![Removal Based Improved Replication Control
and Fault Tolerance Method for Road Traffic Data Management
Dr Annaluri Sreenivasa Rao1a
, Dr Attili Venkata Ramana2b
, Dr. G. Suresh Reddy3c
, Dr C.Sunil Kumar4d
a)
Assistant Professor in IT,VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India,
b)
Assoc. Prof in CS, Dept. of ECM, Sreenidhi Institute of Science and Technology, Hyderabad, India,
c)
Professor , Dept of IT,VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India,
d )
Professor in ECM, Sreenidhi Institute of Science and Technology, Hyderabad, India
a)
dr.attilivenkataramana@gmail.com,b)
annaluri.rao@gmail.com, c)
ithod@vnrvjiet.in, d)
ccharupalli@gmail.com
Abstract— The massive growth in road traffic and subsequence
generation of traffic related data insisting the researcher to
proceed for the analytical research on the traffic prediction.
However the gigantic size of the data and chances of storage
failure may cause the purpose inefficient.The advancement in
technologies and high demand for fault tolerant storage solutions
most of the cloud based commercial storage service providers
are now equipped with Erasure based Reed – Solomon fault
tolerance mechanism. However the additional cost for replication
is still an overhead for service providers and customers. In this
work, we propose a novel erasure based code and further
optimization as shortening the proposedcode also for the digital
storage formats. The work also results into a comparative study
of cost analysis for commercial cloud based storage service
providers. Finally the work demonstrates the improvement in
code shortening and making the performance higher.
Keywords— Erasure, Reed – Solomon,
Code Shortening,
Performance Comparison, Evolution Application,
Response Time Comparison, Dropbox, Google Drive,
Hightail, OneDrive, SugarSync
I. INTRODUCTION
In the past years, the high upcoming demand for storage
with high performance and reliability were been
understood.The industry was approaching towards a phase
where the lack of standardization of digital storagewas
limiting the applications to make storage more reliable for
commercial storage providers. The major bottleneck for the
standardization was the non-standard storage solutions
used by different service providers. In the early 80’s, the
industry adopted cloud computing for distributed storage
solutions. The effort was well recognized and multiple
companies came together to form a consortium in order to
frame the standardization for digital storage.
As far as data storage is concerned, there are multiple
schemes are available to improve file and data compression.
The other most influencing parameters For instance, a data file
that is uploaded and accessed on the server may seriously be
effected by the network bandwidth as well as the server
workload. This will degrade the efficiency [1]. Moreover the
cloud storage services deals with a great scope and domain of
the data being storage and retrieved along with the frequency
of access varying depending on the mode of the operation
performed on the data [2]. Offering unlimited storage container
space might cause a high economic drawback on the cloud
storage provider and as well as the users due to inefficient
storage [3]. Hence, a technique or automation is needed to
find the best suitable storage structure based on cost and
other influencing factors. There are many free offerings of
the cloud storage services; however they may not suite
the application requirement to the best always [4].
Two major companies, Philips and Sony took the major
initiative to define the standard storage formats in digital
media. The standard is well accepted today and been referred
as compact storage format. This standard format is majorly
used for achieving any data, which also reduces the storage
cost compared to the early storage formats. However the
compact storage format has limitations in order to achieve high
availability. It is difficult to predict how a storage media gets
corrupted. In the earlier studies we have understood the
reasons for storage device failure. Henceforth we realise the
following errors for storage failures as
(1) The additional noise affecting the storage during
transmission or during retrieval
And
(2) Mishandling of the removable devices
The most important improvement in the recent time for fault
tolerance in digital media storages is the Reed – Solomon code.
The basic benefit of the Reed – Solomon codes is to rearrange so
that the timely restoration can be achieved for storage devices.
Thus in this work we concentrate on further enhancement of the
Erasure based fault tolerance mechanism.
The rest of the work is framed such as in Section II we
understand the cost effectiveness of the commercial cloud
storage solutions, in Section III we realise the basic Reed
Solomon Fault Tolerance scheme, in Section IV we
propose the novel Reed – Solomon based code, in Section
V we propose the further optimization of the proposed
code, in Section VI we discuss the implementation and
results and in Section VII we conclude the work.
II. COMMERCIAL CLOUD STORAGE SERVICES
As the choice of storage services from cloud is not
limited and most of those are configured to give best
advantages for specific type of data and operation, we
compare most of the services here [5 – 7].
A. Dropbox
The Dropbox is a storage service which is available for
client side access for Windows systems, Linux Systems,
Macintosh systems, Blackberry mobile operating systems,
Android mobile operation systems and finally the IPhone
operating systems. The free Basic account comes with a paltry
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 16, No. 3, March 2018
141 https://sites.google.com/site/ijcsis/
ISSN 1947-5500](https://image.slidesharecdn.com/18paper28021851ijcsiscamerareadypp-180403173637/85/Removal-Based-Improved-Replication-Control-and-Fault-Tolerance-Method-for-Road-Traffic-Data-Management-1-320.jpg)
![Fig.4. Cost Comparison for Sugar Sync
Table 10.Support for Mobile BasedCloudApplications in SugerSync
Client OS Type Support
Apple IPhone Operating Available
Systems
Android Mobile Operating Available
Systems
Blackberry Operating Available
Systems
Microsoft Mobile Available
Operating System
III. REED – SOLOMON CODE FOR FAULT TOLERANCE
The most important factor that makes Reed-Solomon
framework to implement is the simplicity. Here in this work
we consider the scenario to compare the performance of
Reed – Solomon and Proposed Encoding technique [8].
