Modeling a Load-adaptive Data Replication in Cloud Environments
Julia Myint and Axel Hunger
Department of Computer Engineering, University of Duisburg Essen, Essen, Germany
Keywords: Replication, Cloud Storage, Reliability, Data Popularity.
Abstract: Replication is an essential cornerstone of cloud storage where 24x7 availability is needed. Failures are
normal rather than exceptional in the cloud computing environments. Aiming to provide high reliability and
cost effective storage, replicating based on data popularity is an advisable choice. Before committing a
service level agreement (SLA) to the customers of a cloud, the service provider needs to carry out analysis
of the system on which cloud storage is hosted. Hadoop Distributed File System (HDFS) is an open source
storage platform and designed to be deployed in low-cost hardware. PC Cluster based Cloud Storage System
is implemented with HDFS by enhancing replication management scheme. Data objects are distributed and
replicated in a cluster of commodity nodes located in the cloud. In this paper, we propose a Markov chain
model for replication system which is able to adapt the load changes of cloud storage. According to the
performance evaluation, the system can be able to adapt the different workloads (i.e data access rates) while
maintaining the high reliability and long mean time to absorption.
1 INTRODUCTION
Cloud computing is a new computing paradigm that
is gaining increased popularity. It enables enterprise
and individual users to enjoy flexible, on demand
and high quality services such as high volume data
storage and processing without the need to invest on
expensive infrastructure, platform or maintenance.
Although high performance storage servers are
the ultimate solution for the challenges, the
implementation of inexpensive storage system
remains an open issue. Moreover, the economic
situation and the advent of new technologies have
sparked strong interest in the cloud storage provider
model. Cloud storage providers deliver economics of
scale by using the same storage capacity to meet the
needs of storage user, passing the cost saving to their
storage. Data replication is a well-known technique
from distributed systems and the main mechanism
used in the cloud for reducing user waiting time,
increasing data availability and minimizing cloud
system bandwidth consumption by offering the user
different replicas with a coherent state of the same
service (Sun et al., 2012). This paper proposes a
modelling approach of cloud storage system in
replication aspect.
The rest of this paper is organized as follows. In
the next section, we discuss the related papers with
our paper. In section 3, we propose the cloud storage
architecture and model the system by using Markov
model. Then the model is analyzed in section 4.
Finally, we conclude our paper.
2 RELATED WORK
Among a large amount of researches in storage
system for cloud computing, Google File storage
system for cloud computing, Google File System
(GFS) (Ghemawat et al., 2003) and Hadoop
distributed file system (HDFS) (Borthakur, 2007)
are widely used and most popular. Other cloud
storage systems that use key-values mechanisms are
Dynamo (Decandia et al., 2007), Pnuts (Cooper et
al., 2008) and Cassandra (Lakshman and April,
2010).
Replication management has been active
research issue in Cloud storage system proposed by
(Vo et al., 2010), (Jagadish et al., 2005), (Wei et al.,
2010) and (Ye et al., 2010). Modeling and analysis
of cloud computing has been an active research for
availability, reliability, scalability and security
issues. (longo et al., 2011), (Chuob et al., 2011),
(Sun et al., 2012) proposed modelling approach for
this purpose.
The above cloud storage systems and models
511
Myint J. and Hunger A..
Modeling a Load-adaptive Data Replication in Cloud Environments.
DOI: 10.5220/0004374805110514
In Proceedings of the 3rd International Conference on Cloud Computing and Services Science (CLOSER-2013), pages 511-514
ISBN: 978-989-8565-52-5
Copyright
c
2013 SCITEPRESS (Science and Technology Publications, Lda.)
apply different strategies for effective storage.
However they do not consider the rapid changes of
data popularity in modelling cloud storage
replication system with Markov chain approach. In
this paper, therefore, a model for an efficient
replication scheme which is able to adapt the data
popularity is proposed with the analysis of data
reliability and mean time until absorption (failure).
3 PROPOSED SYSTEM
OVERVIEW
The proposed cloud storage system is implemented
with Hadoop storage cluster (HDFS). HDFS applies
tri-replication and configurable per file. However,
HDFS does not provide policy to determine the
replication factor. In this section, the architecture of
proposed Cloud storage system and modeling of this
architecture will be presented.
3.1 Cloud Storage Architecture
As a cloud user, applications are browsed through
browsing interfaces. The applications may involve
data storage and file retrieval applications.
Cloud storage client is an interface between
user interface and storage servers. The storage client
is a code library that exports HDFS client module.
Replication Manager considers how many replicas
should be replicated to cope data popularity. After
that, it updates the replication number in HDFS
configuration file, contacts the Name node together
with the configuration setting and requests for the
list of Data nodes that host replicas of the blocks of
the file. It then contacts a Data node directly and
requests the transfer of the desired block. When a
client writes, it first asks the Name node to choose
Data nodes to host replicas. The designed cloud
storage is based on PC cluster. The PC cluster
consists of a single Name node and a number of
Data nodes.
