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https://doi.org/10.1007/s11277-018-6047-0
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QoS Aware Replica Control Strategies for Distributed Real
Time Database Management System
Nuparam Chauhan1
· Surya Prakash Tripathi2
© Springer Science+Business Media, LLC, part of Springer Nature 2018
Abstract
In distributed real time database management system (DRT-DBMS), QoS aware replica-
tion control strategy is necessary to ensure availability and to improve the system perfor-
mance. Since user request and need of data is dynamic in nature, it requires an approach
which addresses the problem of data distribution and also resolves them in DRT-DBMS. A
large quantity of data file is produced so we need a dynamic replication approach through
which we can reduce the data access time. In order to replicate it, at the nearest site, replica
placement and replacement algorithms are required to be evaluated. This paper presents a
heuristic approach Greedy-Cover Firefly algorithm in which replica is placed dynamically
based on QoS requirement and replaced with the optimal adaptive replica replacement
algorithm for each data object. The replication cost required for copying data object to each
set of a node is calculated to select the optimal node. Here, the replication decision is taken
on the basis of access history of data on multiple sites. Due to the limited storage capac-
ity of each node, the value of each file is calculated, and the file with the smallest value is
removed to replace other files. A detailed simulation shows that the proposed algorithms
can greatly improve the system performance to reduce the system resources consumed with
respect to time and QoS requirement.
Keywords Real time systems · Quality of service · Optimal replication · Replica
replacement · Access frequency
1 Introduction
Recent years, DRT-DBMS are widely used for processing and managing a large amount
of data. The distributed DRT-DBMS is responsible for organizing distributed database
which is stored in different physical locations. For efficient management of DRT-DBMS,
it needs to ensure timing constraints [1]. These databases are configured to process in the
* Nuparam Chauhan
nrcua80@gmail.com
Surya Prakash Tripathi
tripathee_sp@yahoo.co.in
1
Computer Science and Engineering Department, FGIET, Raebareli 229001, India
2
Computer Science and Engineering Department, IET, Lucknow 226001, India

2. N. Chauhan, S. P. Tripathi
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timely manner of certain actions. Deadlines, Temporal consistency, and priority scheduling
are the factors need to consider during the execution of scheduling process [2]. The real
time database systems are partitioned into several databases to form Distributed Real Time
Database (DRTDB) [3]. If the data from DRTDB reflects the entity of recent status, then
the object of data is new in the system environment [4]. After completing the validity of an
existing data, it becomes invalid temporarily, and it requires installing new information’s.
Sensor update transactions are used to update the recent status of the entities periodically
[5].
In DRTDB, it is a common issue for completing the requests at a given moment. The
requests are processed based on their needs and by using the service delay involved to meet
the deadline [6]. Enabling the tasks in timeliness manner is a problem in real time data-
bases, and it leads to difficulty while applying those results to data intensive applications
of computing [7]. The hard deadline transactions are based on worst-case execution time
(WCET) and knowledge of activation period. To ensure this, existing analysis methods
and scheduling algorithms are focused [8]. When retaining the temporal consistency, more
transactions are accommodated by the system, and it requires less resource consumption. It
utilizes more capacity for reducing the updated workload [9]. The real time update policy
of the system is changed, and the effect of changes are required to be applied. The dynamic
environment requires timeliness update in access control policies which is responsible for
real time updates [10].
In distributed real time environment, replication is introduced to increase the perfor-
mance and handling fault tolerance. There are two methodologies for data replication.
The active replication which involves applying writes operation synchronously. The pas-
sive replication propagates the data to other sites after committing the transaction [11].
The data is stored in multiple sites hence concurrency control protocols are developed to
enhance replica and to minimize the complexities [12]. The replication control algorithms
are based on the position in which the data is replicated. All the available data replicates in
all sites in full replication, but it increases the time taken to complete the transaction [13].
Accessing information from the database is restricted for some of the users in the multi-
user environment to provide security. The copy of data is stored in all the databases in full
replication of different authorization [14].
The integrity constraints are maintained in a DRT DBMS without declaring it explic-
itly; the Serializability property is used. Data replication is used to improve the availability
of data by storing it in various databases. The inconsistencies not required to consider the
data which appears in the concurrency of failure [15]. For solving the problem of consist-
ency, two techniques are used. They are operational transformation and locking. In real
time group editors, integrated transformation and locking techniques are used to explore
complementary roles [16]. To change and retrieve the values of data objects which are
stored in a replicated data storage system can be accessed through front end node. The
local database handles updates and requests which are propagated to other replicas by esti-
mating read replica sets for multiple degrees. Then it informs about the freshness of read-
ing replica to all the front end nodes [17].
Replication control algorithms are used with the integration of replication control and
scheduling. The token-based method is introduced with conflict resolution policies to bal-
ance the real time transaction and to control replication [18]. Continuous convergence is a
protocol which provides predictability and consistency by permitting the data to be repli-
cated deterministically. Several update operations are allowed which leads to overloading
of resources temporarily [19]. In full replication, updates are sent to all the terminals. The
scalability is achieved by segmenting databases in full virtual replication [20]. Distributed

