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for CSE & IT Department students. In the mean time we have also launched a
separate IEEE / NON-IEEE Project development division only for CSE & IT
Students. Our moto is to provide the quality of education which would lead the
upcoming generation more strong in the technology.
HIGHLIGHTS:
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2. Projects developed by
Experts with rich experience in technology
3. We have been recognized
by leading news papers such as "INDIAN EXPRESS",
"DINAKARAN",
and “DHINAMALAR".
4. Organized various
workshops and conferences in leading colleges like
Remote data integrity checking is of crucial
importance in cloud storage. It can make the clients verify whether their
outsourced data is kept intact without downloading the whole data. In some
application scenarios, the clients have to store their data on multi-cloud
servers. At the same time, the integrity checking protocol must be efficient in
order to save the verifier’s cost. From the two points, we propose a novel remote
data integrity checking model: ID-DPDP (identity-based distributed provable
data possession) in multi-cloud storage. The formal system model and security
model are given. Based on the bilinear pairings, a concrete ID-DPDP protocol is
designed. The proposed ID-DPDP protocol is provably secure under the hardness
assumption of the standard CDH (computational Diffie- Hellman) problem. In
addition to the structural advantage of elimination of certificate management,
our ID-DPDP protocol is also efficient and flexible. Based on the client’s
authorization, the proposed ID-DPDP protocol can realize private verification, delegated
verification and public verification.
EXISTING SYSTEM:
The
foundations of cloud computing lie in the outsourcing of computing tasks to the
third party. It entails the security risks in terms of confidentiality,
integrity and availability of data and service. The issue to convince the cloud
clients that their data are kept intact is especially vital since the clients
do not store these data locally. Remote data integrity checking is a primitive
to address this issue. For the general case, when the client stores his data on
multi-cloud servers, the distributed storage and integrity checking are
indispensable. On the other hand, the integrity checking protocol must be efficient
in order to make it suitable for capacity-limited end devices. Thus, based on
distributed computation, we will study distributed remote data integrity
checking model and present the corresponding concrete protocol in multi-cloud
storage.
DISADVANTAGES OF
EXISTING SYSTEM:
vData
checking in more complex using multi servers.
vNeeded
large storage space.
vIn
sufficient data loss.
PROPOSED
SYSTEM:
In
identity-based public key cryptography, this paper focuses on distributed
provable data possession in multi-cloud storage. The protocol can be made
efficient by eliminating the certificate management. We propose the new remote
data integrity checking model: ID-DPDP. The system model and security model are
formally proposed. Then, based on the bilinear pairings, the concrete ID-DPDP
protocol is designed. In the random oracle model, our ID-DPDP protocol is
provably secure. On the other hand, our protocol is more flexible besides the
high efficiency. Based on the client’s authorization, the proposed ID-DPDP
protocol can realize private verification, delegated verification and public
verification.
ADVANTAGES OF PROPOSED
SYSTEM:
vIt has more significant storage space.
vIt provides secure public data’s.
vUsing Private Key generation.
SYSTEM ARCHITECTURE:
SYSTEM CONFIGURATION:-
HARDWARE REQUIREMENTS:-
Processor-Pentium
–IV
Speed-1.1 Ghz
RAM-512 MB(min)
Hard Disk-40 GB
Key Board-Standard Windows Keyboard
Mouse-Two or Three Button Mouse
Monitor-LCD/LED
SOFTWARE
REQUIREMENTS:
Operating
system:Windows XP.
Coding
Language:.Net
Data
Base:SQL Server 2005
Tool:VISUAL STUDIO 2008.
REFERENCE:
Wang,
H., “Identity-Based Distributed Provable
Data Possession in Multi-Cloud Storage” IEEE TRANSACTIONS ON CLOUD
COMPUTING, VOL. PP, NO. 99, 11 March 2014
PROBABILISTIC
CONSOLIDATION OF VIRTUAL MACHINES IN SELF-ORGANIZING CLOUD DATA CENTERS CLICK HERE TO VIEW THE OUTPUT
ABSTRACT:
Power efficiency is one of
the main issues that will drive the design of data centers, especially of those
devoted to provide Cloud computing services. In virtualized data centers, consolidation
of Virtual Machines (VMs) on the minimum number of physical servers has been
recognized as a very efficient approach, as this allows unloaded servers to be switched
off or used to accommodate more load, which is clearly a cheaper alternative to
buy more resources. The consolidation problem must be solved on multiple dimensions,
since in modern data centers CPU is not the only critical resource: depending
on the characteristics of the workload other resources, for example, RAM and
bandwidth, can become the bottleneck. The problem is so complex that centralized
and deterministic solutions are practically useless in large data centers with
hundreds or thousands of servers. This paper presents ecoCloud, a
selforganizing and adaptive approach for the consolidation of VMs on two
resources, namely CPU and RAM. Decisions on the assignment and migration of VMs
are driven by probabilistic processes and are based exclusively on local
information, which makes the approach very simple to implement. Both a fluid-like
mathematical model and experiments on a real data center show that the approach
rapidly consolidates the workload, and CPU-bound and RAM-bound VMs are
balanced, so that both resources are exploited efficiently.