We consider there will be K storage devices each
hold n bytes of data such that,
D = ∑D1 , D2 .D3 .....Dk …Eq1
Where D is the collection of storage devices
Also there will be L storage devices each hold n bytes
of check sum data such that,
C = ∑C1 , C 2 , C3 ....CL …Eq2
Where C is the collection of Checksum devices
The checksum devices will hold the calculated values
from each respective data storage devices.
The goal is to restore the values if any device from
the C collection fails using the non – failed devices.
The Reed – Solomon deploys a function G in order to
calculate the checksum content for every device in C.
Here for this study we understand the example of the
calculation with the values as K = 8 and L = 2 for the
devices C1and C2 with G1 and G2 respectively [9].
The core functionalities of Reed – Solomon is to break the
collection of storage devices in number of words [10] [11]. Here in
this example we understand the each number of words
is of u bits randomly. Hence the words in each device
can be assumed as v, where v is defined as
v =
8bits 1word
( nbytes). . ÷ … Eq3÷
byte u Bits
Furthermore, v is defined as
V =
8n
…Eq4
u
Henceforth, we understand the formulation for
checksum for each storage device as
Ci = Wi .(D1 , D2 , D3 ...Dk ) …Eq5 Where the coding
function W is defined to operate on each
word
After the detail understanding of the Erasure fault
tolerance scheme, we have identified the limitations of
the applicability to the cloud storage services and
propose the novel scheme for fault tolerance in this work
in the next section.
IV. PROPOSED NOVEL FAULT TOLERANCE SCHEME
With the understanding of the limitations of existing
erasure codes to be applied on the cloud based storage
systems as the complex calculations with erasure codes
will reduce the performance of availability measures
significantly. Thus we make an attempt to reduce the
calculation complexities with simple mathematical
operations in the standard erasure scheme.
The checksum for storage devices are considered as
Ci from the Eq5. We propose the enhancement as the
following formulation for checksum calculation:
Ci =Wi .( D1 , D2 , D3 ... Dk ) = Wi ( D1 ⊕ D2 ⊕ D3 ... ⊕ Dk )
…Eq6
Here the XOR operation being the standard mathematical
operation most suitable for logical circuits used in all standard
hardware makes it faster to be calculated.
Also we redefine the function to be applied on each
word for the storage devices D as following:
w
1,1 . . .
w
1, L
. . . ..
W = . . . . . …Eq7
. . . ..
w . . . w
K ,1 K ,L
K X L
The proposed matrix will be stored on one of the devices
and will be recalculated only once. As the modified
checksum formulation is an XOR operation, thus which will
automatically notify in case of any change.
Furthermore, we optimize the proposed code
framework in the next section.
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 16, No. 3, March 2018
144 https://sites.google.com/site/ijcsis/
ISSN 1947-5500](https://image.slidesharecdn.com/18paper28021851ijcsiscamerareadypp-180403173637/85/Removal-Based-Improved-Replication-Control-and-Fault-Tolerance-Method-for-Road-Traffic-Data-Management-4-320.jpg)
![V. OPTIMIZING PROPOSED NOVEL FAULT
TOLERANCE SCHEME
The Reed Solomon code is expressed by the power
of coefficient denoted by n for the data blocks, where n
is expressed as
n = 2 m −1 …Eq 8
and the code blocks are represented as
…Eq 9k = 2 m − 1 − 2t
Where m represents the number of bits per data and t
represents the capability of correcting errors. In general
theReed – Solomon code considers an 8 bit data and 2
bit code, the error correcting code can be represented
as (255,251) code.
Here in this part of the work, we try to optimize the
code length further to reduce the replication cost. The
steps of the optimization algorithm are explained here:
Step-1. First we consider the effective code in
(255,251) block, where the code is consisting
of zero and non-zero codes.
Step-2. Then we find the number of zero codes in
the segment. For instance the numbers of zero
codes are227 in the code block. These codes
will not have any effect in the error correction
and fault tolerance mechanism.
Step-3. Then we find the effective block of the
code as (28,24) for a 2 bit error correction code.
Step-4. Hence as a final outcome of the
optimization technique, we got the optimized
code block.
VI. IMPLEMENTATION AND RESULTS
To simulate and understand the improvement in the
outcomes we implement the Reed – Solomon code with the
enhancement and optimization proposed in this work.
We accept any random data as the initial data block
for the testing [Table -11].
Table 11.Initial Data Block
0 0 0 0
1 0 0 0
0 1 0 0
0 0 1 0
0 0 0 1
1 1 0 0
0 1 1 0
0 0 1 0
1 1 0 1
1 0 1 0
0 1 0 1
1 1 1 0
0 11 1
1 1 1 1
1 0 1 1
1 0 0 1
Based on the modified fault tolerance scheme, we realise
the addition and multiplication table [Table -12 & 13].
