Incentivising Resource Sharing in Social Clouds

Magdalena Punceva

Joint work with: Ivan Rodero (Rutgers University), Manish Parashar (Rutgers University), Omer Rana (Cardiff University) and Ioan Petri

(Cardiff University)

Social Clouds

• What is a social cloud? – Resource sharing system built on top of an existing social network.

• Purpose: sharing resources among participants in social networks – Resource: storage, computational power, applications

– Social networks: Facebook, LinkedIn, Twitter…

• Why social clouds? – Utilize trust relationships already existing between such participants

(in contrast to p2p sharing).

Social Clouds

1-hop sharing

2-hops sharing

3-hops sharing

Trust in social networks

• Trust – Inherent in social networks: friends trust each other

– People will be more willing to share resources with friends or socially close users then with total strangers.

– Trust levels are variable

– By trust we mean trusting that a friend will not misbehave (e.g. a friend will not interrupt a resource exchange or transaction).

Incentives and Trading

• Incentives – Although trust exists between friends, incentives are needed to

motivate the users to share their spare resources.

– Incentives remain an important hurdle to make effective use of social clouds environment.

The central problem in this work is defining the right incentives for sharing in social clouds.

• Trading as an incentive - Users will trade resources between each other and get payed for the resources they share.

Problem statement

• Key challenges: propose economic incentives for sharing resources that satisfy the following goals: 1. Node-providers who offer good quality services and resources

should get an advantage

2. Node-consumers should be able to report and share their experiences and the feedback should affect the payoff providers receive.

3. Distributed solution

• Existing related approaches – Barter: simple however limiting [BitTorrent}

– Credit-networks: p2p sharing [Z. Liu et al., P. Dandekar et al.]

– Global currency: complex rules [C. Aperjis et al.,V. Vishnamurthy et al. , B. Yang et al.]

Related approach: credit networks

u w v

c1 c2

• Node u trusts node v for up to c1 units of v’s

currency (v can use a service from u for up to c1

units of v’s currency)

• All nodes use the same currency

• Nodes participate in an underlying social network.

• Credit limits c1 and c2 reflect the level of trust

between u and v and v and w respectively.

Related approach: credit networks

u w v

p p

• Transaction goes through a chain of friends (1-hop

neighbors).

• Transaction: w purchases a product/service from u

worth p units.

Related approach: credit networks

u w v

c1-p c2-p

p p

In order for a transaction to be successful: c1>p and

c2>p

There must be at least p credits on every link

• After transaction: credit limits are being decreased

on each link p units.

Our approach: distributed currency

• Each node generates its own currency.

• Currency values may be different.

• Trading is done using such virtual currencies.

• When a node pays to another node, currency exchange rates must be known to both.

• Partially inspired by Silvio Gesell’s work: The Natural Economic Order, 1958.

Idea: The value of a node’s currency depends on the quality of the resources/services it offers.

How to define currency exchange rates? • Currency exchange rates should satisfy the following conditions:

1) Common knowledge: nodes should know the exchange rates

2) Conservation: currency exchange rates should be conserved along any cycle of payment.

B C

A

1/2

1/3

?

1 B-dollar=2 A-dollars 1 C-dollar=3 B-dollars Exchange rate between A and C must be 1/6 in order to conserve the currencies.

• The requirements (common knowledge and conservation) imply globally defined exchange rates. Is distributed model possible?

• Our solution: clusters of trusted (socially close) nodes.

• Currency exchange rates are defined within each cluster.

Clusters of trust

How to define the exchange rates within a cluster? • Value of a node’s currency depends on the quality of its

resources.

• Consumers give feedback as a score about the providers -> reputation model

• The reputation of a node is an average of all received scores .

Two types of payments

• Two types of payments: within a cluster (Transaction 1) and between clusters (Transaction 2).

Transaction 1

Transaction 2

Payments within a cluster (Transaction 1)

• Reputation lists are maintained within each cluster: e.g. a list (r1,r2,..,rn) corresponds to nodes 1…n that belong to cluster 1

• Reputation scores are given upon successful transaction.

• Reputation of a node is an average of all scores received.

• Currency conversion rates:

rv

u v

ru

1u’s dollar=(ru/rv) v’s dollars

Payments between clusters (Transaction 2) • As in credit networks: nodes exchange IOU (I owe you) credits.

• Such credits have limited use: if node u has p IOUs from node v, then can use them to purchase service/product only from v.

• Convertible currencies can be used for purchasing services/products from any node in the corresponding cluster.

• Simple to implement, supports asynchronous demands, simpler than price forming mechanisms.

Our solution: summary

Nodes who offer good services should get an advantage

Their currencies will have higher values since they depend on reputations

Consumers should be able to give feedback and share their experiences

Reputation model: aggregates feedback scores

Distributed solution

Clusters provide decentralized and self-

organized solution

Experimental setup

• Java based simulator

• Synthetic social graph based on measurements study about Microsoft IM communication graph – p(k)≈k-ae-bk

– av. clustering coefficient: 0.37

• Main metric: number of successful transactions, account statements

• Experiment: set of predefined transactions

• Transaction path: shortest path (Dijkstra algorithm).

Our simulations should answer these questions • How does the number and sizes of clusters affect the number

of transactions completed? How much do we gain in terms of completed transactions compared to pure credit networks?

• Is the approach scalable?

• How much does the non-uniform (power-law) distribution of reputations and social graph degrees affect the successfully completed transactions?

Our results: impact of cluster sizes

Success rate increases non-linearly with cluster sizes.

Our results: impact of reputation distribution

Equal and uniform reputation distributions lead to higher

success rate than the power-law distribution.

Our results: scalability and impact of the social graph

n 256 512 1024 2048 4096

success

rate(%) 76.00 72.86 81.70 78.70 70.65

Our results: impact of account limits

Number of successful transaction almost linearly

increases with credit limits.

Conclusions

• We extended the credit-network approach by enabling

within clusters currency conversions.

• By simulations we have shown how much it improved

long-term liquidity (achieved higher number of

completed transactions).

• Different currency values give advantage to high-

quality providers (incentive for improving the quality of

resources)

• Distributed reputation model

• Scalable with social graphs’ sizes and structures.

Research directions

• Integrating with CometCloud: parallelization and

application.

• Solutions for non-cooperative nodes: nodes may

downrate good providers, make coalitions to increase

reputation mutually.

• Free money property: money loses value over time.

• Exchange rate should include the impact of demand

and predefined quality of service.

• Network dynamics: nodes join/leave the network.

• Cluster dynamics: nodes join/leave a cluster.

Questions?