3. QoS Aware Replica Control Strategies for Distributed Real Time…
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system is used not only for storing data it also handles the replicated data arrived from new
devices. In order to replicate large volume of data efficiently, serializability is provided
with timing constraints and prior information [21]. The correct replica should be main-
tained regardless of a node failure, and it assures that there is no disagreement among cor-
rect replicas. Fine-grained and light weight policy has the computational overhead of lock
management and scalability to tolerate with database failure [22].
The outline of this paper is as follows. Section 2 discusses the related work of replica
placement based on QoS. Section 3 describes proposed replication control framework. Sec-
tion 4 illustrates the results and performance evaluation of proposed approach. Section 5
discusses significant aspects of our work and concludes.
2 Related Work
For providing efficient access to distributed real time storage system, replication control
approaches are mandatory. The works related to replication strategy is discussed below.
Guoxin et al. had proposed Selective Data replication mechanism to reduce latency and
communication overload of inter-data centres. OSN model was deployed to data centers
globally in which all data center has the copy of data and master centre updates all the cop-
ies. Frequent communication between data centers is needed when the distant user commu-
nicates frequently. It leads to service latency of distributed storage system. The inter-data
center update is needed for replicas to update rates and selecting the data for the process of
replication. SD3 is introduced to reduce the intercommunication among data center when
the data is replicated. SD3 increases inter-data center communication with enhanced per-
formance using multicast update tree, data center congestion control, and replica deactiva-
tion. The experiments demonstrate the effectiveness of the scheme [23].
Jon et al. had proposed DYFRAM for table fragmentation and allocation dynamically. It
is a decentralized approach which depends on table’s access pattern. To minimize the cost
of communication in a network, the tables are fragmented and replicated on all the sites.
DYFRAM is used to detect the type of fragmentation, allocation, and replication by analys-
ing query. The frequent changes and the workload of access pattern from various sites can
be generated by the applications of distributed database management system. Depending
on the history of recent access, it increases the number of local access than remote access.
It achieves replication, fragmentation, and reallocation with reduced cost of communica-
tion and feasibility for patterns [24].
Lin et al. had proposed a dynamic object replication algorithm, and Real-time distrib-
uted dynamic Window Mechanism (RDDWM) for minimizing the cost requirement and
complete the replication request within a specific time. Deadline is a commonly used
constraint that is the request must be completed within the duration. It creates the opti-
mum structure for requests like read and write. The request may be affected by the cost of
the transaction and IO cost. Hence the mathematical model is considered with deadline
requirement to reduce the cost. This technique was implemented with two factors such as
the highest and lowest time of deadline [25].
Torky et al. had proposed a hybrid combination of optimistic and pessimistic replica-
tion approaches for large and medium-scale distributed databases. Various factors degrade
the construction of real time distributed database. Consistency between real time objects
is replicated to all nodes of the distributed database. Due to adaptive dynamic replica-
tion control algorithm, separate consistency is managed, and the status of updating also