Table 12.Addition Table
0 a^0 a^1 a^2 a^3 a^4 a^5 a^6 a^7 a^8 a^9 a^10 a^11 a^12 a^13 a^14 -------------
----------------------------------------------------------------
0 |0 a^0 a^1 a^2 a^3 a^4 a^5 a^6 a^7 a^8 a^9 a^10 a^11 a^12 a^13 a^14
a^0 |a^0 0 a^4 a^8 a^14 a^1 a^10 a^13 a^9 a^2 a^7 a^5 a^12 a^11 a^6 a^3
a^1 |a^1 a^4 0 a^5 a^9 a^0 a^2 a^11 a^14 a^10 a^3 a^8 a^6 a^13 a^12 a^7
a^2 |a^2 a^8 a^5 0 a^6 a^10 a^1 a^3 a^12 a^0 a^11 a^4 a^9 a^7 a^14 a^13
a^3 |a^3 a^14 a^9 a^6 0 a^7 a^11 a^2 a^4 a^13 a^1 a^12 a^5 a^10 a^8 a^0
a^4 |a^4 a^1 a^0 a^10 a^7 0 a^8 a^12 a^3 a^5 a^14 a^2 a^13 a^6 a^11 a^9
a^5 |a^5 a^10 a^2 a^1 a^11 a^8 0 a^9 a^13 a^4 a^6 a^0 a^3 a^14 a^7 a^12
a^6 |a^6 a^13 a^11 a^3 a^2 a^12 a^9 0 a^10 a^14 a^5 a^7 a^1 a^4 a^0 a^8
a^7 |a^7 a^9 a^14 a^12 a^4 a^3 a^13 a^10 0 a^11 a^0 a^6 a^8 a^2 a^5 a^1
a^8 |a^8 a^2 a^10 a^0 a^13 a^5 a^4 a^14 a^11 0 a^12 a^1 a^7 a^9 a^3 a^6
a^9 |a^9 a^7 a^3 a^11 a^1 a^14 a^6 a^5 a^0 a^12 0 a^13 a^2 a^8 a^10 a^4
a^10 |a^10 a^5 a^8 a^4 a^12 a^2 a^0 a^7 a^6 a^1 a^13 0 a^14 a^3 a^9 a^11
a^11 |a^11 a^12 a^6 a^9 a^5 a^13 a^3 a^1 a^8 a^7 a^2 a^14 0 a^0 a^4 a^10
a^12 |a^12 a^11 a^13 a^7 a^10 a^6 a^14 a^4 a^2 a^9 a^8 a^3 a^0 0 a^1 a^5
a^13 |a^13 a^6 a^12 a^14 a^8 a^11 a^7 a^0 a^5 a^3 a^10 a^9 a^4 a^1 0 a^2
a^14 |a^14 a^3 a^7 a^13 a^0 a^9 a^12 a^8 a^1 a^6 a^4 a^11 a^10 a^5 a^2 0
Table 13.MULTIPLICATION TABLE
0 a^0 a^1 a^2 a^3 a^4 a^5 a^6 a^7 a^8 a^9 a^10 a^11 a^12 a^13 a^14
-----------------------------------------------------------------------------
0 | 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
a^0 |0 a^0 a^1 a^2 a^3 a^4 a^5 a^6 a^7 a^8 a^9 a^10 a^11 a^12 a^13 a^14
a^1 |0 a^1 a^2 a^3 a^4 a^5 a^6 a^7 a^8 a^9 a^10 a^11 a^12 a^13 a^14 a^0
a^2 |0 a^2 a^3 a^4 a^5 a^6 a^7 a^8 a^9 a^10 a^11 a^12 a^13 a^14 a^0 a^1
a^3 |0 a^3 a^4 a^5 a^6 a^7 a^8 a^9 a^10 a^11 a^12 a^13 a^14 a^0 a^1 a^2
a^4 |0 a^4 a^5 a^6 a^7 a^8 a^9 a^10 a^11 a^12 a^13 a^14 a^0 a^1 a^2 a^3
a^5 |0 a^5 a^6 a^7 a^8 a^9 a^10 a^11 a^12 a^13 a^14 a^0 a^1 a^2 a^3 a^4
a^6 |0 a^6 a^7 a^8 a^9 a^10 a^11 a^12 a^13 a^14 a^0 a^1 a^2 a^3 a^4 a^5
a^7 |0 a^7 a^8 a^9 a^10 a^11 a^12 a^13 a^14 a^0 a^1 a^2 a^3 a^4 a^5 a^6
a^8 |0 a^8 a^9 a^10 a^11 a^12 a^13 a^14 a^0 a^1 a^2 a^3 a^4 a^5 a^6 a^7
a^9 |0 a^9 a^10 a^11 a^12 a^13 a^14 a^0 a^1 a^2 a^3 a^4 a^5 a^6 a^7 a^8
a^10 |0 a^10 a^11 a^12 a^13 a^14 a^0 a^1 a^2 a^3 a^4 a^5 a^6 a^7 a^8 a^9
a^11 |0 a^11 a^12 a^13 a^14 a^0 a^1 a^2 a^3 a^4 a^5 a^6 a^7 a^8 a^9 a^10
a^12 |0 a^12 a^13 a^14 a^0 a^1 a^2 a^3 a^4 a^5 a^6 a^7 a^8 a^9 a^10 a^11
a^13 |0 a^13 a^14 a^0 a^1 a^2 a^3 a^4 a^5 a^6 a^7 a^8 a^9 a^10 a^11 a^12
a^14 |0 a^14 a^0 a^1 a^2 a^3 a^4 a^5 a^6 a^7 a^8 a^9 a^10 a^11 a^12 a^13
Henceforth, we compare the results of the generic
Reed-Solomon Coding and the proposed fault tolerance
technique [Table – 14] based on the initial code.
Table 14.FAULT TOLLERANCERESULT
Parameter Generic RS Proposed
Optimized RS
Initial a^1 a^3 a^5 a^1 a^3 a^5
Polynomial
Encoded Data a^5 a^3 a^1 a^6 0 00 a^6 a^4
a^4 a^2 a^0 a^2 a^0
Fault a^5 a^3 a^1 a^6 a^6 a^4 a^2 1
Tolerance a^4 a^2 1
Code
Optimization 0% 57%
Reduction
VII. CONCLUSION
In this work the commercial cloud storage services are been
compared based on the cost and performance factors. The
result of the comparative measures provided the
understanding of the demand for highly reliable and cost
effective fault tolerance system. Henceforth, in this work we
study the core Reed - Solomon fault tolerance mechanism
based on Erasure codes. The work contributes towards the
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 16, No. 3, March 2018
145 https://sites.google.com/site/ijcsis/
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![improved performance code for fault tolerance for digital
storage devices rather than magnetic. Also the work
enhanced the performance of the proposed technique
by applying the improvement in terms of optimization.
