An Efficient Approach For

8/3/2019 An Efficient Approach For

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A seminar on

AN EFFICIENT APPROACH FORDATA PLACEMENT IN

DISTRIBUTED

SYSTEMS


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WHAT WE WILL COVER?????

Introduction to Distributed systems

Data placement in distributed systems

Fragments allocation problem

Algorithm for data fragment allocation

Implementation results

Comparisons

Conclusion


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DISTRIBUTED SYSTEMSA distributed database system allows applications to

access data from local and remote databases.

Types of distributed systems:a)Client/server database system

b)Homogeneous DDB system

c)Heterogeneous DDB system


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A DISTRIBUTED SYSTEM


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TYPES OFDISTRIBUTED SYSTEMS

a)Client/server database system




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a)Client/Server

database system

A databaseserver is theOracle software

managing adatabase, and aclient is anapplication thatrequests

informationfrom a server.


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A homogenousdistributeddatabase systemis a network oftwo or moreOracleDatabases thatreside on one ormore machines.


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In aheterogeneousdistributeddatabasesystem, at least

one of thedatabases is anon-OracleDatabasesystem.


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DATA PLACEMENT IN DISTRIBUTED

SYSTEMS

Data placement is best possible allocation ofdata fragment in a distributive environment,based on

->fragment access patterns

->cost of moving data fragments from onesite to the other.


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->Fragmentaccess patterns:

Users at different sites have own set of informationrequirements:

1)unique to users at single node2)sharing among users at multiple nodes.

->Cost of moving datafragments:

The key factor that is to be considered in moving

the data fragments is cost.

Poor data allocation leads to higher costs in thenode or in the communication network.


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FRAGMENTS ALLOCATION PROBLEM

Fragments allocation problem is studied intwo environments:

* Static allocation is done prior to

design of the database.* Dynamic allocation is done based on

changing access patterns with focus on loadbalancing issues.


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ALGORITHM FORDATA ALLOCATION

Objective:The objective of the proposed fragment

allocation method is to determine which fragments are

used by each query being hosted at specific sites such

That all queries are satisfied while minimizing

communication cost, processing time, and storagecosts, and in the same time not violating storage

capacity and processing time constraints.


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SAGA ALGORITHMRole of GA:

1. Starts by generating 100 solutions(chromosomes)

2. This initial population is used to produce next generationusing operations selection, crossover, mutation.

3.The newly generated population will contain 100 offsprings.

4. This process is repeated 50 times to produce 50generations and it is calledTest-1.

5. Test-1 is run 100 times to obtain 100x50x100, a total of

500,000 solutions


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SAGA ALGORITHMu..

Role of SA:

1. Starts at the parents selection step of GA.

2. SA forces GA to select the parents from a

wider space of population by accepting low fitness

chromosomes (bad solutions) with the hope to

improve solutions in future generations.


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SAGA ALGORITHMu..

The entire operation of producing 500,000 solutions 4 times,

every time using different method:

First, using GA with random single-point crossover

(GA).

Second, using GA + SA by increasing thetemperature from 0 to 100 (SAGA 0-100).

Third, using GA + SA by decreasing the temperature

from 100 to 0 (SAGA 100-0).

Fourth, using GA + SA by fixing the temperature at

100 (SAGA 100).


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SAGA IMPLEMENTATION RESULTS

Let us consider the distributed database is containing

15 fragments that need to be allocated over 5 sites, and

it was assumed that each site requires specific

fragments as presented inTable 1 as shown below:

Site Required fragments

1. 6,9,10,12,13,14

2. 7,11

3. 3,4,5,6,10,12,13,14

4. 2,4,5,8,9,10,11,14

5. 1,2,3,6,10,15


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CONTINUEDuu..

The proposed SAGA allocation model considers only the

communication costs and attempts to find an allocationschema that minimizes the total cost of query processing.

Here it is also assumed that cost of data movement from

one site to other is only one unit.

To test SAGA algorithm we adopt abutterfly topology

1 3

2

5 4


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CONTINUEDuu

Let's see for example how the allocation cost for

fragment 9 is calculated. If we allocate fragment 9 to site 1,

given that fragment 9 was required by sites 1 and 4 . Then

the total cost of fragment 9 allocation is composed of two

costs:

Cost(9)=Cost(1, 9)+Cost(4, 9)= 0 + 2 = 2


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NUMBER OF SOLUTIONS PER COST


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AVERAGE COST PER GENERATION


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CONCLUSION

A SAGA approach for optimal allocation of data fragments

in a distributed environment was proposed, where the

mechanism for achieving this optimality relied on

knowing the cost involved in moving data fragments from

one site to the other.

In SAGA approach, different SAGA methods for

data allocation were employed. However, the

implementation confirmed that SAGA 100 outperformed

all other SAGA and GA methods as it helped us to reach

low cost solutions much faster.


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FUTURE ENHANCEMENTS

In future, fragments can be divided and each

fragment must be encrypted before it isallocated to a particular site.


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Presented by

R.SRIJA08751A1280

An Efficient Approach For

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