Incentivising Resource Sharing in Social Clouds


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Social Clouds provide the capability to share resources among participants within a social network - leveraging on the trust relationships already existing between such participants. In such a system, users are able to trade resources between each other, rather than make use of capability offered at a (centralized) data centre. Incentives for sharing remain an important hurdle to make more effective use of such an environment, which has a significant potential for improving resource utilization and making available additional capacity that remains dormant. We utilize the socio-economic model proposed by Silvio Gesell to demonstrate how a "virtual currency" could be used to incentivise sharing of resources within a "community". We subsequently demonstrate the benefit provided to participants within such a community using a variety of economic (such as overall credits gained) and technical (number of successfully completed transactions) metrics, through simulation.

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Incentivising Resource Sharing in Social Clouds

  1. 1. 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)
  2. 2. 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).
  3. 3. Social Clouds 3-hops sharing 2-hops sharing 1-hopsharing
  4. 4. 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).
  5. 5. 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.
  6. 6. 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.]
  7. 7. Related approach: credit networks u v w 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.
  8. 8. Related approach: credit networks• Transaction: w purchases a product/service from u worth p units. p p u v w• Transaction goes through a chain of friends (1-hop neighbors).
  9. 9. Related approach: credit networks• After transaction: credit limits are being decreased on each link p units. p p u v w c1-p c2-pIn order for a transaction to be successful: c1>p andc2>pThere must be at least p credits on every link
  10. 10. 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.
  11. 11. How to define currency exchangerates?• 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. A 1 B-dollar=2 A-dollars 1 C-dollar=3 B-dollars 1/2 ? Exchange rate between A and C must be 1/6 in order to conserve the currencies. B C 1/3
  12. 12. Clusters of trust• 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.
  13. 13. How to define the exchange rateswithin 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 .
  14. 14. Two types of payments Transaction 2Transaction 1 • Two types of payments: within a cluster (Transaction 1) and between clusters (Transaction 2).
  15. 15. 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: 1u’s dollar=(ru/rv) v’s dollars u v ru rv
  16. 16. 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.
  17. 17. Our solution: summaryNodes who offer good Their currencies will haveservices should get an higher values since theyadvantage depend on reputationsConsumers should be able Reputation model:to give feedback and share aggregates feedbacktheir experiences scores Clusters provideDistributed solution decentralized and self- organized solution
  18. 18. 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).
  19. 19. Our simulations should answerthese 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?
  20. 20. Our results: impact of cluster sizes Success rate increases non-linearly with cluster sizes.
  21. 21. Our results: impact of reputationdistributionEqual and uniform reputation distributions lead to highersuccess rate than the power-law distribution.
  22. 22. Our results: scalability and impact ofthe social graph n 256 512 1024 2048 4096 success 76.00 72.86 81.70 78.70 70.65 rate(%)
  23. 23. Our results: impact of account limits Number of successful transaction almost linearly increases with credit limits.
  24. 24. Conclusions• We extended the credit-network approach by enablingwithin clusters currency conversions.• By simulations we have shown how much it improvedlong-term liquidity (achieved higher number ofcompleted transactions).• Different currency values give advantage to high-quality providers (incentive for improving the quality ofresources)• Distributed reputation model• Scalable with social graphs’ sizes and structures.
  25. 25. Research directions• Integrating with CometCloud: parallelization andapplication.• Solutions for non-cooperative nodes: nodes maydownrate good providers, make coalitions to increasereputation mutually.• Free money property: money loses value over time.• Exchange rate should include the impact of demandand predefined quality of service.• Network dynamics: nodes join/leave the network.• Cluster dynamics: nodes join/leave a cluster.
  26. 26. Questions?