IEEE 2014 JAVA CLOUD COMPUTING PROJECTS Performance and cost evaluation of an adaptive encryption architecture for cloud databases

GLOBALSOFT TECHNOLOGIES
Performance and cost evaluation of an adaptive
encryption architecture for cloud databases
Abstract:
The cloud database as a service is a novel paradigm that can support several
Internet-based applications, but its adoption requires the solution of information
confidentiality problems. We propose a novel architecture for adaptive encryption
of public cloud databases that offers an interesting alternative to the trade-off
between the required data confidentiality level and the flexibility of the cloud
database structures at design time. We demonstrate the feasibility and performance
of the proposed solution through a software prototype. Moreover, we propose an
original cost model that is oriented to the evaluation of cloud database services in
plain and encrypted instances and that takes into account the variability of cloud
prices and tenant workload during a medium-term period.
Existing System:
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The cloud computing paradigm is successfully converging as the fifth utility , but
this positive trend is partially limited by concerns about information confidentiality
and unclear costs over a medium-long term .We are interested in the Database as a
Service paradigm (DBaaS) that poses several research challenges in terms of
security and cost evaluation from a tenant’s point of view. Most results concerning
encryption for cloud-based services are in applicable to the database paradigm.
Other encryption schemes, which allow the execution of SQL operations over
encrypted data, either suffer from performance limits or they require the choice of
which encryption scheme must be adopted for each database column and
SQL operations .
Proposed System:
The proposed architecture guarantees in an adaptive way the best level of data
confidentiality for any database workload, even when the set of SQL queries
dynamically changes. The adaptive encryption scheme, which was initially
proposed for applications not referring to the cloud, encrypts each plain column
into multiple encrypted columns, and each value is encapsulated into different
layers of encryption, so that the outer layers guarantee higher confidentiality but
support fewer computation capabilities with respect to the inner layers. we propose
the first analytical cost estimation model for evaluating cloud database costs in
plain and encrypted instances from a tenant’s point of view in a medium-term
period. It takes also into account the variability of cloud prices and the possibility
that the database workload may change during the evaluation period. This model is
instanced with respect to several cloud provider offers and related real prices. As
expected, adaptive encryption influences the costs related to storage size and

network usage of a database service. However, it is important that a tenant can
anticipate the final costs in its period of interest, and can choose the best
compromise between data confidentiality and expenses.
Architecture :
Implementation Modules:
1. Adaptive encryption
2. Metadata structure
3. Encrypted database management
4. Cost Estimation of cloud database services
5. Cost model

6. Cloud pricing models
7. Usage Estimation
Adaptive encryption:
The proposed system supports adaptive encryption methods for public cloud
database service, where distributed and concurrent clients can issue direct SQL
operations. By avoiding an architecture based on one [or] multiple intermediate
servers between the clients and the cloud database, the proposed solution
guarantees the same level of scalability and availability of the cloud service. Figure
1 shows a scheme of the proposed architecture where each client executes an
encryption engine that manages encryption operations. This software module is
accessed by external user applications through the encrypted database interface.
The proposed architecture manages five types of information.
• plain data is the tenant information;
• encrypted data is stored in the cloud database;
• plain metadata represent the additional information that is necessary to execute
SQL operations on encrypted data;
• encrypted metadata is the encrypted version of the metadata that are stored in the
cloud database;
• master key is the encryption key of the encrypted metadata that is distributed to
legitimate clients.
Metadata structure:

Metadata include all information that allows a legitimate client knowing the master
key to execute SQL operations over an encrypted database. They are organized and
stored at a table-level granularity to reduce communication overhead for retrieval,
and to improve management of concurrent SQL operations. We define all metadata
information associated to a table as table metadata. Let us describe the structure of
a table metadata .Table metadata includes the correspondence between the plain
table name and the encrypted table name because each encrypted table name is
randomly generated. Moreover, for each column of the original plain table
it also includes a column metadata parameter containing the name and the data
type of the corresponding plain column (e.g., integer, string, timestamp). Each
column metadata is associated to one or more onion metadata, as many as the
number of onions related to the column.
Encrypted database management:
The database administrator generates a master key, and uses it to initialize the
architecture metadata. The master key is then distributed to legitimate clients. Each
table creation requires the insertion of a new row in the metadata table. For each
table creation, the administrator adds a column by specifying the column name,
data type and confidentiality parameters. These last are the most important for this
paper because they include the set of onions to be associated with the column, the
starting layer (denoting the actual layer at creation time) and the field
confidentiality of each onion. If the administrator does not specify the
confidentiality parameters of a column, then they are automatically chosen by the
client with respect to a tenant’s policy. Typically, the default policy assumes that
the starting layer of each onion is set to its strongest encryption algorithm.