4. N. Chauhan, S. P. Tripathi
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increased for all objects. In order to obtain availability and consistency, remote access tech-
niques are restricted with timing constraints [26].
Xueyan et al. had proposed an algorithm full replication for enabling quality of ser-
vices (QoS). For the development of business-related applications and information facili-
ties, QoS is taken into account for distributing the content. The issue of QoS aware replica
placement is determined with respect to the QoS request. There are two kinds of services
considered to provide QoS. They are Replica blind and replica-aware services. For imple-
menting replica-aware services, the routing request is optimized with high response. The
location of the replica is not aware of the servers in a blind model. Hence the request can
be reached to its destination with routing technique in this service. To calculate the optimal
location of replicas, several algorithms are used with replica-aware services, and they are
NP-complete in the problem of QoS aware placement problem [27].
3
Proposed Replication Control Strategies
The distributed RT DBMS is a real time processing system that handles workloads whose
states changes frequently. They use timing constraints to represent the temporal validity of
certain data objects.
The data replication model of distributed RT DBMS is shown in Fig. 1. It consists of
an original server which generates data objects and the number of sites. The distributed
system is denoted as D = (S, L) in which S denotes the server and L ⊆ S × S represents a
link between servers. The communication cost of each link (a, b) ⊂ L is denoted as d(a,
b). Server con communicates with each other through this link. It is defined as the sum of
communication cost among the link is the communication cost of the path. In between two
servers a and b, d(a, b) is the communication cost of the shortest path. The cost required
to store the replica on server b is represented as t(a). Each server is capable of process-
ing multiple requests from the clients. If the server has the requested data object, then the
request can be processed locally. Otherwise, the request will be directed to the nearest
server that has a copy of requested data. The communication cost from client to server is
negligible since it does not affect the decision of replica.
a g
b
c
f
e
d
O
g1
g2
g3
g7
g6
g5
g4
v2
v1
v3
v4
v5
v6
v7
Fig. 1 Data replication in connected network

5. QoS Aware Replica Control Strategies for Distributed Real Time…
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3.1 QoS Aware Replica Placement
In the distributed system, s represents the original server which is the only server generates
data object. Without losing generality, replication server has the copy of original data. For
a collection of replication servers, the replication strategy is denoted as U ⊆ 𝐒 − {s} . In
order to evaluate the strategy of replication, it is necessary to calculate the replication cost
R(U) which is the sum of storage cost G(U) and update cost V(U).
3.1.1 Replication Cost
The addition of all storage cost of server’s replication model is denoted as
where, t(b) represents the cost of each server. The update request can be sent to each replica
server from the original server for enabling consistency. The number of update requests at
a specific time interval is denoted as u. All servers are connected to update distribution
tree T where the shortest path tree is rooted to the original server. Each node receives an
update request from the original server through communication links. This request to every
node can be obtained from its parent and rooted it to its child until all the servers receive
request. For the update distribution tree, the frequency of update is u, and the replication is
evaluated as follows. Let p (b) represents the parent node of b and ­
Tb denotes the subtree
in the update distribution tree. If Tb ∩ R ≠ 0
, then the link (b, p (b)) involves the process
of update multicast. Hence the update cost is the overall communication cost of the link
which is calculated as
Each server a is in need of quality requirement q(a) that is the total request created by the
server a is evaluated within the communication cost q(a). It is assumed that every node has
the capability of finding nearest replica by its own. If the request is not serviced within the
given communication cost q(u), then it will be violated. If all the requests are processed
successfully, then it is considered as a feasible state.
3.1.2 Greedy‑Cover Firefly Algorithm
In order to solve the QoS aware replica replacement problem, the new algorithm Greedy-
Cover Firefly has proposed. The procedure for selecting minimum cover set is described as
follows.
Step 1: Find the shortest path for each node from the original server and consider the
shortest path tree for representing each node.
Step 2: Create cover set for each node which includes the path and the link.
Step 3: Eliminate the super cover set by removing repeated paths.
Step 4: Initialize the population that is the number of flies, {x1, ­x2…xn}.
Step 5: For each member of Firefly calculate intensity value, {I1, ­I2, …In}.
Step 6: Update the step of each firefly based on minimum cover set.
(1)
G(U) =
∑
b⊂U
t (b)
(2)
V(U) = u ×
∑
b≠s, Tb∩ R≠0
d(b, p(b))

