Payment channel networks (PCNs) have emerged as a viable solution to the scalability problem of blockchain systems. In PCNs, two peers can open a payment channel to transfer funds without publishing every transaction to a global blockchain. Payments can be routed between two unchanneled peers via a payment path, i.e., a sequence of adjacent payment channels. The routing protocol is the core of a PCN, since it regulates path discovery for transaction senders and receivers. Lots of routing algorithms have been proposed, most of which, however, lack either utility, i.e., a low payment success rate, or privacy, i.e., leaking channel balances. In this work, we first identify the utility-privacy tradeoff in existing routing algorithms. Aiming to trade privacy for utility, we propose a noised PCN by introducing a probabilistic model on the publicly-revealed channel balances. This enables us to express the reliability of a successful transaction for a given channel/path, Channel owners are allowed to select their own noise mechanisms, which directly link to routing fees they will charge. We further apply utility theory to design routing algorithms which achieve a balance between utility and privacy. Extensive simulations using a Lightning Network (LN) simulator called CLoTH have been conducted to validate the improvement in the payment success rate of the proposed architecture under different settings.

Payment Channel Networks (PCNs) have been introduced as a viable solution to the scalability problem of the popular blockchain. In PCNs, a payment channel allows its end nodes to pay each other without publishing every transaction to the blockchain. A transaction can be routed in the network if there is a path of channels with sufficient funds, and the intermediate routing nodes can ask the transaction sender for a compensatory fee. However, a channel may eventually become depleted and cannot support further payments in a certain direction, as transaction flows from that direction is heavier than flows from the other direction. In this paper, we discuss a PCN node’s possible roles and objectives, and analyze the strategies nodes should take under different roles by considering nodes’ benefits and the network’s performance. Then, we examine two basic network structures (ring and chord) and determine the constraints under which they constitute a Nash equilibrium. Based on the theoretical results, we propose a balance-aware fee-incentivized routing algorithm to guarantee cost-efficient routing, fair fee charging, and the network’s long lasting good performance in general PCNs. Testbed-based evaluation is conducted to validate our theoretical results and to show the feasibility of our proposed approach.

Bitcoin builds upon an unstructured peer-to-peer overlay network to disseminate transactions and blocks. Broadcast in such a network is slow and brings inconsistencies, i. e., peers have different views of the system state. Due to the delayed block propagation and the competition of mining, forking, i. e., the blockchain temporarily diverges into two or more branches, occurs, which wastes computation power and causes security issues. This paper proposes an autonomous and distributed topology optimization mechanism to reduce block propagation delay and hence reduce the occurrence of blockchain forks. In the proposed mechanism, a node can autonomously update his neighbor set using the information provided by his current neighbors, since each neighbor will recommend a peer from his own neighbor set, i. e., a neighbor’s neighbor, to this node. Each recommendation is based on a peer’s propagation ability, which is characterized as a criteria function obtained through a combination of empirical analysis and machine learning. We further propose some metrics to evaluate a Bitcoin network topology. Experiment results reflect the effectiveness of the proposed mechanism and indicate the correlation between block propagation time and fork rate. Thus, we analyze the relation between block propagation time and fork rate by applying an epidemic model to capture the block propagation process. We prove that a Bitcoin network topology with a relatively small network delay variance among all nodes produces a lower fork rate than another topology if its average block propagation time to 84% of the entire network is shorter.

The development of Blockchain-based mobile applications are impeded due to the resource limitations of mobile devices. Computation offloading can be a viable solution. In this paper, we consider a two-layer computation offloading paradigm including an edge computing service provider (ESP) and a cloud computing service provider (CSP). We formulate a multi-leader multi-follower Stackelberg game to address the computing resource management problem in such a network, by jointly maximizing the profits of each service provider (SP) and the payoffs of individual miners. We study two practical scenarios: a fixed-miner-number scenario for permissioned blockchains and a dynamic-miner-number scenario for permissionless blockchains. For the fixed-miner-number scenario, we discuss two different edge operation modes, i.e., the ESP isconnected(to the CSP) orstandalone, which form different miner subgames based on whether each miner's strategy set is mutually dependent. The existence and uniqueness of Stackelberg equilibrium (SE) in both modes are analyzed, according to which algorithms are proposed to achieve the corresponding SE(s). For the dynamic-miner-number scenario, we focus on the impact of population uncertainty and find that the uncertainty inflates the aggressiveness in the ESP resource purchasing. Numerical evaluations are presented to verify the proposed models.

