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8/3/2019 An Efficient Approach For
1/23
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
b)Homogeneous DDB system
c)Heterogeneous DDB 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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b)Homogeneous DDB system
A homogenousdistributeddatabase systemis a network oftwo or moreOracleDatabases thatreside on one ormore machines.
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c)Heterogeneous DDB system
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