6. N. Chauhan, S. P. Tripathi
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Step 7: Update the solution set based on brighter firefly.
Step 8: Replicate the data object to the resultant node.
Step 9: Terminate if the criterion is satisfied else go to step 4.
The set of servers within the QoS requirement q(a) is the cover set c(a) of server b. The
cover set c(a) is represented as
Each server contains its unique cover set and the server a should be satisfied by the
server c if server g ∈ c(a) is replicated. For processing the server a, each server in c(a) rep-
licate the data. The communication cost c(b) is not considered if c(a) ⊆ c(b)
. If the data is
replicated on server g ∈ c(a), then the server g can accomplish a and b simultaneously. If it
is observed that|c(b)| |c(a)|
, then server a is likely to be satisfied by server b. When pro-
cessing the remaining cover sets, b has additional need of quality requirement. The over-
lapping of c(b) with other cover set, if it contains number of elements. Similar to that b also
required to be covered by some other cover set. In this algorithm, the cover set with smaller
value has been used to place the replicated data on that server. The cost of replication is
reduced by using the replication model with less number of servers. Based on the above
considerations, the Greedy-Cover Firefly algorithm is developed.
Initially, the greedy cover detects the cover set for each server, and it removes the
super cover set c (b) from the other cover set c (a). The server b is removed from q(a)
if c (a) ⊆ c (b), a ≠ b
. Greedy cover evaluates each server, selects the smallest cover set
and replicates the data on that server with normalized benefit. Only unsatisfied cover
sets are remaining after greedy selects the required cover set. Greedy cover Firefly algo-
rithm selects the smallest cover set, and it continues the process until all the cover sets are
selected.
(3)
c (b) = {b|d (a, b) ≤ q (a)}

7. QoS Aware Replica Control Strategies for Distributed Real Time…
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This is a three phase Greedy-Cover Firefly algorithm in which the first step detects the
cover set for each server. It is detected by calculating the total number of servers and the
time required for this calculation is denoted as O (| S |2
). The second phase detects and
removes the super cover set. All pair of cover sets are checked with O (| S |2
) possibilities
which finds the super cover sets. In the third phase it replicates data into the server and the
computation time required for calculation is O(|S|log|S| + |S|3
= O(|S|3
).
3.2 Optimal Adaptive Replica Replacement
When requesting several files from a local storage device ­
(h1, ­h2,…hn) (n ≥ 1), it will not be
accessed directly from the original server. In order to retrieve one file from another site, it
requires transferring of file to a local storage device. For each server the storage capacity

8. N. Chauhan, S. P. Tripathi
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is limited hence the replicated files should be replaced to provide efficient access. Optimal
adaptive replica replacement algorithm is proposed for replacing the files. The algorithm
is processed with two steps. In the initial phase, the replication decision about copying the
data into local storage is taken. Then it replaces the replicas by finding the value of files
in the local storage devices. The data access can be enhanced for the file with the largest
value, and the corresponding file will be retained. By taking replication decision, the rela-
tive capacity of storage system is measured as
where, N is the total capacity of all nodes and F denotes the total capacity of all files. The
threshold e defines whether the file is needed to be copied or not. When the threshold value
is higher than the number of file’s replica, then data is decided to be copied. If the thresh-
old value is lower, then it will not be copied. After the replication decision, if the num-
ber of replicas is less than the threshold then it required being copied. Because of limited
storage capacity, some files are needed to be deleted to copy the other files. In this paper,
optimal adaptive replica replacement algorithm has proposed for replica replacement of
distributed RT DBMS.
First, determines the access frequency of file A(f) which depends on the history of last
access. Then evaluate the access cost A(c) which comprise of maximum bandwidth and
number of replicas.
3.2.1 Replicas Access Frequency
The information related to the access history of files at a specific time period is obtained
to calculate the replicas access frequency. In order to differentiate the requirement of his-
tory records, the data captured at varying time interval has different values. The time of
half- life is denoted as the time interval which specifies the time needed to quantify half of
the initial value and quantity is represented by value in this algorithm. The importance of
various history records is evaluated by setting different values. The recent access history is
more prior than older accesses which are having smaller values. The value of file access at
­nth
time interval is denoted as
(4)
e =
N
F
(5)
An (f) = 2−n