EXISTING SYSTEM:
In the past few years
important results have been achieved in terms of energy consumption reduction, especially
by improving the efficiency of cooling and power supplying facilities in data
centers. The Power Usage Effectiveness (PUE) index, defined as the ratio of the
overall power entering the data center and the power devoted to computing
facilities, had typical values between 2 and 3 only a few years ago, while now
big Cloud companies have reached values lower than 1.1. However, much space
remains for the optimization of the computing facilities themselves. It has
been estimated that most of the time servers operate at 10-50 percent of their
full capacity [2], [3]. This low utilization is also caused by the intrinsic variability
of VMs’ workload: the data center is planned to sustain the peaks of load,
while for long periods of time (for example, during nights and weekends), the
load is much lower [4], [5]. Since an active but idle server consumes between
50 and 70 percent of the power consumed when it is fully utilized [6], a large
amount of energy is used even at low utilization.
DISADVANTAGES OF
EXISTING SYSTEM:
·It is power consuming.
·Large amount of energy is used even at low
utilization.
PROBLEM STATEMENT:
The ever increasing demand
for computing resources has led companies and resource providers to build large
warehouse-sized data centers, which require a significant amount of power to be
operated and hence consume a lot of energy.
SCOPE:
The optimal assignment of VM’s to reduce the power consumption.
PROPOSED SYSTEM:
We presented ecoCloud, an approach for consolidating
VMs on a single computing resource, i.e., the CPU. Here, the approach is
extended to the multidimension problem, and is presented for the specific case
in which VMs are consolidated with respect to two resources: CPU and RAM. With
ecoCloud, VMs are consolidated using two types of probabilistic procedures, for
the assignment and the migration of VMs. Both procedures aim at increasing the utilization
of servers and consolidating the workload dynamically, with the twofold
objective of saving electrical costs and respecting the Service Level
Agreements stipulated with users. All this is done by demanding the key decisions
to single servers, while the data center manager is only requested to properly
combine such local decisions. The approach is partly inspired by the ant
algorithms used first by Deneubourg et al. [9], and subsequently by a wide research
community, to model the behavior of ant colonies and solve many complex
distributed problems. The characteristics inherited by such algorithms make
ecoCloud novel and different from other solutions. Among such characteristics:
1) the use of the swarm intelligence paradigm, which allows a complex problem
to be solved by combining simple operations performed by many autonomous actors
(the single servers in our case); 2) the use of probabilistic procedures,
inspired by those that model the operations of real ants; and 3) the
self-organizing behavior of system, which ensures that the assignment of VMs to
servers dynamically adapts to the varying workload.
ADVANTAGES OF PROPOSED
SYSTEM:
·Efficient CPU usage.
·It reduces power consumption.
·Efficient
resource utilization.
SYSTEM
ARCHITECTURE:
SYSTEM CONFIGURATION:-
HARDWARE REQUIREMENTS:-
üProcessor-Pentium
–IV
üSpeed-1.1 Ghz
üRAM-512 MB(min)
üHard
Disk-40 GB
üKey
Board-Standard Windows Keyboard
üMouse-Two or Three Button Mouse
üMonitor-LCD/LED
SOFTWARE
REQUIREMENTS:
•Operating system:Windows XP
•Coding Language:Java
•Data Base:MySQL
•Tool:Net Beans IDE
REFERENCE:
Carlo Mastroianni, Michela Meo and Giuseppe Papuzzo“Probabilistic Consolidation of
Virtual Machines in Self-Organizing Cloud Data Centers”IEEE TRANSACTIONS ON
CLOUD COMPUTING, VOL. 1, NO. 2, JULY-DECEMBER 2013.