Cloud computing provides a feasible solution to data outsourcing, and hence forming a cloud-based data market, where data users buy data from owners through querying cloud servers. However, it also incurs new privacy and security problems, as data is under a centralized third-party instead of the data owner’s direct control. Existing data markets are also questioned on their inflexible and opaque pricing, where the value of data ownership and the cost of query searches are mixed. In this paper, we consider blockchain-based storage as a better choice to ensure safe data outsourcing since data is spread out across many data points. We propose an Ethereum-based data market that provides distributed storage and correct remote data search. We design a new pricing model, where each query will be charged by two parties: owner (paid for providing his data) and miner (rewarded by performing query searches). We study a new cooperative search scheme through a proxy to reduce cost on the user side. Given that each user query is charged based on its number of keywords, then a cooperative search can reduce user-side cost by combining multiple queries into a group so that overlapped keywords will only be charged for one time. To ensure user QoE, a combined query should not be significantly larger than any of its original queries in terms of the number of keywords. The total price is based on the total number of keywords in all groups. Since it is a cooperative model with shared resources, we also study various incentive properties on the user side, yielding a cost sharing mechanism to split joint cost in a truth-revealing and fair manner. We further extend our market with a set of substitute data owners and propose a double auction mechanism to match users and owners based on their requirements. Experiments have been conducted on real query trace to demonstrate the effectiveness of our proposed scheme.

The emergence of federated learning and the increasingly powerful mobile devices lead to a mobile-crowd machine learning paradigm. In this paper, we consider a mobile-crowd federated learning system that includes a central server and a set of mobile devices. As the model requester, the server motivates all devices to train an accurate model by paying them based on their individual contributions. Each participating device needs to balance between the training rewards and costs for profit maximization. A Stackelberg game is proposed to model interactions between the server and devices. To match with reality, our model takes the training deadline and the device-side upload time into consideration. Based on different definitions of individual contribution, two reward policies, i.e., the size-based policy and accuracy-based policy, are compared. The existence and uniqueness of Stackelberg equilibrium (SE) under both definitions are analyzed, according to which algorithms are proposed to achieve the corresponding SE(s). We show that there is a lower bound of 0.5 on the price of anarchy in the proposed game. We extend our model by considering the uncertainty in the upload time, where each device’s upload time is subject to a normal distribution due to its unstable channel. Numerical evaluations are presented to verify the proposed models.

Summary Fair allocation has been studied intensively in both economics and computer science. Many existing mechanisms that consider fairness of resource allocation focus on a single resource. With the advance of cloud computing that centralizes multiple types of resources under one shared platform, multi‐resource allocation has come into the spotlight. In fact, fair/efficient multi‐resource allocation has become a fundamental problem in any shared computer system. The widely used solution is to partition resources into bundles that contain fixed amounts of different resources, so that multiple resources are abstracted as a single resource. However, this abstraction cannot satisfy different demands from heterogeneous users, especially on ensuring fairness among users competing for resources with different capacity limits. A promising approach to this problem is dominant resource fairness (DRF), which tries to equalize each user's dominant share (share of a user's most highly demanded resource, that is, the largest fraction of any resource that the user has required for a task), but this method may still suffer from significant loss of efficiency (i.e., some resources are underused). This article develops a new allocation mechanism based on DRF aiming to balance fairness and efficiency. We consider fairness not only in terms of a user's dominant resource, but also in another resource dimension which is secondarily desired by this user. We call this allocation mechanism 2‐dominant resource fairness (2‐DF). Then, we design a non‐trivial on‐line algorithm to find a 2‐DF allocation and extend this concept to k‐dominant resource fairness (k‐DF).

Besides the computation limitation, the requirement of storing the entire blockchain is another challenge for blockchain mining in mobile environments, and thus has hindered the development of blockchain-powered mobile applications. Storage outsourcing to a cloud service provider (CSP) is a viable solution. An individual miner can store his blockchain in the cloud and then validate transactions by querying the CSP. However, validation outsourcing to a remote CSP incurs delay and damages a miner's winning probability in the mining competitions. To shorten such an unwanted delay, miners can also cache the unspent transaction output (UTXO) set in a nearby edge service provider (ESP) for fast transaction validations, which definitely brings extra costs. In this paper, we consider a two-layer outsourcing paradigm to solve storage shortage for mobile miners. Due to the delay-cost tradeoff when selecting service providers, we can model interactions among miners as a non-cooperative game and formulate a Nash equilibrium problem to investigate the effects of outsourcing on miners' utilities. We also study the access probability of UTXOs with different generation times. This will guide miners on how to select unspent transaction outputs if they decide only to cache the partial UTXO set in the edge. We further extend our game by modeling multiple mining rounds as a one-shot game to see how the cache update frequency affects miners' strategies. Numerical evaluation is conducted to show the feasibility of storage outsourcing and to validate the proposed models and theoretical results.

