This document discusses distributed load balancing across multiple data centers. It aims to achieve load balancing using a distributed system to increase performance and resource utilization, and to analyze large amounts of data using Hadoop. The key objectives are to distribute dynamic local workloads across nodes in cloud computing to avoid single points of failure, and to efficiently analyze huge amounts of data from data centers.

1. DISTRIBUTED LOAD BALANCING
AND MULTIPLE DATA CENTERS
ANALYSIS
Presented by:
Sowmya C
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2. CLOUD
Cloud computing is the delivery of computing services
over the Internet.
Characteristics of cloud
• On demand services.
• Broad network access.
• Reliability.
• Resource pooling.
• Rapid elasticity.
• Measured service.
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3. BIG DATA
Big data is similar to small data but bigger.
Having data bigger it requires different
approaches:
• Techniques ,tools and architecture.
Big data comes from sensor devices, video,
audio, networks, social media, transactional
applications.
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4. WHY BIG DATA?
Big data enables:
• Increased storage capacity
• Increased processing power
• Helps to make better business
decision
• Examining large amount of data.
• Effective marketing
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5. PROBLEM STATEMENT
• Load balancing is the main challenge in cloud
computing, centralized systems are subjected to
single point of failure hence it is required to
distribute the dynamic local workload across all the
nodes.
• The outcome of data centers is huge and it is
necessary to use an efficient technology to analyse
the data.
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6. OBJECTIVE
• Achieving load balancing in datacenters using
distributed load balancing system to increase
performance and resource utilization.
• Data analysis using an efficient tool called hadoop.
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7. Load balancing in data centers
Load balancing is the
process of improving the
performance of the
system by shifting of
workload among the
processors.
Data centers are the
locations containing a
group of servers.
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8. Types of load balancing
Static load balancing
 The decision of shifting the load
does not depend on the current
state of the system.
 Algorithms are non preemptive.
 Round Robin.
 Central Manager.
 Threshold algorithm.
 randomized algorithm
Dynamic load balancing
 current state of the system is used to
make any decision for load
balancing.
 Dynamic load balancing algorithms
are preemptive.
 Types of Dynamic load balancing
 Local Queue Algorithm.
 Central Queue algorithm.
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9. CENTRALIZED LOAD BALANCING
Limitations of centralized load
balancing
• Only suitable for WAN’s
where traffic is predictable
and stable.
• Example : google’s inter-
datacenters traffic
engineering algorithm needs
to run just 550 times per day
Existing system architecture
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Main
Controller
c1

10. DISTRIBUTED DATA CENTERS
Needs for distributed
systems :
• High speed of system.
• High performance
• Huge processing
power
Proposed system architecture
Distributed load balancer
S1 S2 S3 S4
App.A App.cApp.B
Network
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computer1 computer2 computer3 computer4

11. DISTRIBUTED LOAD BALANCING SYSTEM
Distribution systems can be defined as collection of computing
and communication resources located in distributed data centers
which are shared by several end users.
Advantages of distributed systems
• High performance
• Distribution
• Transparency
• Reliability
• Incremental growth
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12. Data
Need for big data
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13. HADOOP
Open source data storage and processing API.
Massively scalable and automatically parallelizable.
Core components :
• Hadoop common
• Hadoop distributed file system
• Map reduce
• YARN
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14. MAPREDUCE ABSTRACTION
Map returns
information •Map
Reduce
accepts
information
•combine
Reduces applies
a user defined
function to
reduce data
•reduce
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15. CONCLUSION
We can achieve high throughput, resource
utilization.
we can reach high user satisfaction.
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