9. QoS Aware Replica Control Strategies for Distributed Real Time…
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where n denotes the number of time interval. Hence A(f) provides the summation of files
value at different time interval. The value is set to ­
2−1
when it is accessed at the first time
and the value is set to ­
2−2
when it is accessed for the second time and so on.
3.2.2 Replicas Access Cost
During the replica replacement of entire process, the cost is affected by factors such as
bandwidth and size of replica. The probability of fault occurred during the transfer process
is increased with high bandwidth consumption and it may block the data distribution pro-
cess. Effective performance of replacement algorithm is achieved with reduced total cost.
The access cost is calculated with,
where, S(c) denotes the replica size and B(c) denotes the storage cost.
3.2.3 Replacement Policy for Replica
By using the following assumptions, the value of the file is defined as
where, A(f) is the access frequency, B(c) is the storage cost of the file, P(c) represents the
number of files, and A(c) represents the access cost. V(c) provides file’s value for the local
site, and the obtained values are sorted in ascending order. From the sorted list files are
deleted until the site releases the files capacity. Then the deleted files are replaced with the
newly generated replicas.
4 Performance Evaluation
The performance of replication strategies have evaluated with 8 nodes, and each connected
with a network link. Each node has 5 kb storage capacity, and it has fixed storage cost to
store data objects. The total number of files generated by the original server is 200 and
arrival time is uniformly distributed between 0.5 and 5 s. The size of each file is in the
range of 1–128 bytes. The generated files are replicated based on the replication cost until
space is sufficient to copy it. After the storage limits, the file value is calculated to remove
the file from the required node. The implementation parameters used are shown in Table 1.
The QoS requirement for the proposed replication approach was shown in Fig. 2. For
the given storage cost, replication cost of each placement is calculated with the update cost.
(6)
A (c) =
S(c)
B(c)
(7)
V(c) =
A(f) ∗ B(c)
P(c) ∗ S(c)
Table 1 Implementation
parameters Number of nodes 8
Node’s capacity 5 kb
File size Uniform (1–128) bytes
Data arrival time Uniform (0.5–5) s
Number of files 200

10. N. Chauhan, S. P. Tripathi
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The QoS requirement is computed for 50, 100, 150 and 200 data objects. The replication
cost was minimized up to 24.82 with the help of Greedy firefly algorithm. Each data object
generated by the original server is stored with the average replication cost 25.46, 25.20,
25.25, 24.82 which is obtained with the shortest path. This is the minimum cost to tackle
QoS requirement, and the cost is 35.78, 36.99, 37.96 and 36.92 for the random nodes.
The comparison of optimized replication cost with the random replica placement in Fig. 2
shows the efficiency of proposed replication control strategy.
From each server, the cost needed for the communication is shown in Table 2. The aver-
age communication cost obtained for each node is 30, 31, 14, 28, 22, and 27. The update
time of data objects required for data replication is compared with conventional random
replication as shown in Fig. 3. The proposed method takes less time than the conventional
methods for updating data objects, and it provides efficiency for data storage. Optimal
adaptive replacement strategy is compared with fusion based technique. For the proposed
approach, most of the data objects are replaced with less than 5000 microseconds. How-
ever, in the case of fusion based approach, the data objects are replaced between 5000 and
Fig. 2 Performance comparison of QoS requirements for various algorithms
Table 2 Average communication
cost of each site #
Site # Update cost Storage cost Communi-
cation cost
Site 1 20 6 30
Site 2 14 4 31
Site 3 18 1 14
Site 4 26 3 28
Site 5 13 5 22
Site 6 23 7 27
Site 7 17 2 25

