1. Authors Alessandro Duminuco, Ernst Biersack Taoufik and En-Najjary
Proactive Replication in Distributed Storage Systems Using Machine Availability Estimation
Authors
Alessandro Duminuco, Ernst Biersack Taoufik and En-Najjary
Presented by Xiaoyu Sun
9/21/2018

2. outline Motivation Goal of this paper An adaptive control problem
Impact of estimation time
A hybrid scheme for availability
Validation
Experiment
Conclusion
What system we are talking about? Why the problem which this paper try to handle is important?
What approaches people already used? The disadvantage and advantage of these approaches
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3. Motivation
Peer-to-Peer based distributed storage system Service guarantees
What is p2p? What implementation p2p can do? content delivery, networking, search (Yacy a free distributed search engine )
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4. Motivation Durability Availability
once stored, data are never lost, although
the data may not be available all the time
Availability
assures that data can be retrieved in any moment
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5. Motivation Methods used for Redundancy in Storage System
Replication of the original data
parity encoding of the original data
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6. Motivation Types of failure behavior Permanent failure behavior;
To copy with permanent failures and to assure durability;
Transient failure behavior;
Reintegrated in the system;
Hints: from the traces of peer availability, we know that temporary disconnections are much more frequent than permanent ones.
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7. Motivation Advantage of Reactive approach
Adaptiveness
availability
Disadvantage of Reactive approach
Waste of resources
Bursty use of resources
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8. Motivation Advantage of Proactive approach
A fixed repair rate
Smooth the resource usage
Disadvantage of Proactive approach
Fail to handle the changing failure behaviors
Durability compromised
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9. Goal of this paper Durability Adaptiveness
A limited network bandwidth
Durability
Adaptiveness
Maximize the smoothness of the repair rate
Maximize the smoothness of the bandwidth needed for the repairs
Degraded performance of other activities
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10. An adaptive control problem
Periodically infer the failure behavior of the peers
The number of available peers at time t
Repair rate is a time dependent signal
∆T observation period used by estimator and the interval between two updates of R(t)
Signal the occurrence a repair or a reconnection
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11. The system model Connected state Temporarily disconnected state
Abandon state
µ: Single peer disconnection rate.
A session time
: Single peer reconnection rate.
Repair rate is a time dependent signal
A disconnection time
P: abandon probability
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12. The system model Q1 represents the peers in the connected state
G/G/1 first G stands for probability distribution of the inter arrival times
Second G stands for the probability distribution of the service times
1 stands for the number of servers
M stands for Exponential probability density
G any arbitrary probability distribution
D all customers have the same value
Q2 represents the peers in the disconnected state
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13. The system model
L = λW
The long-term average number of customers in a stable system L is equal to the long-term average arrival rate, λ, multiplied by the long-term average time a customer spends in the system, W; or expressed algebraically: L = λW
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14. The estimator The estimator is to estimate two parameters μ and P
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15. The estimator
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16. The controller
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17. Impact of estimation time
The estimation time ∆T is the most crucial parameters of this model.
Impact on bandwidth usage ∆T
∆T=0
Robustness of the Estimation
One tries to push system reactivity too much
The time needed to estimate the parameters is dynamic
Any different choice would make the controller follow short term fluctuation
Cause: uneven use the bandwidth resources
correlated failures of many nodes where most of
available fragments will suddenly disappear
to choose the maximum ¢T that divides the time in
segments in which the system can be approximated as
being statistically stable
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18. Impact of estimation time
The implementation of this paper does not fix ∆T
D means the average number of disconnections observed during an estimation period
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19. A hybrid scheme for availability
The objective of the controller is to make the repair rate equal to the rate of permanent failures.
Define a threshold here THpro
If the number of available fragments hits a lower THpro, the system switches to a purely reactive scheme.
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20. Validation
System Model Validation
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21. Validation
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22. Validation Estimator Validation
The convergence time depends on μ. This leads us
to say that in a changing environment we cannot
use a constant estimation period, but instead ¢TD
should be adapted to the order of magnitude of the
parameter μ as we did in eq. (8).
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23. Validation
Controller Validation
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24. Validation
Upper left lower right
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25. Experiments Goal of experiments The capacity to assure durability ;
The smoothness of the repair rate;
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26. Experiments the cost of the reactive scheme is a bursty repair
activity
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27. Experiments D is too big with respect to the
parameter dynamics and the estimator is not able to
cope with their changes
too small values of D the estimation is not reliable and for too
big values the distribution of the number of available
fragments degrades too much
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28. Experiments
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29. Experiments
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30. Experiments
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31. Conclusion
This system combines the resilience of reactive schemes with the smoothness of proactive schemes.
Validated the proposed scheme and demonstrated its effectiveness using synthetic data
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