3.2 Modeling
The modeling of Cloud storage system is based on
proposed system architecture shown in figure 1. The
cloud storage system S consists of some number of
nodes (i.e DataNodes) n on which replicas of data D
can be created. A node participates some duration of
time t
p
i.e an exponentially distributed random
variable with mean 1/λ where λ is the rate of
departure. We assume that t
p
is independent and
identically distributed for all nodes in the system.
Over a period of time, node departures decrease the
number of replicas of D present in the system. In the
system S, we assume that the cloud user access to
data D in cloud storage system with mean 1/
α
.
Likewise, we also assume that the user request to the
file does not arrive with mean 1/
β
. In these two
cases,
α
and
β
are data access rate and data cold rate
respectively.
Figure 1: Cloud Storage Architecture.
To be more reliable and maintainable in cloud
storage system, the system must also use a repair
mechanism that creates new replicas to account for
lost ones. The repair mechanism must first detect the
loss of a replica, and then create a new one by
copying D to another node from an existing replica.
The whole process may take the system some
duration of time t
r
i.e an exponentially distributed
random variable with mean 1/µ where µ is the rate
of repair.
In any state of system, the system can replicate
data D for the duration of its participation in the
system or for the popularity of data access rates.
When a node leaves the system, we assume that its
state is lost. We note that the number of replica n is
a parameter that the system can choose depending
on the storage limit which may impose the upper
bound on n.
3.2.1 Markov Chain
To analyse the system, it is reduced to a Markov
chain. The proposed system is adapted from the
problem of Gambler’s ruin found in (Epstein, 1977)
and (Feller, 1968). In Markov chain model, the
system has k functioning replicas where k (0 k n
) and l data access level where l (min l max).
The remaining n-k are being repaired. Thus the
system has (n+1)*(max-min+1) possible states in
Markov chain. If the system is in state (k,l), there are
k functioning replicas and l data access level. In state
(k,l), any one of the k functioning replicas can fail or
access frequency can be lower than l data access
level. In the former case, the system goes to state (k-
Replication
Manager
Data Access
History
User Access
Log
HDFS
configuratio
n
xml
Name
Node
DataNode
Data Stora
g
e
HDFS Cluster Cloud
CLOSER2013-3rdInternationalConferenceonCloudComputingandServicesScience
512
1,l) and in the later case, the system goes to state (k-
1,l-1). There is another possibility in state k. One of
the n-k non-functioning replicas is repaired in which
case the system goes to state (k+1,l)or if data access
frequency exceeds the level (k+1,l) with rate µ , to
(k+1,l+1) with rate α, to (k-1,l) with rate
λ
, to (k-1,l-
1) with rate
β
. Note that state 0 is an absorbing state
and the system no longer recover D when there are
no more functioning replicas left. Figure 2 shows the
Markov chain model of the system and the
definitions of the symbols are described in table 1.
Figure 2: Markov Chain moDel for a Load Adaptive
Replication.
Table 1: Symbols used in Figure 2
Symbols Descriptions
n Number of replica
λ Departure rate
µ Repair rate
α Access rate due to increase popularity
β Data cold rate due to decrease popularity
k Number of active replica
l Data access level
max,
min
Maximum data access level and minimum
data access level
To analyze the Markov chain model of the proposed
system shown in figure 1, we reduce the system with
n=3 (k=[1,2,3]), max=2, min=0, and l=[0,1,2]. Then
the simplified model is shown in figure 3.
Figure 3: The State Diagram of Maximum Replication
Factor=3 and 3 Data Access Levels.
5 NUMERICAL RESULT
The proposed replication model is evaluated by
using SHARPE software package (Trivedi et al.,
2009). We evaluated the system with two solutions-
(1) Mean time to absorption (failure) and (2)
Reliability. In this system model, the effect of
changing data access frequency, initial configuration
of workload and initial replication factor on the
system are analysed. As the cloud storage system is
implemented with commodity computing nodes, the
assumption of mean time to failure (MTTF) is 1
month and we calculate the other parameters are
listed in Table 2.
According to the parameter values in table 2, we
quantified how long a replicated system can
maintain some states before it is lost permanently
due to the dynamics of the data access rates. Figure
4(a) shows the mean time to absorption by varying
data access rates The effect of changing initial
replication factor on mean time to absorption is
illustrated in figure 4(b).
Table 2: Operating Parameter Values for the Model.
