35 content distribution with dynamic migration of services for minimum cost using aes

International Journal of Advanced Engineering, Management and Science (IJAEMS) [Vol-2, Issue-12, Dec- 2016]
Infogain Publication (Infogainpublication.com) ISSN : 2454-1311
www.ijaems.com Page | 2161
Content Distribution with Dynamic Migration of
Services for Minimum Cost using AES
P.Siva Rajesh
M.Tech Student, Dept of CSE, S.R.K.R engineering college, Bhimavaram, AP, India.
Abstract— Content Delivery Networks are the key for
today’s internet content delivery. Users are knowingly or
unknowingly accessing the CDN via internet. No matter
how much the data retrieved by the user it may contain the
CDN hand behind every character of text and every pixel of
image. CDN came into existence to solve the delay problem.
The moment when a user requests for a web page and the
response delivered to the corresponding users web browser
facing a huge delay. The main goal of this paper is content
distribution of web services to multiple data centers placed
in different geographical locations and providing security.
A content distribution service is a major part of popular
Internet applications. In proposed system hybrid clouds are
used i.e., both private cloud as well as public cloud. One
data center is allocated to each region. Providing security
to the data is always an important issue because of the
critical nature of the cloud and very large amount of
complicated data it carries. To provide security cipher text
policy algorithm is used. Authentication technique is used
to verify the user authentication. If the user is authorized to
access services then and only he receives configuration key
to use.
Keywords— Content Delivery Networks, Content
Distribution, AES.
I. INTRODUCTION
Now a day’s Cloud computing technology is used rapidly to
access resources for various applications. There are
different types of resources they are Computational
resources, Networking resources (Computational resources
such as Memory, CPU, Storage and Networking resources
such as Bandwidth). Cloud provider’s activities for
utilization and allocating resources are within time of cloud
environment. It requires the type and resources needed by
each application to complete a user job. Order and time of
allocation of resources are also an input for optimal
resources allocation. In proposed system we used hybrid
cloud for utilization of public cloud and private cloud. CDN
(Content Distribution Network) when it accessing data it
requests the cloud server and it search file on CDN. DCN
(Data Content Network) it provides access key to user to
access file. In dynamic migration technique used cold spot
and hot spot. Hot spot technique is used to distribute load.
This technique is used to achieve load balancing, increase
performance as well as throughput.
A content delivery network or content distribution network
(CDN) is a large distributed system of proxy servers
deployed in multiple data centers via the Internet. The goal
of a CDN is to serve content to end-users with high
availability and high performance. CDNs are widely used in
today’s Internet topography, internet traffic is significantly
increasing now-a-days. A high percentage of traffic is
reduced due to the usage of CDNs and they efficiently
deliver the contents and it’s related services. A content
delivery network is a highly-distributed platform of servers
that responds directly to end user requests for web content.
CDNs, carry nearly half of the world’s Internet traffic.
They are omnipresent by their presence and diminish the
challenges of delivering content over the Internet.
The main goal of the proposed system is to minimize the
operational cost over time for cloud provider as well as
achieving load balancing and provide security. To provide
security cipher text policy algorithm is used. Applying
authentication techniques are used to verify the user
authentication. If user is authorized to access services then
and only he receives configuration key to use. User can
access or used only key access pages. User cannot access or
use other pages.
II. RELATED WORK
Shaolei Ren, Yuxiong He and FeiXu[1] et al. limited
computational resources need to fairly allocated among
different organizations. Resources are allocated to end user
on demand. Fei Xu. et al. proposed the GreFar algorithm
which is optimizing energy cost and farness among
different organizations. This algorithm is achieving energy
cost, latency as well as fairness.

Pathan et al[2] To realize this objective, during this paper,
they measure the utility of content delivery via MetaCDN,
capturing the system-specific perceived edges. They have a
tendency to use this utility live to plot a request-redirection
policy that ensures high performance content delivery. we
have a tendency to conjointly quantify a content provider’s
edges from exploitation MetaCDN supported its user
perceived performance. Chen et al.[3] proposes to create
CDNs within the cloud so as to attenuate value beneath the
constraints of QoS demand, however they solely propose
greedy-strategy based heuristics while not obvious
properties. In distinction, we target an optimization
framework that renders optimal migration solutions for end
of the day of the system.
Tang et al.[4] investigates the QoS-aware duplicate
placement issues for responsiveness QoS necessities.
During this paper they thought-about 2 categories of service
models: replica-aware services and replica-blind services. In
replica-aware services, the servers are alert to the locations
of replicas and might thus optimize request routing to boost
responsiveness. we have a tendency to show that the QoS-
aware placement downside for replica-aware services is NP-
complete. In blind services, the servers aren't alert to the
locations of replicas or perhaps their existence. As a result,
every duplicate solely serves the requests flowing through it
beneath some given routing strategy. economical algorithms
are projected to reckon the best locations of replicas beneath
totally different value models.
Charikar et al. proposed a 4-approximation algorithm for
solving the minimum K-median problem in [5]. This is so
far the best known approximation algorithm in the worst
case bound for the metric K-median problem. Kalpakis et
al. [6] proposed an algorithm which considers all three costs
(retrieval, update and storage). In this paper they considered
only tree topology. But none of the works is related to
provisioning cost between replica sites is relevant to the
replication direction.
III. PROBLEM STATEMENT
The content distribution mainly involves two key points
they are (i) back-end storage of the contents and (ii) front-
end internet services that serve the user requests for
contents. The data owner might migrate each service parts
into the general public cloud: contents will be replicated in
storage servers within the cloud, while requests will be sent
to internet services put in on VMs on the computing servers.
Our objective during this paper is to model a dynamic,
optimal algorithm is to strategically make the subsequent
selections for service migration into the hybrid cloud
architecture: (i) content replication: which content ought to
be replicated during which data center at every time? (ii)
providing security: How the content owner should securely
migrate data to his chosen data center?
Our algorithm focuses on minimizing recurring operational
cost of the content distribution system, not one-time costs
such as the purchase of machines in the cloud and contents.
The content distribution mainly depends on how efficiently
replicating the content in different data centers located at
different geographical locations. The data owner has the
freedom to select the data centers provide by different
organizations.
IV. PROPOSED SYSTEM
In proposed system used techniques is Lyapunov
optimization technique for minimization of cost. Their need
to update cost dynamically when number of user requests
from the cloud server increases. Lyapunov optimization is
a powerful technique for optimizing time averages in
stochastic queuing networks. Work in presents a drift-plus-
penalty theorem that provides a methodology for designing
control algorithms to maximize time average network utility
subject to queue stability.
There is a public cloud consisting of data centers located in
multiple geographical locations, denoted as set N. One
knowledge center resides in every region. There are two
forms of inter-connected servers in every knowledge center:
storage servers for data storage, and computing servers that
support the running and provisioning of virtual machines
(VMs). Servers within identical knowledge center can
access one another via a particular DCN.
An illustration of system architecture is given in fig.1. Data
owner Browse Files and encode data and generate RSA
Secret key. Data owner select the data centre and cloud
server name from different data centers provided by
different organizations. Data owner Upload data to
corresponding cloud service provider. Data owner Verify
the data from the cloud by SHA-512. Data owner is able to
see the log about the cost and space of the cloud as soon as
the control center updates in the corresponding cloud.