The result of the proposed optimization technique is
57% reduction in the storage cost without negotiating
with the fault tolerance reliability.
REFERENCES
[1] J. Kubiatowicz, D. Bindel, Y. Chen, P. Eaton, D. Geels, R. Gummadi,
S. Rhea, H. Weatherspoon, W. Weimer, C. Wells, and B. Zhao,
“Oceanstore: An Architecture for Global-Scale Persistent Storage,”
Proc. Ninth Int’l Conf. Architectural Support for Programming
Languages and Operating Systems (ASPLOS), pp. 190-201, 2000.
[2] P. Druschel and A. Rowstron, “PAST: A Large-Scale, Persistent
Peer-to-Peer Storage Utility,” Proc. Eighth Workshop Hot Topics
in Operating System (HotOS VIII), pp. 75-80, 2001.
[3] A. Adya, W.J. Bolosky, M. Castro, G. Cermak, R. Chaiken, J.R.
Douceur, J. Howell, J.R. Lorch, M. Theimer, and R. Wattenhofer,
“Farsite: Federated, Available, and Reliable Storage for an
Incompletely Trusted Environment,” Proc. Fifth Symp. Operating
System Design and Implementation (OSDI), pp. 1-14, 2002.
[4] A. Haeberlen, A. Mislove, and P. Druschel, “Glacier: Highly
Durable, Decentralized Storage Despite Massive Correlated
Failures,” Proc. Second Symp. Networked Systems Design and
Implementation (NSDI), pp. 143-158, 2005.
[5] Q. Tang, “Type-Based Proxy Re-Encryption and Its
Construction,” Proc. Ninth Int’l Conf. Cryptology in India:
Progress in Cryptology (INDOCRYPT), pp. 130-144, 2008.
[6] G. Ateniese, K. Benson, and S. Hohenberger, “Key-Private Proxy Re-
Encryption,” Proc. Topics in Cryptology (CT-RSA), pp. 279-294, 2009.
[7] J. Shao and Z. Cao, “CCA-Secure Proxy Re-Encryption without
Pairings,” Proc. 12th Int’l Conf. Practice and Theory in Public
Key Cryptography (PKC), pp. 357-376, 2009.
[8] J. Bellorado and A. Kavcic , "Low-complexity soft-decoding
algorithms for Reed-Solomon codes: An algebraic soft-in hard-
out chase decoder" , IEEE Trans. Inf. Theory. , vol. 56 , no. 3 ,
pp.945 -959 , 2010
[9] F. Garc-Herrero, J. Valls and P. K. Meher , "High-speed RS
(255, 239) decoder based on LCC decoding" , Circuits Syst.
Signal Process. , vol. 30 , no. 6 , pp.1643 -1669 , 2011
[10] W. Zhang, H. Wang and B. Pan , "Reduced-complexity LCC
Reed-Solomon decoder based on unified syndrome
computation" , IEEE Trans. Very Large Scale Integr. (VLSI) Syst.
, vol. 21 , no. 5 , pp.974 -978 , 2013
[11] J. Jiang and K. R. Narayanan , "Algebraic soft-decision decoding of
Reed-Solomon codes using bit-level soft information" , IEEE Trans.
Inf. Theory , vol. 54 , no. 9 , pp.2008 -3928.
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Vol. 16, No. 3, March 2018
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Removal Based Improved Replication Control and Fault Tolerance Method for Road Traffic Data Management
- 1. Removal Based Improved Replication Control and Fault Tolerance Method for Road Traffic Data Management Dr Annaluri Sreenivasa Rao1a , Dr Attili Venkata Ramana2b , Dr. G. Suresh Reddy3c , Dr C.Sunil Kumar4d a) Assistant Professor in IT,VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India, b) Assoc. Prof in CS, Dept. of ECM, Sreenidhi Institute of Science and Technology, Hyderabad, India, c) Professor , Dept of IT,VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India, d ) Professor in ECM, Sreenidhi Institute of Science and Technology, Hyderabad, India a) dr.attilivenkataramana@gmail.com,b) annaluri.rao@gmail.com, c) ithod@vnrvjiet.in, d) ccharupalli@gmail.com Abstract— The massive growth in road traffic and subsequence generation of traffic related data insisting the researcher to proceed for the analytical research on the traffic prediction. However the gigantic size of the data and chances of storage failure may cause the purpose inefficient.The advancement in technologies and high demand for fault tolerant storage solutions most of the cloud based commercial storage service providers are now equipped with Erasure based Reed – Solomon fault tolerance mechanism. However the additional cost for replication is still an overhead for service providers and customers. In this work, we propose a novel erasure based code and further optimization as shortening the proposedcode also for the digital storage formats. The work also results into a comparative study of cost analysis for commercial cloud based storage service providers. Finally the work demonstrates the improvement in code shortening and making the performance higher. Keywords— Erasure, Reed – Solomon, Code Shortening, Performance Comparison, Evolution Application, Response Time Comparison, Dropbox, Google Drive, Hightail, OneDrive, SugarSync I. INTRODUCTION In the past years, the high upcoming demand for storage with high performance and reliability were been understood.The industry was approaching towards a phase where the lack of standardization of digital storagewas limiting the applications to make storage more reliable for commercial storage providers. The major bottleneck for the standardization was the non-standard storage solutions used by different service providers. In the early 80’s, the industry adopted cloud computing for distributed storage