Parameters Values
The failure rate is
λ =
=
∗∗
1.389* 10
-
3
per hour
The failed machine needs 1 day to
recover normal condition. The repair rate
is
µ=
=
4.167*
10
-2
per
hour
Data access rate is
α =
300 to 550
per hour
We assume the data access is slow
with mean 7 days. Data cold rate is
=
=
∗
5.952*
10
-3
per
hour
ModelingaLoad-adaptiveDataReplicationinCloudEnvironments
513
(a) (b) (c)
Figure 4: (a) Comparison of Mean Time to Absorption with the Starting State of k=2and l=[0,1,2] (b) Mean Time to
Absorption Starting with k=[1,2,3] and l=2 (c) Reliability Comparison of Various Data Access Rate.
In order to evaluate how much reliable in the
proposed system, we compared the reliability values
by varying the data access rate from 300 to 550 per
hour. The results are shown in figure 4(c). In the
figure, it can be said that the system is more reliable
in accordance with higher data access rate.
6 CONCLUSIONS
Data replication is an essential technique to reduce
user waiting time, speeding up data access by
providing users with different replicas of
the same
service. To take advantage of these, we propose an
effective replication model to manage replication
degree in which it takes failure rate and data access
popularity into account.
In this paper, we quantify
the effects of variations in workload (i.e data access
rate) and initial system configuration (setting up the
replica number and data access level) on cloud
storage quality in terms of reliability and mean time
to failure. The experimental results demonstrate that
the proposed model is able to adapt the varying data
access load and therefore it can be more efficient in
cloud data storage.
REFERENCES
Borthakur, D., 2007, “The Hadoop Distributed File
System: Architecture and Design”. The Apache
Software Foundation.
Chuob, S. and et.al., 2011, “Modeling and Analysis of
Cloud Computing Availability based on Eucalyptus
Platform for E-Government Data Center”, 2011 Fifth
International Conference on Innovative Mobile and
Internet Services in Ubiquitous Computing.
Cooper, B. F., Ramakrishnan, R., Srivastava, U.,
Silberstein , A., Bohannon, P., and et.al., 2008, “Pnuts:
Yahoo!’s Hosted Data Serving Platform”,In VLDB.
Cristina, L. and et. al., 2012 “A strong-Centric Analysis of
MapReduce Workloads: File Popularity, Temporal
Locality and Arrival Patterns”, In Proc. IEEE IISWC.
Decandia, G., Hastorun, D., Jampani, M., Kakulapati, G.,
Lakshman, A., Pilchin, A., Sivasubramanian, S.,
Vosshall, P. and Vogels, W., 2007,“Dynamo:
Amazon’s Highly Available Key-value Store”, In SOSP.
Epstein, R., 1977, The Theory of Gambling and Statistical
Logic. Academic Press.
Feller, W., 1968, An Introduction to Probability Theory
and Its Applications. John Wiley and Sons.
Ghemawat, S., Gobioff, H., Leung, S. T., October, 2003,
“The Google File System”, Proceedings of 19th ACM
Symposium on Operating Systems Principles(SOSP
2003), New York USA.
Jagadish, H. V., Ooi, B. C., and Vu, Q. H., 2005,
“BATON: A Balanced Tree Structure for Peer-to-Peer
Networks”. In VLDB.
Lakshman, Malik, P., April 2010, “Cassandra - A
Decentralized Structured Storage System”, ACM
SIGOPS Operating Systems Review, Volume 44 Issue 2.
Longo, F., Ghosh, R., Naik, V. K. and Trivedi, K. S., 2011
“A Scalable Availability Model for Infrastructure-as-
a-Service Cloud”, DSN.
Ramabhadran, S. and Pasquale, J., 2006, “Analysis of
Long-Running Replicated Systems”, In the
Proceedings of 25th IEEE International Conference on
Computer Communications, pp. 1--9.
Sun, D. W. and et.al., Mar. 2012,”Modeling a Dyanmic
Data Replicatin Strategy to Increase System
Availability in Cloud Computing Environments“,
Journal of Computer Science and Technology
27(2):256-272. DOI 10.1007/s11390-012-1221-4.
Trivedi, K. S. and Sahner R., March 2009, “SHARPE at
the age of twenty two,” ACM Sigmetrics Performance
Evaluation Review, vol . 36, no. 4, pp. 52–57.
Vo, H. T., Chen, C., Oo, B. C., September 13-17 2010,
“Towards Elastic Transactional Cloud Storage with
Range Query Support”, International Conference on
Very Large Data Bases , Singapore.
Wei, Q. and et. al., 2010 “CDRM: A Cost-effective
Dynamic Replication Management Scheme for Cloud
Storage Cluster”, IEEE International Conference on
Cluster Computing.
Ye, Y. and et. al., 2010 “Cloud Storage Design Based on
Hybrid of Replication and Data Partitioning”, 16
th
International Conference on Parallel and Distributed
Systems.
CLOSER2013-3rdInternationalConferenceonCloudComputingandServicesScience
514
