Multimodal learning enables models to process and reason over diverse information sources, unlocking human-like perceptual and cognitive capabilities. As such models gain adoption, efficiently serving them on GPUs has become increasingly important. However, the modular architecture of multimodal models poses significant challenges to existing unimodal serving systems, which treat models as monolithic and overlook inter-module heterogeneity. This results in severe GPU underutilization. To address this, we propose Eevee, a multimodal serving system based on a new scheduling paradigm we call module multiplexing. Unlike prior approaches that execute all modules sequentially with uniform batch sizes, Eevee schedules modality-specific modules concurrently on the same GPU with independently tuned batching and resource allocation. This design enables fine-grained GPU sharing, boosting intra-GPU parallelism and improving request-level throughput. We implement a prototype of Eevee and evaluate it on several representative multimodal models (e.g., CLIP, BLIP, LLaVA, InternVL). Our results show that Eevee significantly outperforms state-of-the-art serving systems in both throughput and GPU utilization.

Large language models (LLMs) have driven significant progress in natural language processing, yet their training and fine-tuning remain limited by memory constraints, particularly the substantial memory footprints of optimizer states. Existing solutions address this challenge by offloading optimizer states and update tasks to the CPU, but this often leads to increased GPU idleness due to the CPU's limited computational capabilities, especially in pipeline parallelism.

With the rapid growth and rising complexity of web content, edge-deployed LLMs have become essential for enhancing users' online experiences. However, sparsely-activated Mixture-of-Experts (MoE) models, which are well-suited for edge scenarios, face significant memory bottleneck challenges. Offload-based methods have been proposed to mitigate the problem, but they face difficulties with expert prediction. To promote the application of MoE models in edge scenarios, we propose Fate, an offloading system designed for MoE models to enable efficient inference in resource-constrained environments. The key insight behind Fate is that gate inputs from adjacent layers can be effectively used for expert prefetching, achieving high prediction accuracy. Furthermore, Fate employs a shallow-favoring expert caching strategy that increases the expert hit rate to 99%. Additionally, Fate integrates tailored quantization strategies for cache optimization and I/O efficiency. Experimental results show that, compared to baselines, Fate achieves up to 1.34×-5.07× prefill speedup and 1.26×-4.41× decoding speedup, while maintaining inference quality.

The Execute-Order-Validate (EOV) framework has been used to improve the scalability of blockchains by concurrently executing transactions. However, the EOV framework also poses a critical performance issue. Specifically, when multiple transactions access the same data, only one of them can be committed eventually while the others are aborted due to the strong concurrency control restriction. This inefficiency makes the EOV framework far from practicality since there always exist hotspot variables that can be frequently accessed in real-world scenarios, such as the Fungible Token (FT) and Non-Fungible Token (NFT) online marketplace. In this paper, we propose RapidSnail, a novel EOV framework that enables transactions to execute based on the uncommitted data to reduce the transaction abort rate in such scenarios with hotspot variables. We first propose a new read-write set representation and a concurrency execution schedule algorithm in the execution phase to maintain the concurrent efficiency. Then we propose an effect-based conflict graph construction algorithm in the order phase to handle the conflict transactions based on the new read-write set. Finally, we propose a concurrent commitment schedule algorithm to adopt the new read-write set to validate the transactions concurrently in the validation phase. Our experiment results show that RapidSnail increases the throughput by at least 4× compared to the state-of-the-art EOV framework under high contention workload. More specifically, RapidSnail reduces the abort rate by 50%, and achieves at least 4× speedup in the order phase and 2.94× speedup in the validation phase over the existing EOV frameworks.

Multi-turn conversational systems based on large language models are increasingly being integrated into web platforms and applied across a wide range of domains. However, these systems typically combine the userߣs current query with contextual information from previous interactions, resulting in continuously expanding input prompts. This leads to a significant increase in time-to-first-token (TTFT), causing intolerable delays in web response times. To address this issue, we introduce TransformKV, which maximizes the reuse of the KV cache from previous conversations rather than recomputing, thereby reducing TTFT latency. TransformKV first identifies the specific locations that require transformation to maximize the reuse of the KV cache with minimal operations. It then efficiently transforms the KV cache for a subset of tokens by recomputing only the KV cache that impact semantics. Additionally, TransformKV further reduces TTFT latency by performing only QKV computations in certain layers while skipping other computations that contribute less to overall performance. Experimental results demonstrate that in multi-turn conversation tasks, TransformKV can reduce inference latency by up to 30%, achieving up to a 1.8× improvement in performance compared to similar approaches. Notably, as the context window size increases, the performance gains become even more pronounced.