solutions. The effort was well recognized and multiple companies came together to form a consortium in order to frame the standardization for digital storage. As far as data storage is concerned, there are multiple schemes are available to improve file and data compression. The other most influencing parameters For instance, a data file that is uploaded and accessed on the server may seriously be effected by the network bandwidth as well as the server workload. This will degrade the efficiency [1]. Moreover the cloud storage services deals with a great scope and domain of the data being storage and retrieved along with the frequency of access varying depending on the mode of the operation performed on the data [2]. Offering unlimited storage container space might cause a high economic drawback on the cloud storage provider and as well as the users due to inefficient storage [3]. Hence, a technique or automation is needed to find the best suitable storage structure based on cost and other influencing factors. There are many free offerings of the cloud storage services; however they may not suite the application requirement to the best always [4]. Two major companies, Philips and Sony took the major initiative to define the standard storage formats in digital media. The standard is well accepted today and been referred as compact storage format. This standard format is majorly used for achieving any data, which also reduces the storage cost compared to the early storage formats. However the compact storage format has limitations in order to achieve high availability. It is difficult to predict how a storage media gets corrupted. In the earlier studies we have understood the reasons for storage device failure. Henceforth we realise the following errors for storage failures as (1) The additional noise affecting the storage during transmission or during retrieval And (2) Mishandling of the removable devices The most important improvement in the recent time for fault tolerance in digital media storages is the Reed – Solomon code. The basic benefit of the Reed – Solomon codes is to rearrange so that the timely restoration can be achieved for storage devices. Thus in this work we concentrate on further enhancement of the Erasure based fault tolerance mechanism. The rest of the work is framed such as in Section II we understand the cost effectiveness of the commercial cloud storage solutions, in Section III we realise the basic Reed Solomon Fault Tolerance scheme, in Section IV we propose the novel Reed – Solomon based code, in Section V we propose the further optimization of the proposed code, in Section VI we discuss the implementation and results and in Section VII we conclude the work. II. COMMERCIAL CLOUD STORAGE SERVICES As the choice of storage services from cloud is not limited and most of those are configured to give best advantages for specific type of data and operation, we compare most of the services here [5 – 7]. A. Dropbox The Dropbox is a storage service which is available for client side access for Windows systems, Linux Systems, Macintosh systems, Blackberry mobile operating systems, Android mobile operation systems and finally the IPhone operating systems. The free Basic account comes with a paltry International Journal of Computer Science and Information Security (IJCSIS), Vol. 16, No. 3, March 2018 141 https://sites.google.com/site/ijcsis/ ISSN 1947-5500
- 2. 2GB of storage. For document based applications this is huge. The Storage service is good choice for applications using the container for read only data. Table 1.Cost Comparison for Dropbox. Data Load Cost Load in GigaBytes Price in US Dollars 100 99 USD 200 99 USD 300 99 USD 400 499 USD 500 499 USD 1000 Not Available > 1000 Not Available Here we provide a graphical representation of the cost price comparison: Fig.1. Cost Comparison for Dropbox Table 2.Support for Mobile Based Cloud Applications in Dropbox Client OS Type Support Apple IPhone Operating Available Systems Android Mobile Operating Available Systems Blackberry Operating Available Systems Microsoft Mobile Available Operating System B. Google Drive The most popular cloud storage service is Drive storage from Google. The basic account comes with 15 Giga bytes of storage for a new customer account or an existing account created with Google Email. The highest rated benefit of the Google Drive is the service can be also be integrated with other existing google services for storing various types of data from other services. Table 3.CostComparisonfor Google Drive Data Load Cost Load in Giga Bytes Price in US Dollars 100 60 USD 200 120 USD 300 120 USD 400 240 USD 500 240 USD 1000 600 USD > 1000 1200 to 9600 USD Here we provide a graphical representation of the cost price comparison: Fig.2. Cost Comparison for Google Drive Table 4.Support for Mobile BasedCloudApplications in Google Drive Client OS Type Support Apple IPhone Operating Available Systems Android Mobile Operating Available Systems Blackberry Operating Not Available Systems Microsoft