introduction to blockchain and applications of game theory
TRANSCRIPT
Dusit (Tao) Niyato
School of Computer Science and Engineering (SCSE),Nanyang Technological University, Singapore
Introduction to Blockchain and Applications of Game Theory
1
• Introduction to Blockchain
• Evolutionary Game for Mining Pool Selection in Blockchain
Networks
• Cloud/Fog Computing Resource Management and Pricing for
Blockchain Networks
– Stackelberg Game
– Auction
– (Auction with) Deep Learning
• Testbed and Experiment
• Summary and Future Directions
Outline
2
Introduction• Blockchain represents novel approach to the landscape of information
collection, distribution, and governance
– Add new and undeletable transactions and organize them into
blocks.
– Cryptographically verify each transaction in the block.
– Append the new block to the end of the existing immutable
blockchain.
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Blockchain: A Practical Guide to Developing Business, Law, and Technology Solutions
Introduction
4
Blockchain: A Practical Guide to Developing Business, Law, and Technology Solutions
A ($10)B ($20) C ($15)
A ($10)
B ($20)
C ($15)
A ($10)
B ($20)
C ($15)
A ($10)
B ($20)
C ($15)
A ($30)
Introduction to Blockchain
Public Ledger
• Every viable transaction is stored in a public ledger
• Transactions are placed in blocks, which are linked by SHA256 hashes.
• https://blockchain.info
• Blockchain can be abstracted as an infinitely-growing, append-only string
that is canonically agreed upon by the nodes in the blockchain network
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Hash of k-1
Block k
Transaction1
Transaction2
Hash of k
Block k+1
Transaction3
Transaction4
Hash of k+1
Block k+2
Transaction5
Transaction6
Introduction
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Blockchain: A Practical Guide to Developing Business, Law, and Technology Solutions
A
B
C
A has $10
B has $20
C has $15
A has $5
B has $25
C has $15
A has $5
B has $35
C has $5
A has $15
B has $25
C has $5
A→B $5
Hash
AS C→B $10
Hash
CS B→A $10
Hash
BS
C→A $10
Hash
CS
Irreversible cryptographic hash function
The only way to solve such mathematical
problem is to guess random numbers that
combined with the previous block content
generate a defined result (usually a number
below a certain value).
Ledger
AS is digital signature signed by
A using A’s private key
Mining
Introduction
Proof of Work
• Byzantine failure is any fault presenting different symptoms
to different observers (loss of a system service due to a
Byzantine fault in systems that require consensus)
• Consensus is a mechanism to ensure trust in the network,
which means that users in the network commonly reach an
agreement of a block added to the existing blockchain
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Introduction
Proof of Work
• Sybil attack, an attacker creates a large population of
pseudonymous or fake users in the network
– These fake users can vote and reach consensus to accept false transactions generated by the
attacker
• Solution to such an attack is to raise the complexity of
mining so that attackers may not have enough computing
power to support enough fake users in the network, and
thus rendering a Sybil attack economically infeasible
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A
B
C
F
D
E
Introduction to Blockchain
51% Attack and Double-Spending
• What if two blocks are created at the same time?
• (Different) miners broadcast both blocks
• The other miners build blocks on the longest chain
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A has $10
B has $20
C has $15
A→B $5
Hash
AS
C→B $10
Hash
CS B→A $10
Hash
BS
C→A $10
Hash
CS
Ledger
A→C $4
Hash
AS
Introduction to Blockchain
51% Attack and Double-Spending
• What if two blocks are created at the same time?
• (Different) miners broadcast both blocks
• The other miners build blocks on the longest chain
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A has $10
B has $20
C has $15
A→B $5
Hash
AS
C→B $10
Hash
CS C→D $1
Hash
CS
C→A $10
Hash
CS
Ledger
C→B $1
Hash
CS
A ships item to C
Mined by C
Introduction to Blockchain
Proof of Work
• Since these miners spend computing time and energy for recording
blockchain users’ performed transactions, the mining reward is needed so
as to guarantee the incentive
• In practical blockchain systems, e.g., Bitcoin, a miner which successfully
mines a block receives the mining reward when the mined block is
successfully added to the blockchain
A
B
C
Mining, proof of work, and
incentive introduce
resource allocation issues
and cause competitive
environment in blockchain
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Introduction to Blockchain
Types of Blockchain
• Permissionless/public blockchain networks: – Anyone can join the network to read the blockchian data, issue transactions and participate
in the consensus process.
