Dynamic Resource Allocation and Data Security for Cloud

International Journal of Innovative Research in Advanced Engineering (IJIRAE) ISSN: 2349-2163
Volume 1 Issue 6 (July 2014) http://ijirae.com
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© 2014, IJIRAE- All Rights Reserved Page - 272
Dynamic Resource Allocation and Data Security for Cloud
Rajesh M. Devakate Amol B. Rajmane
Annasaheb Dange College of Engg. & Technology, Ashokrao Mane Group of Institutions, Vathar Tarf
Ashta, Maharashtra, India. Vadgaon, Maharashtra, India.
Abstract— Cloud computing is the next generation of IT organization. Cloud computing moves the software and
databases to the large centres where the management of services and data may not be fully trusted. In this system, we
focus on cloud data storage security, which has been an important aspect of quality of services. To ensure the
correctness of user’s data in the cloud, we propose an effective scheme with Advanced Encryption Standard and MD5
algorithm. Extensive security and performance analysis shows that the proposed scheme is highly efficient. In
proposed work we have developed efficient parallel data processing in clouds and present our research project for
parallel security. Parallel security is the data processing framework to explicitly exploit the dynamic storage along with
data security. We have proposed a strong, formal model for data security on cloud and corruption detection.
Keywords— Cloud computing, Resource Allocation, cryptography Encryption, Decryption, corruption detection.
I. INTRODUCTION
Several trends are opening in cloud computing, which is an Internet-based and use of computer technology to
development. The cheaper and more powerful processors with the software as a service (SaaS) computing architecture are
transforming data centres into pools of computing service on a large scale. The increasing network bandwidth and reliable
network connections make it even possible that internet users can subscribe high quality services from data and software
that resides on remote centres. Moving data into the cloud storage offers great benefit to users since they don’t have to
care about the difficulties of direct hardware management. The colonizer of cloud computing vendors, Amazon Simple
Storage Service (S3) and Amazon Elastic Compute Cloud (EC2) [1] are both known examples. While these online
services provides large amounts of storage space and customizable computing resources, this platform shift, however, is
eliminating the responsibility of local machines for data storage and maintenance at the same time. As a result, users are at
the leniency of their cloud service providers for the availability and integrity of their data. From the perspective of data
security, which has always been an important part of quality of service, cloud computing poses new challenging security
threats for number of reasons. Such as traditional cryptographic system for the purpose of data security protection cannot
be directly adopted due to the user’s loss control of data under cloud storage. Therefore, verification of correct data stored
in the cloud must be conducted without knowledge of the whole data. Considering various kinds of data for each user
stored in the cloud and the demand of continuous assurance of their data safety, the problem of verifying correctness of
data storage in the cloud becomes even more challenging. Cloud computing is not just a third party data warehouse. The
data stored in the cloud may be updated by the users. However, this dynamic feature also makes traditional integrity
insurance techniques. The deployment of cloud computing is powered by data centres running in a simultaneous,
cooperated and distributed manner. Individual user’s data is redundantly stored in multiple locations to further reduce the
data integrity threats. Therefore, distributed protocols for storage correctness assurance will be of most importance in
achieving a robust and secure cloud data storage system in the real world. However, such important area remains to be
fully explored in the literature.
This techniques, while can be useful to ensure the storage correctness without having users possessing data and they are
all focusing on single server scenario and most of them do not consider dynamic data operations. As a complementary
approach, researchers have also proposed distributed protocols [8] for ensuring storage correctness across multiple servers.
Again, none of these distributed schemes is aware of dynamic data operations. As a result, their applicability in cloud data
storage can be drastically limited.
In this paper, we proposed an effective and flexible distributed scheme with explicit dynamic data support to ensure the
correctness of user’s data in the file distribution preparation to provide redundancies and guarantee the data dependability.
This construction drastically reduces the communication and storage overhead as compared to the traditional replication-
based file. To process/allocate jobs like customer documentation upload to cloud server, we will provide facility of admin
to feed input to main server i.e. documents will be uploaded by admin on main server. Further our system will
automatically process data in parallel feed to cloud nodes for storage. Obviously, these documents will be chunked in
multiple parts and with the encryption. Once admin/user uploads desired documents, the basic working platform will be
ready for actual research. The file/resources storage on cloud is common application of corporate cloud. But if any loss of
data occurred due to virus attack or hacking, it will be difficult to recover.
Proposed work focuses on security and corruption detection of data. For this purpose we developed own system which
co-actively work with parallel data processing approach. We also utilized concept of MD5 to tag files with document
identity number. Hence if any part of file missing system can recover data using anti-parallel resource algorithm.