Mobile Not Available Operating System C. Hightail The previous version of business cloud storage of Hightail was popular by name of YouSendIt. The basic reason for creating the name was the core of the features that Hightail provides. Hightail is majorly known for sharing files, which can be digitally signed for verifications. The core technology behind this provider is link sharing, where the sender can upload a file and the link to that same file can be shared with the recipient. The recipient can click on the link to download the same. This service is popular for business users as it provides the private cloud storage and the desktop version of International Journal of Computer Science and Information Security (IJCSIS), Vol. 16, No. 3, March 2018 142 https://sites.google.com/site/ijcsis/ ISSN 1947-5500
- 3. the client, which can be used for syncing local files to the cloud storage. Table 5.CostComparisonforHightail Data Load Cost Load in Giga Bytes Price in US Dollars 100 Free 200 Free 300 Free 400 Free 500 Free 1000 Free > 1000 195 USD Table 6.Support for Mobile BasedCloudApplications in Hightail Client OS Type Support Apple IPhone Operating Available Systems Android Mobile Operating Not Available Systems Blackberry Operating Not Available Systems Microsoft Mobile Not Available Operating System D. OneDrive The OneDrive was previously popular as SkyDrive. The functionalities are mostly same as Dropbox. The most important factor for this storage service is that the client version is available for Windows systems, Linux Systems, Macintosh systems, Blackberry mobile operating systems, Android mobile operation systems and finally the IPhone operating systems. Moreover the supports for social media plug-ins are also available here. This feature makes the application more compatible with other applications to access data directly. Table 7.CostComparisonforOneDrive Data Load Cost Load in Giga Bytes Price in US Dollars 100 50 USD 200 100 USD 300 Not Available 400 Not Available 500 Not Available 1000 Not Available > 1000 Not Available Here we provide a graphical representation of the cost price comparison: Fig.3. Cost Comparison One Drive Table 8.Support for Mobile BasedCloudApplications in OneDrive Client OS Type Support Apple IPhone Operating Available Systems Android Mobile Operating Available Systems Blackberry Operating Available Systems Microsoft Mobile Available Operating System E. SugarSync The SugarSync is majorly popular among business users for its effective and fast online backup solutions. The service can also be used for complete folder and individual file syncing with multiple applications and multiple users. Moreover the service provides a unique function to share the stored content over multiple devices at same point of time but with different permission levels. The most important factor for this storage service is that the client version is available for Android mobile operation systems and also the IPhone operating systems. Table 9.CostComparisonforSugerSync Data Load Cost Load in Giga Bytes Price in US Dollars 100 99 USD 200 250 USD 300 250 USD 400 250 USD 500 250 USD 1000 550 USD > 1000 Pay Per Use Here we provide a graphical representation of the cost price comparison: International Journal of Computer Science and Information Security (IJCSIS), Vol. 16, No. 3, March 2018 143 https://sites.google.com/site/ijcsis/ ISSN 1947-5500
- 4. Fig.4. Cost Comparison for Sugar Sync Table 10.Support for Mobile BasedCloudApplications in SugerSync Client OS Type Support Apple IPhone Operating Available Systems Android Mobile Operating Available Systems Blackberry Operating Available Systems Microsoft Mobile Available Operating System III. REED – SOLOMON CODE FOR FAULT TOLERANCE The most important factor that makes Reed-Solomon framework to implement is the simplicity. Here in this work we consider the scenario to compare the performance of Reed – Solomon and Proposed Encoding technique [8]. We consider there will be K storage devices each hold n bytes of data such that, D = ∑D1 , D2 .D3 .....Dk …Eq1 Where D is the collection of storage devices Also there will be L storage devices each hold n bytes of check sum data such that, C = ∑C1 , C 2 , C3 ....CL …Eq2 Where C is the collection of Checksum devices The checksum devices will hold the calculated values from each respective data storage devices. The goal is to restore the values if any device from the C collection fails using the non – failed devices. The Reed – Solomon deploys a function G in order to calculate the checksum content for every device in C. Here for this study we understand the example of the calculation with the values as K = 8 and L = 2 for the devices C1and C2 with G1 and G2 respectively [9]. The core functionalities of Reed – Solomon is to break the collection of storage devices in number of words [10] [11]. Here in this example we understand the each number of words is of u bits randomly. Hence the words in each device can be assumed as v, where v is defined as v = 8bits 1word ( nbytes). . ÷ … Eq3÷ byte u Bits Furthermore, v is defined as V = 8n …Eq4 u Henceforth, we understand the formulation for checksum for each storage device as Ci = Wi .