• Private blockchain networks: – The consensus is maintained by a centralized server
• (Hybrid) Permissioned/consortium blockchain networks:
– Only the authorized nodes are allowed to participate in the consensus process.
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Proof of authority
Evolutionary Game for Mining Pool
Selection in Blockchain Networks
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Mining Pool Selection
Motivations and Objectives
• In blockchain networks, the consensus protocol based on proof-of-work
uses monetary incentive to encourage the nodes in the network to
participate in the blockchain maintenance process
• Due to the exponential increase of the difficulty of the cryptographic
puzzle, an individual block miners tends to join a mining pool and
collaborate with other miners in order to reduce the income variance and
earn stable profit.
https://www.blockchain.com 14
Mining Pool Selection
Motivations and Objectives
• The chance for individual (solo) miners to win the blockchain mining race
is negligible and the real-world blockchain networks are dominated by the
nodes that represent mining farms or mining pools
• mining pool works as a task scheduler for large number of solo miners
• It divides the computation task for proof of work puzzle solving into sub-
problems and assigns them to the registered solo miners according to
their devoted mining power
Mining pool A Mining pool B
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Mining Pool Selection
Problem Formulation
• A large population of N individual miners
• According to the consensus protocol, the miner of each confirmed block receives a fixed amount of reward from the new block and a flexible amount of transaction fees for maintaining the blockchain’s consensus and approving the transactions
• Individual miners organize themselves into a set of M mining pools
• Probability for pool i to mine a block can be expressed as
individual mining power fraction of miners
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Mining Pool Selection
Problem Formulation
• if pool i mines a new block of length si, the total propagation time of that block is
• Probability of winning the mining
• Expected reward of a pool
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block verification timepropagation time
Mining Pool Selection
Problem Formulation
• Players: Miners
• Strategy: Choosing mining pool
• Payoff:
• Replicator dynamics:
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cost (energy)
reward
vector of population
average payoff
Mining Pool Selection
Some Results
• Two pools
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Evolutionary Stable Strategy
The proof is based on Jacobian matrix
Mining Pool Selection
Some Results
• Convergence
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Mining Pool Selection
Some Results
• Basin of Attraction
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Cloud/Fog Computing Resource
Management and Pricing for
Blockchain Networks
Stackelberg Game
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Cloud/Fog Computing Resource Management
Stackelberg GameMotivations and Objectives
• Due to proof of work, computationally lightweight nodes such as the Internet of Things (IoT) devices may be prevented from directly participating in the consensus process
• “Cloud mining” becomes a viable option where the mobile devices offload their storage load and/or computation tasks in proof of workto the Cloud/Fog Providers
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Cloud/Fog Computing Resource Management
Stackelberg GameMotivations and Objectives
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Cloud/Fog Computing Resource Management
Stackelberg GameMotivations and Objectives
• We model the interactions between the rational blockchain miners and the cloud/fog provider as a two-stage Stackelberg game
• We study both the uniform pricing scheme and the discriminatory
pricing scheme for the cloud/fog provider
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Cloud/Fog Computing Resource Management
Stackelberg GameGame Formulation
• Players: Cloud/fog provider (leader), N miners (followers)
• Strategy: Pricing (leader), service demand (followers)
• Solution: Stackelberg equilibrium
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Cloud/Fog Computing Resource Management
Stackelberg GameGame Formulation
• Miner subgame (Lower Stage II)
27resource demand
Cloud/Fog Computing Resource Management
Stackelberg GameGame Formulation
• Miner subgame (Lower Stage II)
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Cloud/Fog Computing Resource Management
Stackelberg GameGame Formulation
• Cloud/fog provider subgame (Upper Stage I)
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total demand
resource cost
Cloud/Fog Computing Resource Management
Stackelberg GameGame Formulation
• Cloud/fog provider subgame (Upper Stage I)
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Stackelberg Equilibrium
Cloud/Fog Computing Resource Management
Stackelberg GameResults
• Experiment
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Cloud/Fog Computing Resource Management
Stackelberg GameResults
• Simulation
