Based on paper: A QOS aware self-correcting observation based load balancer.

1. A QoS-aware self-correcting observation based
load balancer
Elsevier 2016
Hadi Rasouli
srbiau.ac.ir
April 2017
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2. List Of Contents
• Introduction
• Related Works
• QoS-aware self-correcting observation based load balancer (QSLB)
• Implementation
• Experimental results
• Conclusion and future work
• References
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3. Introduction
• Brief explanation
• Load Balancer
• Quality of Service (QoS)
3

4. Introduction
• Problem analysis
• Estimate the capabilities of the servers
• Administering the capacity of the cluster
• Performance evaluation
• Our Approach:
A QoS-aware self-correcting observation based
load balancer
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5. Introduction
• SSAL Architecture (Sliding window based Self-Learning and Adaptive Load Balancer )
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6. Related works
• Introduction
• Related Works
• QoS-aware self-correcting observation based load balancer (QSLB)
• Implementation
• Experimental results
• Conclusion and future work
• References
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7. Related works
• The problems addressed in this paper
• Load balancing
• Fault tolerance
• State replication
• QoS monitoring
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8. Related works
• The problems addressed in this paper
• Load balancing
• Static load balancing algorithms
• Bio inspired algorithms
• Genetic Algorithms
• Game theory based algorithms
• Fault tolerance
• State replication
• QoS monitoring
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9. QoS-aware self-correcting observation based load
balancer (QSLB)
• Introduction
• Related Works
• QoS-aware self-correcting observation based load balancer (QSLB)
• Implementation
• Experimental results
• Conclusion and future work
• References
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10. QSLB
• Features
• QSLB optimizes the overall throughput even in unstable server environments
• The servers’ capability information estimated by a QSLB can be borrowed by
any other QSLB that is subsequently started.
• The QSLBs periodically exchange the state information (Learning)
• Make corrections if needed
• by using a centralized algorithm
• by using a distributed algorithm
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11. QSLB
• QSLB logical view
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12. QSLB
• Manage the servers’ capability information for the entire system
• Centralized methods
• A QSLB acting as the coordinator (QSLBC)
• An external entity as coordinator
• Distributed methods
• Distributed Correction using Multicast (DCM)
• Publish-Subscribe Model (PSM)
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13. Implementation
• Introduction
• Related Works
• QoS-aware self-correcting observation based load balancer (QSLB)
• Implementation
• Experimental results
• Conclusion and future work
• References
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14. Implementation
• To implement
• The QSLBs need to communicate with each other to participate
in the Election algorithm to select the Coordinator, and periodically make a
correction to the servers’ capability information.
• Distributed methods(methods are multicast)
• Communication with an external entity like the Notification Service
• Naming service
• As the QSLB extends the SSAL, it is also a multi-threaded Java process
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15. Implementation
• To implement
• The QSLB is developed using the Java SE 1.7
• The QSLB Messages are serializable and transported using the Java
serialization mechanism
• Each server’s capability is specified in MIPS
• The bandwidth of the network used in our experiments is 100 Mbps
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16. Experimental results
• Introduction
• Related Works
• QoS-aware self-correcting observation based load balancer (QSLB)
• Implementation
• Experimental results
• Conclusion and future work
• References
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17. Experimental results
• All the experiments were conducted using the QSLB acting as
the Coordinator (QSLBC) architecture.
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18. Experimental results
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19. Experimental results
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20. Experimental results
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21. Experimental results
• Models to estimate the cluster capacity changes needed to meet the QoS
• ARSM correction estimates
• NRPM correction estimates
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22. Experimental results
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23. Experimental results
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24. Conclusion and future work
• Introduction
• Related Works
• QoS-aware self-correcting observation based load balancer (QSLB)
• Implementation
• Experimental results
• Conclusion and future work
• References
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25. Conclusion and future work
• The SSAL is an observation based load balancer
• When the SSAL fails, none of the user requests will reach the servers
and the SSAL becomes the single point of failure for the overall
system
• we proposed a QoS-aware and Self-correcting observation based
Load Balancer (QSLB) that extends the SSAL to make
it more fault tolerant
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26. Conclusion and future work
• The QSLB also provides additional functionality to (i)
set and monitor the QoS parameter benchmarks, and (ii) find the
cluster capacity changes needed to meet the benchmarks.
• Future work
• With the current QSLB model, each QSLB uses its own Input
Queue. When the QSLB crashes, all the requests in the Input Queue
are simply dropped. This problem can be solved by persisting the requests in
the Input Queue to a permanent storage area, and removing a request from
the storage area when it is completed.
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27. References
• Introduction
• Related Works
• QoS-aware self-correcting observation based load balancer (QSLB)
• Implementation
• Experimental results
• Conclusion and future work
• References
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28. References
• Goel, S., Buyya, R., 2015. Data Replication Strategies in Wide Area
Distributed Systems. available: http://jarrett.cis.unimelb.edu.au/papers/
DataReplicationInDSChapter2006.pdf. [Online; (accessed 15.07.21)].
Gopinath, P.G., Vasudevan, S.K., 2015.
• An in-depth analysis and study of load
balancing techniques in the cloud computing environment. Procedia
Comput. Sci. 50, 427–432. doi:10.1016/j.procs.2015.04.009.Big Data, Cloud
and Computing Challenges, [Online:] Available:
http://www.sciencedirect.com/science/
article/pii/S1877050915005104
• And …
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