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© 2014, IJIRAE- All Rights Reserved Page -273
II. LITERATURE REVIEW
Cloud computing is providing unlimited infrastructure to store and execute customer data and program. As customers
you do not need to own the infrastructure, they are merely accessing or renting; they can forego capital expenditure and
consume resources as a service, paying instead for what they use. Cloud storage can save users lots of money over local
storage solutions [1]. When user uses the cloud, user doesn’t know exactly where your data is hosted, what country it will
be stored in? Data should be stored and processed only in specific jurisdictions as define by user. Provider should also
make a contractual commitment to obey local privacy requirement on behalf of their customers. Data-centred policies that
are generated when a user provides personal or sensitive information, that information travels throughout its lifetime to
ensure that the information is used only in accordance with the policy [5]. Data that is generated during running of
program on instances is all customer data and therefore provider should not perform backups [7, 8].
Cong Wang investigated the problem of data security [2] in cloud data storage, which is essentially a distributed
storage system but doesn’t provide recovery facility. The processing frameworks which are currently used have been
designed for static, homogenous cluster setup and disregard the particular nature of cloud [3]. Fast and secure message
authentication in cryptography [4] provides splitting of file only supports for encryption and decryption of file in cloud
computing. Proofs of retrievability protocol represents the proof of data stored in server is intact and retrievable but not
support for file updates, as well as publicly verifiable PORs.
III. COMMENTS
As there is no facility of data recovery available in cloud domain, this is very crucial application to prove efficient
method for missing data recovery. The cloud’s virtualized nature helps to enable promising new use cases for efficient
parallel data processing. However, it also imposes new challenges compared to classic cluster setups. The major challenge
we see is the cloud’s opaqueness with prospect to exploiting data locality. For security reasons clouds often incorporate
network virtualization techniques which can hamper the inference process, in particular when based on latency
measurements.
To provide efficient parallel data processing for resource allocation we need to consider greater security while resources
being allocated, data loss prevention and efficient allocation of cloud storage resources. Many cloud infrastructures are
facing problems in parallel processing of data. Such problems may encounter due to hacking methodologies.
IV.NEED OF WORK
• To provide secure cloud storage for registered clients.
• To provide security against data hacking / server hacking
• To provide way for administrator to maintain user profiles and data profiles.
• To provide security for files through encryption concept.
• To provide multi-server facility for data storage i.e. to make multi-parts of client files.
• To allocate resources / files based upon storage capacity.
• To recover infected or corrupted resources /files.
V. PROPOSED WORK
To provide parallel data allocation and data security we have stored data on multiple cloud nodes and in case if any
data/part of file is missing or virus infected then we can able to recover data/resources. For recovery purpose we have
implemented self-derived algorithm which collectively work with MD5, dynamic data allocation approach and logical
addresses of file. We aim to recover data 100% which is not possible with existing systems.
In our proposed system client can be able to register with cloud service provider. Registered client have to login to
upload file which he/she want to store on cloud. Here client expects higher level of security for his/her resources. In this
system server receives resources uploaded by client. Server reviews received resource to filter banned data/documentation.
The main server receives resources uploaded by client. Consider user have upload “test.txt” file proposed algorithm
encrypts the file and splits into number of data chunks. These splitted data chunks parallelly processed to store on
auxiliary cloud server-1 and auxiliary cloud server-2 shown in figure 1. This system provides more security for data and
there will not be any direct access to user to auxiliary server’s data. We have implemented MD5 algorithm to check all
files are stored as it is or any files are modified due to impact of any mishap. In case of data corruption or missing file part,
we can recover original data from cloud server by using MD5 hash value.
A. Client Registration Facility for New Clients & Login Facility for Existing Clients
We have developed client registration and login window. This will register new user with system to avail system
facility. In this module existing client can log-in to upload his/her data file. If user is registered then he/she gets ID
& password. At the time of login user has to provide correct ID & password. Then system checks the user ID &
password if both are correct then user logged into the system. Unauthorized person cannot be able to login into the
system.

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Figure 1: System Architecture
B. File Encryption and Upload
In this module, when user is registered he/she uploads file on main server and the main server encrypts the file
and stored on server.
Figure 2: AES Encryption
The Advanced Encryption Standard (AES) algorithm is implemented for encryption of the data shown in figure 2.
The main server receives files uploaded by client. Then file is splitted into number of data chunks. The AES
algorithm will encrypt the splitted file parts. These data will be parallelly processed to store on nodes.
C. File Decryption and Download
In this module, user can download the files from server. The main server decrypts the file using AES algorithm
and gives to the user. The AES algorithm is also implemented for decryption of the data. This will provide more
security for data and there will not be any direct access of user to auxiliary server’s data.
D. Dynamic Resource Allocation
In this module, we have created virtual nodes for allocating different servers for storage. In our project, we have
created nodes for storing the files shown in figure 3. Suppose file is encrypted and stored on different nodes. When
we are trying to download file then file parts are collected from various nodes.
We have presented a system that uses virtualization technology to store data resources (file) dynamically and
support green computing by optimizing the number of servers in use. We have combined different types of
workloads (storage) nicely and improved the overall utilization of server resources. We have developed a set of
heuristics that prevent overload in the system effectively while saving energy used.