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Cloud/Fog Computing Resource
Management and Pricing for
Blockchain Networks
Auction
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Cloud/Fog Computing Resource Management
AuctionMotivations and Objectives
• Shortcomings of Stackelberg game– Demand is continuous
– Cannot guarantee truthfulness
– Competing in obtaining computing resources
– Social welfare may not be maximized
– Lack of network effects factor
• Alternative: Auction
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Cloud/Fog Computing Resource Management
AuctionMotivations and Objectives
• We formulate social welfare maximization problems for two bidding schemes: constant-demand scheme and multi-demand scheme
• We construct an optimal algorithm that achieves optimal social welfare
• Algorithm is designed to be truthful, individually rational and
computationally efficient
• We characterize network effects function to represent the relationship
between the security of the blockchain network and the total
computing resources invested into the network
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Cloud/Fog Computing Resource Management
AuctionSystem Model
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Cloud/Fog Computing Resource Management
AuctionProblem Formulation
• Social welfare maximization
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demand/allocation
network effect
reward
cost
Cloud/Fog Computing Resource Management
AuctionResults
• Experiment
– We vary the CPU resources of the other miners, i.e., the sum of existing honest
miners’ computing resources, to measure the probability of successful attacks
– We then count the number of fake blocks which successfully join the chain every
10000 blocks generated
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Cloud/Fog Computing Resource Management
AuctionResults
• Simulation
– Social welfare
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Cloud/Fog Computing Resource
Management and Pricing for
Blockchain Networks
(Auction with) Deep Learning
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Cloud/Fog Computing Resource Management
(Auction with) Deep LearningMotivations and Objectives
• Shortcomings of traditional auction– Need to solve complex math
– Social welfare maximization vs profit maximization
– Rely on well quantified valuation
– Static
• Alternative: Deep learning
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Auction
market
Seller
Buyers
Product/service
Bid
Internal
DatasetExternal
dataset
Market
strategy
Machine learning
Cloud/Fog Computing Resource Management
(Auction with) Deep LearningSystem Model
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Cloud/Fog Computing Resource Management
(Auction with) Deep LearningPerformance Metrics
• Expected revenue/profit: The expected revenue R is the total price that
the provider receives from the miners, i.e., bidders
• Individual Rationality (IR) violation: IR violation happens if the auction
results in negative utility for any miner (bidder)
• Incentive Compatibility (IC) violation: IC of the auction guarantees that
every bidder achieves the highest utility just by submitting its truthful
bid (truthfulness)
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Cloud/Fog Computing Resource Management
(Auction with) Deep LearningDesign
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Cloud/Fog Computing Resource Management
(Auction with) Deep LearningResults
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• 51% Attack
• Cyber insurance
• Proof of Stake
Future Directions
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• Blockchain-based data management framework for energy market
Testbed and Experiment
PHEV chargers
Building with storage
Generators
Energy market
and
incentive mechanism
Smart grid
Building with
solar panel Building with
storage and
solar panel
Network and computing systems
Smart meter
Beaglebone
Embedded
system
Blockchain
agent
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Demo Video
• Z. Xiong, S. Feng, D. Niyato, P. Wang, and Z. Han, "Optimal pricing-based edge
computing resource management in mobile blockchain," to be presented in IEEE
ICC, Kansas City, MO, USA, 20-24 May 2018.
• W. Wang, D. Niyato, P. Wang, and A. Leshem, "Decentralized caching for content
delivery based on blockchain: A game theoretic perspective," to be presented in
IEEE ICC, Kansas City, MO, USA, 20-24 May 2018.
• Y. Jiao, P. Wang, D. Niyato, and Z. Xiong, "Social welfare maximization auction in
edge computing resource allocation for mobile blockchain," to be presented in
IEEE ICC, Kansas City, MO, USA, 20-24 May 2018.
• N. C. Luong, Z. Xiong, P. Wang, and D. Niyato, "Optimal auction for edge
computing resource management in mobile blockchain networks: A deep learning
approach," to be presented in IEEE ICC, Kansas City, MO, USA, 20-24 May 2018.
• K. Suankaewmanee, D. T. Hoang, D. Niyato, S. Sawadsitang, P. Wang, and Z.
Han, "Performance analysis and application of mobile blockchain," to be presented
in International Conference on Computing, Networking and Communications
(ICNC), Maui, Hawaii, USA, March 5-8, 2018.
Further Materials
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