Presentation of paper published in DisCoTec 2015.

1. Incentivising Resource Sharing
in Federated Clouds
Eduardo Falcão
eduardolfalcao@lsd.ufcg.edu.br
Francisco Brasileiro
Andrey Brito
José Luis Vivas

2. Challenge
●Promotion of cooperation among selfish
individuals in a decentralized context;
●Incentive mechanisms to prevent
collaborative peers from defecting from the
federation;
●Peers need to take efficient decisions
●“To whom should I donate?”
●“How much should I donate?”
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3. Baseline – Network of Favors
●Network of Favors (NoF)
●each peer keeps a local record of the past
interactions with other peers.
Peer Provided
Favors
Consumed
Favors
Credit
D 300 150 150
B 100 55 45
E 30 5 25
C 15 30 0
F 0 200 0
A
{ 3

4. Problem Statement
●Whenever there is contention of resources,
the collaborative actors are prioritized;
●NoF works fine in these scenarios.
Peer Provided
Favors
Consumed
Favors
Credit
D 300 150 150
B 100 55 45
E 30 5 25
C 15 30 0
F 0 200 0
A
{
requests
+10
+10
+10
+10
+10
free: +20
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5. Problem Statement
●When there isn’t contention of resources, some
free riders are able to consume the exceeding
resources.
●These resources won’t be reciprocated in the future.
Peer Provided
Favors
Consumed
Favors
Credit
D 300 150 150
B 100 55 45
E 30 5 25
C 15 30 0
F 0 200 0
A
{
requests
0
+10
free: +20
0
0
0
5

6. Solution
𝑠𝑎𝑡𝑖𝑠𝑓𝑎𝑐𝑡𝑖𝑜𝑛 =
𝑐𝑜𝑛𝑠𝑢𝑚𝑒𝑑
𝑟𝑒𝑞𝑢𝑒𝑠𝑡𝑒𝑑
𝑓𝑎𝑖𝑟𝑛𝑒𝑠𝑠 =
𝑐𝑜𝑛𝑠𝑢𝑚𝑒𝑑
𝑝𝑟𝑜𝑣𝑖𝑑𝑒𝑑
• Satisfaction-Driven NoF (SD-NoF)
– Context: P2P opportunistic desktop
grids;
– Low cost for maintenance and
hosting;
– Provides all exceeding
resources to maximize the
satisfaction of the peers.
• Fairness-Driven NoF (FD-NoF)
– Context: P2P federation of
private cloud providers;
– High cost due to purchase,
maintenance, energy, hosting,
etc;
– Regulates the amount of
provided resources in order to
guarantee good levels of
fairness
probability of being answered level of reciprocity
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7. Simulation Model
●𝒏: number of peers;
●𝒇: % free riders;
●The simulation poceeds in steps;
●Collaborators and free riders have the same total
resource capacity (𝑪), and the same amount of
demand (𝑫 ∙ 𝑪);
●SD-NoF  provides 𝐶;
●FD-NoF  provides 0, 𝐶 ;
●𝝅: probability of consuming;
●Colaborators  [consuming state, provider state];
●Free riders  [consuming state], critical case, 𝜋 = 1.
factors
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8. Scenarios
●Designed to assess the different scenarios of
contention of resources between
collaborators (𝜅):
𝜅 = 0.5  low resource contention
𝜅 = 1  moderate resource contention
𝜅𝜖 {2, 4}  high resource contention
requested
provided
𝜅 =
10
20
𝜅 =
10
10
 𝜅 =
10
5
factors
Constant  n=100; f=75%; steps=10000; C=1
Variable  𝑫 and 𝝅
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9. SD-NoF
Proposal: FD-NoF
collaborators should be
equipped with a control
mechanism that enables them
to regulate the amount of
resources donated
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10. Peer Provided
Favors
Consumed
Favors
Credit
D 300 150 150
B 100 55 45
E 30 5 25
C 15 30 0
F 0 200 0
Peer Provided
Favors
Consumed
Favors
Credit Current
Fairness
Previous
Fairness
D 300 150 150 2 ...
B 100 55 45 1.81 ...
E 30 5 25 6 ...
C 15 30 0 0.5 ...
F 0 200 0 0 ...
FD-NoF
●NoF + Feedback Control Loop  FD-NoF
A
{
10

11. FD-NoF
∆= 𝟓% ∙ 𝑪
A
E
D
C
B
Max(C)
80%
100%
65%
50%
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12. FD-NoF: satisfaction
Resource contention=0.5
Average deterioration: 4%
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13. FD-NoF: fairness
Resource contention=0.5
Average improvement: 44%
Is it worth sacrificing 4% of
satisfaction to increase 44% of
fairness?
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14. Concluding Remarks
●FD-NoF shows good evidences that achieves
its goal:
●Low resource contention
● Satisfaction  -4%
●Fairness  +44%
●Moderate and high resource contention: similar
results to SD-NoF.
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15. Future Work
●Improve the Feedback Control Loop;
●Validation of the results in a realistic scenario;
●Fogbow middleware
●http://www.fogbowcloud.org/
●EUBrazilCC project: federation of private cloud
providers
●http://eubrazilcloudconnect.eu/
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16. Thank you!
Questions?
eduardolfalcao@lsd.ufcg.edu.br
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