(D1 , D2 , D3 ...Dk ) …Eq5 Where the coding function W is defined to operate on each word After the detail understanding of the Erasure fault tolerance scheme, we have identified the limitations of the applicability to the cloud storage services and propose the novel scheme for fault tolerance in this work in the next section. IV. PROPOSED NOVEL FAULT TOLERANCE SCHEME With the understanding of the limitations of existing erasure codes to be applied on the cloud based storage systems as the complex calculations with erasure codes will reduce the performance of availability measures significantly. Thus we make an attempt to reduce the calculation complexities with simple mathematical operations in the standard erasure scheme. The checksum for storage devices are considered as Ci from the Eq5. We propose the enhancement as the following formulation for checksum calculation: Ci =Wi .( D1 , D2 , D3 ... Dk ) = Wi ( D1 ⊕ D2 ⊕ D3 ... ⊕ Dk ) …Eq6 Here the XOR operation being the standard mathematical operation most suitable for logical circuits used in all standard hardware makes it faster to be calculated. Also we redefine the function to be applied on each word for the storage devices D as following: w 1,1 . . . w 1, L . . . .. W = . . . . . …Eq7 . . . .. w . . . w K ,1 K ,L K X L The proposed matrix will be stored on one of the devices and will be recalculated only once. As the modified checksum formulation is an XOR operation, thus which will automatically notify in case of any change. Furthermore, we optimize the proposed code framework in the next section. International Journal of Computer Science and Information Security (IJCSIS), Vol. 16, No. 3, March 2018 144 https://sites.google.com/site/ijcsis/ ISSN 1947-5500
- 5. V. OPTIMIZING PROPOSED NOVEL FAULT TOLERANCE SCHEME The Reed Solomon code is expressed by the power of coefficient denoted by n for the data blocks, where n is expressed as n = 2 m −1 …Eq 8 and the code blocks are represented as …Eq 9k = 2 m − 1 − 2t Where m represents the number of bits per data and t represents the capability of correcting errors. In general theReed – Solomon code considers an 8 bit data and 2 bit code, the error correcting code can be represented as (255,251) code. Here in this part of the work, we try to optimize the code length further to reduce the replication cost. The steps of the optimization algorithm are explained here: Step-1. First we consider the effective code in (255,251) block, where the code is consisting of zero and non-zero codes. Step-2. Then we find the number of zero codes in the segment. For instance the numbers of zero codes are227 in the code block. These codes will not have any effect in the error correction and fault tolerance mechanism. Step-3. Then we find the effective block of the code as (28,24) for a 2 bit error correction code. Step-4. Hence as a final outcome of the optimization technique, we got the optimized code block. VI. IMPLEMENTATION AND RESULTS To simulate and understand the improvement in the outcomes we implement the Reed – Solomon code with the enhancement and optimization proposed in this work. We accept any random data as the initial data block for the testing [Table -11]. Table 11.Initial Data Block 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 1 1 0 0 0 1 1 0 0 0 1 0 1 1 0 1 1 0 1 0 0 1 0 1 1 1 1 0 0 11 1 1 1 1 1 1 0 1 1 1 0 0 1 Based on the modified fault tolerance scheme, we realise the addition and multiplication table [Table -12 & 13]. Table 12.Addition Table 0 a^0 a^1 a^2 a^3 a^4 a^5 a^6 a^7 a^8 a^9 a^10 a^11 a^12 a^13 a^14 ------------- ---------------------------------------------------------------- 0 |0 a^0 a^1 a^2 a^3 a^4 a^5 a^6 a^7 a^8 a^9 a^10 a^11 a^12 a^13 a^14 a^0 |a^0 0 a^4 a^8 a^14 a^1 a^10 a^13 a^9 a^2 a^7 a^5 a^12 a^11 a^6 a^3 a^1 |a^1 a^4 0 a^5 a^9 a^0 a^2 a^11 a^14 a^10 a^3 a^8 a^6 a^13 a^12 a^7 a^2 |a^2 a^8 a^5 0 a^6 a^10 a^1 a^3 a^12 a^0 a^11 a^4 a^9 a^7 a^14 a^13 a^3 |a^3 a^14 a^9 a^6 0 a^7 a^11 a^2 a^4 a^13 a^1 a^12 a^5 a^10 a^8 a^0 a^4 |a^4 a^1 a^0 a^10 a^7 0 a^8 a^12 a^3 a^5 a^14 a^2 a^13 a^6 a^11 a^9 a^5 |a^5 a^10 a^2 a^1 a^11 a^8 0 a^9 a^13 a^4 a^6 a^0 a^3 a^14 a^7 a^12 a^6 |a^6 a^13 a^11 a^3 a^2 a^12 a^9 0 a^10 a^14 a^5 a^7 a^1 a^4 a^0 a^8 a^7 |a^7 a^9 a^14 a^12 a^4 a^3 a^13 a^10 0 a^11 a^0 a^6 a^8 a^2 a^5 a^1 a^8 |a^8 a^2 a^10 a^0 a^13 a^5 a^4 a^14 a^11 0 a^12 a^1 a^7 a^9 a^3 a^6 a^9 |a^9 a^7 a^3 a^11 a^1 a^14 a^6 a^5 a^0 a^12 0 a^13 a^2 a^8 a^10 a^4 a^10 |a^10 a^5 a^8 a^4 a^12 a^2 a^0 a^7 a^6 a^1 a^13 0 a^14 a^3 a^9 a^11 a^11 |a^11 a^12 a^6 a^9 a^5 a^13 a^3 a^1 a^8 a^7 a^2 a^14 0 a^0 a^4 a^10 a^12 |a^12 a^11 a^13 a^7 a^10 a^6 a^14 a^4 a^2 a^9 a^8 a^3 a^0 0 a^1 a^5 a^13 |a^13 a^6 a^12 a^14 a^8 a^11 a^7 a^0 a^5 a^3 a^10 a^9 a^4 a^1 0 a^2 a^14 |a^14 a^3 a^7 a^13 a^0 a^9 a^12 a^8 a^1 a^6 a^4 a^11 a^10 a^5 a^2 0 Table 13.MULTIPLICATION TABLE 0 a^0 a^1 a^2 a^3 a^4 a^5 a^6 a^7 a^8 a^9 a^10 a^11 a^12 a^13 