock may
be terminated. Its users choice to terminate
all transactions involved in the deadlock. Or,
to terminate one transaction at a time until
the deadlock is recovered. This method has
high overheads because after each termina-
tion an algorithm must find out whether the
system is still in deadlock. Age of the pro-
cess as well as priority and several more is-
sues are helpful for termination of pro-
cess.[7]
2. Resource Pre-emption:
data allocated to several transactions may
be successively displaced and assigned to
other transactions until the deadlock is bro-
ken.[7]
In this paper we use process termination method
and performs stack operations for detecting dead-
lock.
Deadlock detection process is as follows: [1]
1. Input number of transactions and data items.
2. Input transaction number and corresponding
data item required by the transaction.
3. Array P[ ] stores transactions.
4. Array Q[ ] stores data items.
5. values at top of the stack indicates transac-
tion(p) that recently requested data and it
will have low timestamp value. write the
transaction value at top of stack to tempo-
rary array T[ ].
6. Increment index of T and write value of data
item at the top of the stack to T[ ].
7. Now check whether data item at top of stack
is requested by any other transaction. If true
then write the value of transaction to succes-
sive index of T[ ].
8. For each predecessor value in T[],check if
that value has a dependent and write the val-
ue into T[] until there exists repetition of
value in array T.
9. If there exists repetition then it indicates that
deadlock has occurred and pop the values of
transaction and data item .
10. If there is no repetition of values then store
the transaction and data item value in X[ ],
Y[ ], which can be called as deadlock free
arrays having transaction and its required
data item pairs and then pop the values.
11. Repeat the steps from 5 to 10 until there is
deadlock free environment.
Illustration:
Consider Transaction wait for Graph :[1]

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ISSN: XXXX-XXXX Volume X, Issue X, Month Year 20
Fig 3: Transaction Wait For Graph [1]
Stack:
Table 1: Stack Containing Transactions And Data
Items.
In this example, the transaction that has lowest
timestamp value i.e; recently requested is at the top
of the stack (4,2). Store the value of transaction in
temporary array T.
4
Fig 4: Transaction Value At Top Of Stack
Now look for the data item required by transaction
‘4’, i.e; data item ‘2’. Add the data item value to suc-
cessive index of array T.
4 2
Fig 5: Transaction Value ‘4’ Requires Data ‘2’
Next step is to check whether new element in T[ ]
requires any data item, if yes, then add the data item
value to successive index of T[ ].
4 2 1
Fig 6: ‘2’ Is Requesting For Data ‘1’
Repeat the procedure until you traverse all transac-
tions and data items. If repetition of value occurs in
T[ ], then it indicates the occurrence of deadlock cy-
cle. So pop the pair at top most values of stack that
causes deadlock. If the top most pair doesn’t cause
deadlock then store the transaction and data item val-
ue in other two new stacks.
Fig 7: Deadlock State
From this array you can infer that there is repetition
of values. So the considered pair of transaction and
data item was (4,2). Pop this pair. And figure looks as
below.
Fig 8: Graph After Removal Of Dependency Of Pair
(4,2).
Table 2: After Popping Top Most Values.
P Q
T3 T4
T3 T2
T2 T1
T1 T3
Now the values at top of stack is (3,4). Repeat the
same procedure as above to check there is deadlock
or not.
3 4
Fig 9: Next Cycle Of Values From Stack
Value 3 requires data item 4. Value 4 in turn is not
requesting any data item so this pair do not create
deadlock. Therefore store this pair values in new
stacks.
Table 3: new stack holding pair of values that do not
cause deadlock.
X Y
3 4
Repeat the steps for all transactions. Finally a dead-
lock free graph is obtained.
CONCLUSION:
In this paper author has made an attempt to detect
deadlock cycle in homogeneous distributed system.
This technique assures that the one or more trans-
4 2 1 3 2

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ISSN: XXXX-XXXX Volume X, Issue X, Month Year 21
actions having low timestamp value will be victim
transactions and will be terminated until deadlock
is resolved.
REFERENCES
[1] Swati Gupta, “Deadlock Detection Techniques
in Distributed Database system”, International
Journal of Computer Applications (0975 – 8887)
Volume 74– No. 21, July 2013.
[2] Swati Gupta , Meenu Vijarania, “Analysis for
Deadlock Detection and Resolution Techniques in
Distributed Database”, International Journal of
Advanced Research in Computer Science and
Software Engineering, Volume 3, Issue 7, July
2013, ISSN: 2277 128X.
[3] EDGAR KNAPP, “Deadlock Detection in Dis-
tributed Databases”, University of Texas at Austin,
Austin, Texas 78712, Vol. 19, No. 4, December
1987.
[4] RON OBERMARCK, “Distributed Deadlock
Detection Algorithm”, ACM Transactions on Da-
tabase Systems, Vol. 7, No. 2, June 1982, Pages
187-208.
[5] Praveen Mahadevanna, “Deadlock Detection
techniques for Distributed Systems”, The Universi-
ty of Texas at Arlington (UTA).
[6] Sean LaPlante, Ben Foster, Robert Jones, Joe
Grago, “ Deadlock Detection in Distributed Sys-
tems”.
[7] www.google.com.
Biography
Ashwini K.C received the B.E.
degree in Computer Science and Engineering from
Pesit South Campus, VTU university, Bangalore,
Karnataka, in 2013.At present persuing the Master of
Technology in Computer Science and Engineering
Department at BTL institute of Technology, Banga-
lore.