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Figure 3: Node Information
E. File Corruption Detection and Recovery
In this module, we have implemented MD5 algorithm. User’s file is splitted and encrypted using AES algorithm.
That file parts are stored on different nodes. By using MD5, we have calculated hash value and stored on database.
When user wants to download his/her file then again hash value of current file is calculated and verified with old
hash value. If both hash values are matched then user gets his original file. If hash values are not same then we can
say that file parts are corrupted or infected. When we know that file is corrupted then we can recover this file.
VI.RESULTS
TABLE I
File & Fragmentation size for 2 Node
File size Fragment
sizeF1 50KB 25KB
F2 50MB 25MB
F3 500MB 250MB
F4 1GB 512MB
Table I shows the file size and fragment size. We are using two nodes for storage then file is splitted into two parts.
TABLE II
Comparison of Encryption Algorithms
File
Average Encryption time
(Milliseconds)
AES128 AES256 DES
F1 0.00055 0.0008 0.00135
F2 0.0006 0.00095 0.00155
F3 0.0007 0.0012 0.0018
F4 0.00095 0.0014 0.0020
Table II shows the file and time required for encryption. We have taken three different algorithms for comparison
viz. Advanced Encryption Standard (128bits), AES (256bits) and Data Encryption Standard.

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Figure 4: Comparison of three encryption algorithms
Figure (4) shows the result of comparison of different encryption algorithms such as Advanced Encryption Standard
(AES128 & AES256) and Data Encryption Standard (DES). The obtained results from different encryption algorithms are
used to analyze the result of proposed system. The obtained results from encryption algorithms are compared & after
comparing the obtained results it shows the AES128 is efficient algorithm for encryption and decryption.
VII. CONCLUSION
We have implemented the system to provide data security and ensure the correctness of user’s data in cloud. Security
is provided through the encryption and decryption technique. Also system is implemented for parallel processing of data.
We provide dynamic resource allocation by creating virtual nodes and data recovery in cloud computing. So user can
recover his/her data if infected or corrupted.
ACKNOWLEDGEMENT
I would like to thanks my guide Prof.A.B.Rajmane for his valuable and constructive comments. I would also like to
thanks Prof. Mrs. A.N. Mulla, HOD Computer Science & Engineering Department, Annasaheb Dange College of
Engineering & Technology, Ashta for her valuable support.
REFERENCES
[1] Amazon.com. Amazon simple storage service (Amazon S3), 2009.
[2] Cong Wang, Qian Wang, Kui Ren and Wenjing Lou “Ensuring Data Storage Security in Cloud Computing” ,IEEE
2009.
[3] Daniel Warneke, Member, IEEE, and Odej Kao. Dynamic resource allocation for efficient parallel data processing in
cloud, 2011.
[4] D. Warneke and O. Kao, “Nephele: Efficient Parallel Data Processing in the Cloud,” Proc. Second Workshop Many-
Task Computing on Grids and Supercomputers (MTAGS ’09), pp. 1-10, 2009.
[5] G. Ateniese, R. Burns, R. Curtmola, J. Herring, L. Kissner, Z. Peterson, and D. Song. Provable data possession at
untrusted stores. In 14th ACM CCS, pages 598–609, 2007.
[6] J. Black, S. Halevi, H. Krawczyk, T. Krovetz, and P. Rogaway. UMAC: Fast and secure message authentication. In
CRYPTO, volume 1666 of LNCS, pages 216–233, 1999.
[7] K. D. Bowers, A. Juels, and A Oprea: A high-availability and integrity layer for cloud storage, 2008. IACR ePrint
manuscript 2008/489.
[8] K. D. Bowers, A. Juels, and A Oprea. Proofs of retrievability: Theory and implementation, 2008. IACR ePrint
manuscript 2008/175.
[9] D. Nurmi, R. Wolski, C. Grzegorczyk, G. Obertelli, S. Soman, L.Youseff, and D.Zagorodnov, “Eucalyptus: A
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Dynamic Resource Allocation and Data Security for Cloud

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