a^14 ----------------------------------------------------------------------------- 0 | 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 a^0 |0 a^0 a^1 a^2 a^3 a^4 a^5 a^6 a^7 a^8 a^9 a^10 a^11 a^12 a^13 a^14 a^1 |0 a^1 a^2 a^3 a^4 a^5 a^6 a^7 a^8 a^9 a^10 a^11 a^12 a^13 a^14 a^0 a^2 |0 a^2 a^3 a^4 a^5 a^6 a^7 a^8 a^9 a^10 a^11 a^12 a^13 a^14 a^0 a^1 a^3 |0 a^3 a^4 a^5 a^6 a^7 a^8 a^9 a^10 a^11 a^12 a^13 a^14 a^0 a^1 a^2 a^4 |0 a^4 a^5 a^6 a^7 a^8 a^9 a^10 a^11 a^12 a^13 a^14 a^0 a^1 a^2 a^3 a^5 |0 a^5 a^6 a^7 a^8 a^9 a^10 a^11 a^12 a^13 a^14 a^0 a^1 a^2 a^3 a^4 a^6 |0 a^6 a^7 a^8 a^9 a^10 a^11 a^12 a^13 a^14 a^0 a^1 a^2 a^3 a^4 a^5 a^7 |0 a^7 a^8 a^9 a^10 a^11 a^12 a^13 a^14 a^0 a^1 a^2 a^3 a^4 a^5 a^6 a^8 |0 a^8 a^9 a^10 a^11 a^12 a^13 a^14 a^0 a^1 a^2 a^3 a^4 a^5 a^6 a^7 a^9 |0 a^9 a^10 a^11 a^12 a^13 a^14 a^0 a^1 a^2 a^3 a^4 a^5 a^6 a^7 a^8 a^10 |0 a^10 a^11 a^12 a^13 a^14 a^0 a^1 a^2 a^3 a^4 a^5 a^6 a^7 a^8 a^9 a^11 |0 a^11 a^12 a^13 a^14 a^0 a^1 a^2 a^3 a^4 a^5 a^6 a^7 a^8 a^9 a^10 a^12 |0 a^12 a^13 a^14 a^0 a^1 a^2 a^3 a^4 a^5 a^6 a^7 a^8 a^9 a^10 a^11 a^13 |0 a^13 a^14 a^0 a^1 a^2 a^3 a^4 a^5 a^6 a^7 a^8 a^9 a^10 a^11 a^12 a^14 |0 a^14 a^0 a^1 a^2 a^3 a^4 a^5 a^6 a^7 a^8 a^9 a^10 a^11 a^12 a^13 Henceforth, we compare the results of the generic Reed-Solomon Coding and the proposed fault tolerance technique [Table – 14] based on the initial code. Table 14.FAULT TOLLERANCERESULT Parameter Generic RS Proposed Optimized RS Initial a^1 a^3 a^5 a^1 a^3 a^5 Polynomial Encoded Data a^5 a^3 a^1 a^6 0 00 a^6 a^4 a^4 a^2 a^0 a^2 a^0 Fault a^5 a^3 a^1 a^6 a^6 a^4 a^2 1 Tolerance a^4 a^2 1 Code Optimization 0% 57% Reduction VII. CONCLUSION In this work the commercial cloud storage services are been compared based on the cost and performance factors. The result of the comparative measures provided the understanding of the demand for highly reliable and cost effective fault tolerance system. Henceforth, in this work we study the core Reed - Solomon fault tolerance mechanism based on Erasure codes. The work contributes towards the International Journal of Computer Science and Information Security (IJCSIS), Vol. 16, No. 3, March 2018 145 https://sites.google.com/site/ijcsis/ ISSN 1947-5500
- 6. improved performance code for fault tolerance for digital storage devices rather than magnetic. Also the work enhanced the performance of the proposed technique by applying the improvement in terms of optimization. The result of the proposed optimization technique is 57% reduction in the storage cost without negotiating with the fault tolerance reliability. REFERENCES [1] J. Kubiatowicz, D. Bindel, Y. Chen, P. Eaton, D. Geels, R. Gummadi, S. Rhea, H. Weatherspoon, W. Weimer, C. Wells, and B. Zhao, “Oceanstore: An Architecture for Global-Scale Persistent Storage,” Proc. Ninth Int’l Conf. Architectural Support for Programming Languages and Operating Systems (ASPLOS), pp. 190-201, 2000. [2] P. Druschel and A. Rowstron, “PAST: A Large-Scale, Persistent Peer-to-Peer Storage Utility,” Proc. Eighth Workshop Hot Topics in Operating System (HotOS VIII), pp. 75-80, 2001. [3] A. Adya, W.J. Bolosky, M. Castro, G. Cermak, R. Chaiken, J.R. Douceur, J. Howell, J.R. Lorch, M. Theimer, and R. Wattenhofer, “Farsite: Federated, Available, and Reliable Storage for an Incompletely Trusted Environment,” Proc. Fifth Symp. Operating System Design and Implementation (OSDI), pp. 1-14, 2002. [4] A. Haeberlen, A. Mislove, and P. Druschel, “Glacier: Highly Durable, Decentralized Storage Despite Massive Correlated Failures,” Proc. Second Symp. Networked Systems Design and Implementation (NSDI), pp. 143-158, 2005. [5] Q. Tang, “Type-Based Proxy Re-Encryption and Its Construction,” Proc. Ninth Int’l Conf. Cryptology in India: Progress in Cryptology (INDOCRYPT), pp. 130-144, 2008. [6] G. Ateniese, K. Benson, and S. Hohenberger, “Key-Private Proxy Re- Encryption,” Proc. Topics in Cryptology (CT-RSA), pp. 279-294, 2009. [7] J. Shao and Z. Cao, “CCA-Secure Proxy Re-Encryption without Pairings,” Proc. 12th Int’l Conf. Practice and Theory in Public Key Cryptography (PKC), pp. 357-376, 2009. [8] J. Bellorado and A. Kavcic , "Low-complexity soft-decoding algorithms for Reed-Solomon codes: An algebraic soft-in hard- out chase decoder" , IEEE Trans. Inf. Theory. , vol. 56 , no. 3 , pp.945 -959 , 2010 [9] F. Garc-Herrero, J. Valls and P. K. Meher , "High-speed RS (255, 239) decoder based on LCC decoding" , Circuits Syst. Signal Process. , vol. 30 , no. 6 , pp.1643 -1669 , 2011 [10] W. Zhang, H. Wang and B. Pan , "Reduced-complexity LCC Reed-Solomon decoder based on unified syndrome computation" , IEEE Trans. Very Large Scale Integr. (VLSI) Syst. , vol. 21 , no. 5 , pp.974 -978 , 2013 [11] J. Jiang and K. R. Narayanan , "Algebraic soft-decision decoding of Reed-Solomon codes using bit-level soft information" , IEEE Trans. Inf. Theory , vol. 54 , no. 9 , pp.2008 -3928. International Journal of Computer Science and Information Security (IJCSIS), Vol. 16, No. 3, March 2018 146 https://sites.google.com/site/ijcsis/ ISSN 1947-5500