Proof of Capacity (PoC) is an eco-friendly alternative to Proof of Work for consensus in blockchains since it determines mining rights based on miners' storage rather than computation. In PoC, for every block, a miner executes hashing on part of his dedicated storage. The miner that comes up with the smallest hash value among all miners will win the block. PoC has yet to be applied to mobile applications, due to the storage limitation of mobile devices. Storage offloading can be a viable solution that allows miners to offload mining all files to a cloud storage. In each mining round, a miner can decide whether to mine on his local device or by a cloud virtual machine (VM). Self-mining requires no extra cost but it incurs download delay, which will reduce the chance of winning. Cloud-mining experiences no delay but it brings cost on VMs. This delay-cost tradeoff challenges each miner to determine a ratio between self-mining and cloud-mining to maximize his utility. We model interactions among miners as a non-cooperative game and formulate a Nash equilibrium problem to investigate the effects of offloading on miners' utilities. We analyze the existence and uniqueness of equilibrium and propose a distributed algorithm to achieve the equilibrium in a uniform-delay setting. Further, we extend our results to non-uniform delays since miners may choose different network settings, e.g. 5G, 4G, or 3G. Both numerical evaluation and testbed experiments on Burstcoin are conducted to show the feasibility of storage offloading and to validate the proposed models and theoretical results.

Lots of privacy and security issues in the current cloud-based data markets will be eliminated by taking advantage of blockchain-based decentralized storage services, which can provide a new paradigm for safe data outsourcing and correct remote search. However, existing data markets are also questioned on their inflexible and opaque pricing, where the value of data ownership and the cost of query search are mixed. Thus, a better pricing model is necessarily needed in an emerging decentralized data market. In this paper, we envision an Ethereum-based data market, in which the pricing model for each query includes two parties: owner (paid for his data ownership) and miner (rewarded by query search). We study a new cooperative search scheme through a proxy to reduce cost on the client (user) side. Suppose each user query is charged based on the number of keywords in the query. The cost reduction is based on combining multiple queries into a group subject to the constraint that the resulting combined query is not significantly larger than any of its original query in terms of the number of keywords. The total price is based on total number of keywords in all groups. As the optimal grouping depends on the pricing of both owner and miner, we build a small testbed to analyze how price setting will affect grouping results. Since it is a cooperative model with shared resources, we also study various incentive properties on the client side, thereby yielding a cost sharing mechanism to split joint cost in a truth-revealing and fair manner.

Computation offloading has been considered as a viable solution to blockchain mining in mobile environments. In this paper, we present a two-layer computation offloading paradigm that includes an edge computing service provider (ESP) and a cloud computing service provider (CSP). We formulate a multi-leader multi-follower Stackelberg game to address the computing resource management problem in such a network, by jointly maximizing the profits of each service provider (SP) and the payoffs of individual miners. Two practical scenarios are investigated: a fixed-miner-number scenario for permissioned blockchains and a dynamic-miner-number scenario for permissionless blockchains. For the fixed-miner-number scenario, we discuss two different edge operation modes, i.e., the ESP is connected (to the CSP) or standalone, which form different miner subgames based on whether each miner's strategy set is mutually dependent. The existence and uniqueness of Stackelberg equilibrium (SE) in both modes are analyzed, according to which algorithms are proposed to achieve the corresponding SE(s). For the dynamic-miner-number scenario, we focus on the impact of population uncertainty and find that the uncertainty inflates the aggressiveness in the ESP resource purchasing. Numerical evaluations are presented to verify the proposed models.

Fair allocation has been studied intensively in both economics and computer science. Many existing mechanisms that consider fairness of resource allocation focus on a single resource. With the advance of cloud computing that centralizes multiple types of resources under one shared platform, multi-resource allocation has come into the spotlight. In fact, fair/efficient multi-resource allocation has become a fundamental problem in any shared computer system. The widely-used solution is to partition resources into bundles that contain fixed amounts of different resources, so that multiple resources are abstracted as a single resource. However, this abstraction cannot satisfy different demands from heterogeneous users, especially on ensuring fairness among users competing for resources with different capacity limits. A promising approach to this problem is dominant resource fairness (DRF), which tries to equalize each user's dominant share (share of a user's most highly demanded resource, i.e., the largest fraction of any resource that the user has required for a task), but this method may still suffer from significant loss of efficiency (i.e., some resources are underused). This paper develops a new allocation mechanism based on DRF aiming to balance fairness and efficiency. We consider fairness not only in terms of a user's dominant resource, but also in another resource dimension which is secondarily desired by this user. We call this allocation mechanism 2-dominant resource fairness (2-DF). Then, we design a non-trivial on-line algorithm to find a 2-DF allocation and extend this concept to k-dominant resource fairness ( k-DF).