11. QoS Aware Replica Control Strategies for Distributed Real Time…
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10,000 ms. When considering the minimum replacement time, there is a slight deviation
with an optimal adaptive approach.
After storing each data object, the total available space for the network is shown in
Table 3. The storage space is high for initial placement, and they are reduced for each
data. When the storage space is not sufficient to place the data objects, the objects are
deleted from the node based on previous access history. It leads to sufficient storage for
the newly generated data objects with respect to the access time interval. The QoS is meas-
ured in terms of time requirement to complete the entire replacement strategy. The time
Fig. 3 Replacement time for data object
Table 3 Storage space after
placing data object
Data object Storage
space
(bytes)
20 32,591
40 29,012
60 25,160
80 20,999
100 17,673
120 15,407
140 14,159
160 12,726
180 11,028
200 9561

12. N. Chauhan, S. P. Tripathi
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requirement is compared with the approaches like supervised learning, conjugate gradient,
and Gauss-Jordan. The completion time is 2.91 s for the proposed approach whereas the
traditional approaches take 10, 50 and 150 s. The performance is higher for the proposed
replication control framework in terms of QoS and computational complexity (Fig. 4).
5 Conclusion
In this work, a distributed real-time database system model has designed in which each
node is considered as a local site. The original server generates and maintains a group
of data objects in a uniform time interval. A new heuristic approach called Greedy-Cover
Firefly algorithm is proposed to fulfil the QoS requirement of specific node. The proposed
algorithm determines the positions of replicas. The minimum cover set is chosen with Fire-
fly optimization algorithm which selects the optimal nodes. The replication cost is com-
puted by considering the update cost and storage cost. Due to its limited storage capacity,
adaptive replica replacement strategy is provided to improve the overall system perfor-
mance. An Optimal Adaptive Replica Replacement algorithm based on the access history,
file’s size and the number of replicas is considered for replacement. To load a new file in
replica replacement, the replicas with the smallest value will be deleted, so the local node
has enough capacity to replace the file. When compared with the traditional algorithms, the
performance of the proposed approach is better regarding replication cost, complexity, and
processing time.
Fig. 4 Total time required for data replication