Cost Estimation of cloud database services:
A tenant that is interested in estimating the cost of porting its database to a cloud
platform. This porting is a strategic decision that must evaluate confidentiality
issues and the related costs over a medium-long term. For these reasons, we
propose a model that includes the overhead of encryption schemes and variability
of database workload and cloud prices. The proposed model is general enough to
be applied to the most popular cloud database services, such as Amazon Relational
Database Service.
Cost model:
The cost of a cloud database service can be estimated as a function of three main
parameters:
Cost = f(T ime, Pricing,Usage) where:
• Time: identifies the time interval T for which the tenant requires the service.
• Pricing: refers to the prices of the cloud provider for subscription and resource
usage; they typically tend to diminish during T .
• Usage: denotes the total amount of resources used by the tenant; it typically
increases during T .In order to detail the pricing attribute, it is important
to specify that cloud providers adopt two subscription
policies: the on-demand policy allows a tenant to payper-use and to withdraw its
subscription anytime; the reservation policy requires the tenant to commit in
advance for a reservation period. Hence, we distinguish between billing costs
depending on resource usage and reservation costs denoting additional fees for
commitment in exchange for lower pay-per-use prices. Billing costs are billed
periodically to the tenant every billing period.

Cloud pricing models:
Popular cloud database providers adopt two different billing functions, that we call
linear L and tiered T . Let us consider a generic resource x, we define as xb its
usage at the b-th billing period and px b its price. If the billing function is tiered,
the cloud provider uses different prices for different ranges of resource usage. Let
us define Z as the number of tiers, and [ˆx1, . . . , ˆxZ−1] as the set of thresholds
that define all the tiers. The uptime and the storage billing functions of Amazon
RDS are linear, while the network usage is a tiered billing function. On the other
hand, the uptime billing functions of Azure SQL is linear, while the
storage and network billing functions are tiered.
Usage Estimation:
The uptime is easily measurable, it is more difficult to estimate accurately the
usage of storage and network , since they depend on the database structure, the
workload and the use of encryption. We now propose a methodology for the
estimation of storage and network usage due to encryption. For clarity, we define
sp, se, sa as the storage usage in the plaintext, encrypted, and adaptively encrypted
databases for one billing period. Similarly, np, ne, na represent network usage of
the three configurations. We assume that the tenant knows the database structure
and the query workload and we assume that each column a A stores ra values. By
denoting as vp a the average storage size of each plaintext value stored in column
a, we estimate the storage of the plaintext database.

System Configuration:
HARDWARE REQUIREMENTS:
Hardware - Pentium
Speed - 1.1 GHz
RAM - 1GB
Hard Disk - 20 GB
Floppy Drive - 1.44 MB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Monitor - SVGA
SOFTWARE REQUIREMENTS:
Operating System : Windows
Technology : Java and J2EE
Web Technologies : Html, JavaScript, CSS
IDE : My Eclipse
Web Server : Tomcat
Tool kit : Android Phone
Database : My SQL
Java Version : J2SDK1.5

IEEE 2014 JAVA CLOUD COMPUTING PROJECTS Performance and cost evaluation of an adaptive encryption architecture for cloud databases

IEEE 2014 JAVA CLOUD COMPUTING PROJECTS Performance and cost evaluation of an adaptive encryption architecture for cloud databases

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IEEE 2014 JAVA CLOUD COMPUTING PROJECTS Performance and cost evaluation of an adaptive encryption architecture for cloud databases