ABSTRACT:
Cloud
applications that offer data management services are emerging. Such clouds
support caching of data in order to provide quality query services. The users
can query the cloud data, paying the price for the infrastructure they use.
Cloud management necessitates an economy that manages the service of multiple
users in an efficient, but also,
resourceeconomic way that allows for cloud profit. Naturally, the maximization
of cloud profit given some guarantees for user satisfaction presumes an
appropriate price-demand model that enables optimal pricing of query services.
The model should be plausible in that it reflects the correlation of cache
structures involved in the queries. Optimal pricing is achieved based on a
dynamic pricing scheme that adapts to time changes. This paper proposes a novel
price-demand model designed for a cloud cache and a dynamic pricing scheme for
queries executed in the cloud cache. The pricing solution employs a novel
method that estimates the correlations of the cache services in a
time-efficient manner. The experimental study shows the efficiency of the
solution.
ARCHITECTURE:
|
User
 |
|
 |
Back _ End _ Database
ALGORITHM:
Global:
cache structures S, prices P, availability Δ
Query Execution (
)
if q
can be satisfied in the cache then
(result, cost)←runQueryInCache (q)
else
(result, cost)←runQueryInBackend (q)
end if
S←addNewStructures ()
return result, cost
optimalPricing (horizon
T, intervals t[i], S)
(Δ,P)←determineAvailability&Prices (T, t,S)
return Δ,P
main ()
execute in parallel tasks T1 and T2:
T1:
for every
new i do
slide the optimization window
OptimalPricin
(T, t[i], S)
end for
T2:
While new
query q do
(Result, cost)←query Execution (q)
end while
if q
executed in cache then
Charge cost to user
else
Calculate total price and charge price
to user
end if
EXISTING SYSTEM:
Existing clouds focus on the provision
of web services targeted to developers, such as Amazon Elastic Compute Cloud
(EC2), or the deployment of servers, such as Go Grid. There are two major
challenges when trying to define an optimal pricing scheme for the cloud
caching service. The first is to define a simplified enough model of the price
demand dependency, to achieve a feasible pricing solution, but not
oversimplified model that is not representative.
A static pricing scheme cannot be
optimal if the demand for services has deterministic seasonal fluctuations. The
second challenge is to define a pricing scheme that is adaptable to (i) Modeling errors, (ii) time-dependent model
changes, and (iii) stochastic behavior of the application. The demand for
services, for instance, may depend in a non predictable way on factors that are
external to the cloud application, such as socioeconomic situations.
Static
pricing cannot guarantee cloud profit maximization. In fact, as we show in our
experimental study, static pricing results in an unpredictable and, therefore,
uncontrollable behavior of profit. Closely related to cloud computing is research
on accounting in wide-area networks that offer distributed services. Mariposa
discusses an economy for querying in distributed databases. This economy is
limited to offering budget options to the users, and does not propose any
pricing scheme. Other solutions for similar frameworks focus on job scheduling and bid negotiation,
issues orthogonal to optimal pricing.
DISADVANTAGE:
Ø A
static pricing scheme cannot be optimal if the demand for services has
deterministic seasonal fluctuations.
Ø Static
pricing results in an unpredictable and, therefore, uncontrollable behavior of
profit.
PROPOSED SYSTEM:
The cloud caching service can maximize
its profit using an optimal pricing scheme. Optimal pricing necessitates an
appropriately simplified price-demand model that incorporates the correlations
of structures in the cache services. The pricing scheme should be adaptable to
time changes.
PRICE ADAPTIVITY TO TIME CHANGES:
Profit maximization is pursued in a
finite long-term horizon. The horizon includes sequential non-overlapping
intervals that allow for scheduling structure availability. At the beginning of
each interval, the cloud redefines availability by taking offline some of the
currently available structures and taking online some of the unavailable ones.
Pricing optimization proceeds in iterations on a sliding time-window that
allows online corrections on the predicted demand, via re-injection of the real
demand values at each sliding instant. Also, the
Iterative
optimization allows for re-definition of the parameters in the price-demand
model, if the demand deviates substantially from the predicted.
MODELING STRUCTURE CORRELATIONS:
Our approach models the correlation of
cache structures as a dependency of the demand for each structure on the price
of every available one. Pairs of structures are characterized as competitive,
if they tend to exclude each other, or collaborating, if they coexist in query
plans. Competitive pairs induce negative, whereas collaborating pairs induce
positive correlation. Otherwise correlation is set to zero. The index-index,
index column,
and
column-column correlations are estimated based on proposed measures that can
estimate all three types of correlation. We propose a method for the efficient
computation of structure correlation by extending a cache based query cost
estimation module and a template-based workload compression technique.
ADVANTAGE:
Ø A
novel demand-pricing model designed for cloud caching services and the problem
formulation for the dynamic pricing scheme that maximizes profit and
incorporates the objective for user satisfaction.
Ø An
efficient solution to the pricing problem, based on non-linear programming,
adaptable to time changes.
Ø A
correlation measure for cache structures that is suitable for the cloud cache
pricing scheme and a method for its efficient computation.
Ø An
experimental study which shows that the dynamic pricing scheme out-performs any
static one by achieving 2 orders of magnitude more profit per time unit.
MODULES:
QUERY EXECUTION:
The cloud cache is a full-fledged DBMS
along with a cache of data that reside permanently in back-end databases. The
goal of the cloud cache is to offer cheap efficient multi-user querying on the
back-end data, while keeping the cloud provider profitable. Service of queries is performed by executing them either
in the cloud cache or in the back-end database. Query performance is measured
in terms of execution time. The faster the execution, the more data structures
it employs, and therefore, the more expensive the service. We assume that the
cloud infrastructure provides sufficient amount of storage space for a large
number of cache structures. Each cache structure has a building and a
maintenance cost.
OPTIMAL Pricing:
We assume that each structure is built
from scratch in the cloud cache, as the cloud may not have administration
rights on existing back-end structures. Nevertheless, cheap computing and
parallelism on cloud infrastructure may benefit the performance of structure
creation. For a column, the building cost is the cost of transferring it from
the backend
and
combining it with the currently cached columns. This cost may contain the cost
of ntegrating the column in the existing cache table. For indexes, the building
cost involves fetching the data across the Internet and then building the index
in the cache.
Since
sorting is the most important step in building an index, the cost of building
an index is approximated to the cost of sorting the indexed columns. In case of
multiple cloud databases, the cost of data movement is incorporated in the
building cost. The maintenance cost of a column or an index is just the cost of
using disk space in the cloud. Hence, building a column or an index in the
cache has a one-time static cost, whereas their maintenance yields a storage
cost that is linear with time.
SYSTEM REQUIREMENTS:
HARDWARE
REQUIREMENTS:
Ø
System : Pentium
IV 2.4 GHz.
Ø
Hard
Disk : 40 GB.
Ø
Floppy
Drive :
1.44 Mb.
Ø
Monitor : 15 VGA Colour.
Ø
Mouse :
Logitech.
Ø Ram :
512 Mb.
SOFTWARE REQUIREMENTS:
Ø Operating system : Windows XP.
Ø Coding Language : ASP.Net with C#
Ø Data Base : SQL Server 2005
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