13. QoS Aware Replica Control Strategies for Distributed Real Time…
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References
1. Ram, N., Udai, S. (2011). Over load detection and admission control policy in DRTDBS. Informa-
tion technology and mobile communication (pp. 45–54). Berlin: Springer.
2. Shanker, U., Misra, M., Sarje, A. K. (2008). Distributed real time database systems: Background
and literature review. Distributed and Parallel Databases, ACM, 23(2), 127–149.
3. Lam, K.-Y., Lee, V. C. S., Hung, S. L., Kao, B. C. M. (1997). Priority assignment in distributed real-
time databases using optimistic concurrency control. IEE Proceedings-Computers and Digital Tech-
niques, 144(5), 324–330.
4. Udai, S., Ram, N. (2011). A new admission control policy and over load detection in DRTDBS.
International Journal of Data Mining and Emerging Technologies, 1(1), 30–38.
5. Pinho, L. M., Vasques, F., Wellings, A. (2004). Replication management in reliable real-time sys-
tems. Real-Time Systems, Springer, 26(3), 261–296.
6. Lam, K. Y., Kuo, T. W., Tsang, W. H., Law, G. C. (2000). Concurrency control in mobile distributed
real-time database systems. Information Systems, Elsevier, 25(4), 261–286.
7. Özgür, U. (1994). Processing real-time transactions in a replicated database system. Distributed and
Parallel Databases, Springer, 2(4), 405–436.
8. Andler, S. F., Hansson, J., Eriksson, J., Mellin, J., Berndtsson, M., Eftring, B. (1996). DeeDS
towards a distributed and active real-time database system. ACM SIGMOD Record, 25(1), 38–51.
9. Malik, S. U., Khan, S. U., Ewen, S. J., Tziritas, N., Kolodziej, J., Zomaya, A. Y., et al. (2016). Perfor-
mance analysis of data intensive cloud systems based on data management and replication: A survey.
Distributed and Parallel Databases, Springer, 34(2), 179–215.
10. Matthias, N., Matthias, J. (2000). Performance modeling of distributed and replicated databases.
IEEE Transactions on Knowledge and Data Engineering, 12(4), 645–672.
11. Swaroop, V., Shanker, U. (2011). Data management in mobile distributed real time database sys-
tems: Reviews and issues. International Journal of Computer Science and Information Technologies,
2(4), 1517–1522.
12. Shetan, R. C. (2013). Performance evaluation of real time database systems in distributed environment.
International Journal of Computer Technology and Applications, 4(5), L785–L792.
13. Krithi, R. (1993). Real-time databases. Distributed and Parallel Databases, 1(2), 199–226.
14. Tokuda, H., Mercer, C. W. (1989). ARTS: A distributed real-time kernel. ACM SIGOPS Operating
Systems Review, 23(3), 29–53.
15. Ramamritham, K., Son, S. H., Dipippo, L. C. (2004). Real-time databases and data services. Real-
Time Systems, ACM Transactions, 28(2–3), 179–215.
16. Bahareh, A. M., Nima, J. N. (2016). A comprehensive review of the data replication techniques in
the cloud environments: Major trends and future directions. Journal of Network and Computer Appli-
cations, 64, 229–238.
17. Korupolu, M. R., Dahlin, M. (2002). Coordinated placement and replacement for large-scale distrib-
uted caches. IEEE Transactions on Knowledge and Data Engineering, 14(6), 1317–1329.
18. Umut, T. (2014). Distributed database design using evolutionary algorithms. Journal of Communica-
tions and Networks, 16(4), 430–435.
19. Zaman, S., Grosu, D. (2011). A distributed algorithm for the replica placement problem. IEEE
Transactions on Parallel and Distributed Systems, 22(9), 1455–1468.
20. Ashish, S., Shyam, P. S. K. (2015). Issues in distributed real time replication database system. The
Opinion, 4(8), 11–20.
21. Rashed, S., Safa’A, S., Abdul-kader, H. (2016). Scalable data-oriented replication with flexible con-
sistency in real-time data systems. Data Science Journal, 15(4), 1–15.
22. Stefan, P. (1994). Replica determinism in distributed real-time systems: A brief survey. Real-Time Sys-
tems, Springer, 6(3), 289–316.
23. Guoxin, L., Haiying, S., Harrison, C. (2016). Selective data replication for online social net-
works with distributed datacenters. IEEE Transactions on Parallel and Distributed Systems, 27(8),
2377–2393.
24. Jon, O. H., Norvald, H. R., Kjetil, N. (2010). DYFRAM: Dynamic fragmentation and replica
management in distributed database systems. Distributed and Parallel Databases, Springer, 282(3),
157–185.
25. Lin, W., Bharadwaj, V. (2006). An object replication algorithm for real-time distributed databases.
Distributed and Parallel Databases, Springer, 19(2), 125–146.
26. Torky, S., Hazem, E.-B., Hala, A. H. (2012). Design of efficient dynamic replica control algorithm
for periodic/aperiodic transactions in distributed real-time databases. IJCSI International Journal of
Computer Science Issues, 9(1), 92–100.

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27. Xueyan, T., Jianliang, X. (2005). QoS-aware replica placement for content distribution. IEEE Trans-
actions on Parallel and Distributed Systems, 16(10), 921–932.
Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and
institutional affiliations.
Nuparam Chauhan Pursuing Ph.D. from Dr. A.P.J. Abdul Kalam
Technical University Lucknow, U.P. in Computer Science and Engi-
neering. He is working as a Associate Professor and Head of Depart-
ment of Computer Science and Engineering in FGIET Raebareli. His
area of specialization is Distributed Real Time Database Management
System. He is a life member of ISTE and nominated member of Com-
puter Society of India. His several research and conference proceeding
have been published in National and International Journals.
Dr. Surya Prakash Tripathi working as a Professor and head in Depart-
ment of Computer Science and Engineering, IET, Lucknow. His area
of specialization is Multi- Objective Optimization, Fuzzy Systems,
Evolutionary Computing, Software Engineering, Data Mining.