Fig.1: System Architecture
Control center is the key in our architecture which
minimizes the cost. It chooses the low cost cloud and
migrate the data owner contents dynamically and inform to
data owner as a log about the data migration in the data
centre. Data center receive all files from the data owner and
check the corresponding cloud details and its storage
services and store all files, Update or Assign memory for all
VMS(vm1,vm2,vm3) for each cloud. It checks the data
integrity in the cloud and inform to end user about the data
integrity from the external attackers. It lists all cloud servers
and their storage details (Memory, cost and owner validity)
and keep track of information about different clouds.
Provide data migration from one to another cloud based on
the cost.
AES ENCRYPTION ALGORITHM:
To providing security to the data is always having a
importance issue because of the critical nature of the cloud
and very large amount of complicated data it carries, the
need is even important. Therefore, data security and privacy
issues that need to be solved have they are acting as a major
obstacle in adopting cloud computing services.
It is a web tool to encrypt and decrypt text using AES
encryption algorithm. You can chose 128, 192 or 256-bit
long key size for encryption and decryption. The result of
the process is downloadable in a text file.
1. Key expansion: From key schedules derives round key
from its ciphers.
2. Initial round: a. Add round key – by using bitwise XOR
combine each bit with round key.
3. Rounds:
a. Sub bytes – each byte is replaced with another byte
using a look-up table as a non-linear substitution.
b. Shift rows – each row is shifted cyclically to a
number of times called transposition.
c. Mix the columns – combines four bytes in each
column.
d. Add round key
4. Final round – a. Sub bytes b. Shift rows c. Add round key
Encryption algorithm:
1) Inverse shift rows
2) Inverse substitute bytes
3) Add round key- step consists of XORing the output of
the previous two steps
4) Inverse mix columns
V. CONCLUSIONS
In this paper studied various techniques and algorithms are
load balancing, job scheduling to utilization of resources in
proper ways. Virtualization techniques is one of the
powerful feature of the cloud computing. Using the
Lyapunov optimization technique which can minimizes the
operational cost of the application with Quality of service
guarantees. Applying AES algorithm we produced a secure
data migration from data owners to the data centers.
Achieving efficient load balancing as well as provide high
security.
REFERENCES
[1] Shaolei Ren, Yuxiong He and Fei Xu,” Provably-
Efficient Job Scheduling for Energy and Fairness in
Geographically Distributed Data Centers,” IEEE
International Conference on Distributed Computing
Systems, 1063-6927/12 $26.00 © 2012 IEEE DOI
10.1109/ICDCS.2012.77.
[2] F. Chen, K. Guo, J. Lin, and T. L. Porta, “Intra-cloud
Lightning: Building CDNs in the Cloud,” in Proc. of
IEEE INFOCOM, 2012.
[3] M. Pathan, J. Broberg, and R. Buyya, “Maximizing
Utility for Content Delivery Clouds,” in Proc. of the
10th International Conference on Web Information
Systems Engineering, 2009.

[4] M. Charikar, and S. Guha. Improved Combinatorial
Algorithms for the Facility Location and K-Median
Problems. In Proc. of the 40th Annual IEEE
Conference on Foundations of Computer Science,
1999.
[5] Tang, Xueyan, and Jianliang Xu. "QoS-aware replica
placement for content distribution." IEEE
Transactions on Parallel and Distributed
Systems 16.10 (2005): 921-932.
[6] K. Kalpakis, K. Dasgupta, and O. Wolfson, “Optimal
placement of replicas in trees with read, write, and
storage costs,” IEEE Transactions on Parallel and
Distributed Systems, pp. 628–637, 2001.

35 content distribution with dynamic migration of services for minimum cost using aes

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35 content distribution with dynamic migration of services for minimum cost using aes