Sharding is considered a promising solution for scaling blockchain systems. However, most existing sharding systems have not considered the dynamics of the environment when making a sharding strategy, including the change of pending transactions, the leaving and joining of participants, and malicious attacks, which could cause performance instability and security issues. To address it, in this paper, we propose an intelligent and efficient dynamic sharding technology to advance the blockchain system performance and security. We first propose a formal and general evaluation framework for blockchain sharding in a dynamic environment, and conclude an optimization target for the system performance and security. To achieve a long-term benefit for the optimization target, a deep reinforcement learning (DRL)-based sharding approach has been proposed to intelligently make optimal sharding strategies. Next, we propose an adaptive resharding protocol to efficiently reduce the overhead introduced by dynamic sharding. Our experimental results illustrate that our proposed dynamic sharding in a simulation testbed can achieve 2.8 times transactions per second compared to traditional static sharding systems, and guarantee high security in a dynamic environment.

Mixture of Experts (MoE), with its distinctive sparse structure, enables the scaling of language models up to trillions of parameters without significantly increasing computational costs. However, the substantial parameter size presents a challenge for inference, as the expansion in GPU memory cannot keep pace with the growth in parameters. Although offloading techniques utilise memory from the CPU and disk and parallelise the I/O and computation for efficiency, the computation for each expert in MoE models is often less than the I/O, resulting in numerous bubbles in the pipeline.

Sharding is a promising solution to scale blockchain by separating the system into multiple shards to process transactions in parallel. However, due to state separation and shard isolation, it is still challenging to efficiently support smart contracts on a blockchain sharding system where smart contracts can interact with each other, involving states maintained by multiple shards. Specifically, existing sharding systems adopt a costly multi-step collaboration mechanism to execute smart contracts, resulting in long latency and low throughput. This article proposesSparrow, a blockchain sharding protocol achieving one-step execution for smart contracts. To break shard isolation, inspired by non-local hotspot data caching in traditional databases, we propose a new idea ofinter-shard caching, allowing a shard to prefetch and cache frequently accessed contract states of other shards. The miner can thus use the inter-shard cache to pre-execute a pending transaction, retrieve all its contract invocations, and commit it to multiple shards in one step. Particularly, we first propose a speculative dispersal cache synchronisation mechanism for efficient and secure cache synchronization across shards in Byzantine environments. Then, we propose a multi-branch exploration mechanism to solve the rollback problem during the optimistic one-step execution of contract invocations with dependencies. We also present a series of conflict resolution mechanisms to decrease the rollback caused by inherent transaction conflicts. We implement prototypes forSparrowand existing sharding systems, and the evaluation shows thatSparrowimproves the throughput by$2.44\times$and reduces the transaction latency by 30% compared with the existing sharding systems.

Serverless ML inference is an emerging cloud computing paradigm for low-cost, easy-to-manage inference services. In serverless ML inference, each call is executed in a container; however, the cold start of containers results in long inference delays. Unfortunately, most existing works do not work well because they still need to load models into containers from scratch, which is the bottleneck based on our observations. Therefore, this paper proposes a low-latency serverless ML inference system called Optimus via a new container management scheme. Our key insight is that the loading of a new model can be significantly accelerated when using an existing model with a similar structure in a warm but idle container. We thus develop a novel idea of inter-function model transformation for serverless ML inference, which delves into models within containers at a finer granularity of operations, designs a set of in-container meta-operators for both CNN and transformer model transformation, and develops an efficient scheduling algorithm with linear complexity for a low-cost transformation strategy. Our evaluations on thousands of models show that Optimus reduces inference latency by 24.00% ~ 47.56% in both simulated and real-world workloads compared to state-of-the-art work.

Recently, stateless blockchains have been proposed to alleviate the storage overhead for nodes. A stateless blockchain achieves storage-consensus parallelism, where storage workloads are offloaded from on-chain consensus, enabling more resource-constraint nodes to participate in the consensus. However, existing stateless blockchains still suffer from limited throughput. In this paper, we present Porygon, a novel stateless blockchain with three-dimensional (3D) parallelism. First, Porygon separates the storage and consensus of transactions as the stateless blockchain, achieving the storage-consensus parallelism. This first-dimensional parallelism divides the processing of transactions into several stages and scales the network by supporting more nodes in the system. Based on such a design, we then propose a pipeline mechanism to achieve second-dimensional inter-block parallelism, where relevant stages of processing transactions are pipelined efficiently, thereby reducing transaction latency. Finally, Porygon presents a sharding mechanism to achieve third-dimensional inner-block parallelism. By sharding the executions of transactions of a block and adopting a lightweight cross-shard coordination mechanism, Porygon can effectively execute both intra-shard and cross-shard transactions, consequently achieving outstanding transaction throughput. We evaluate the performance of Porygon by extensive experiments on an implemented prototype and large-scale simulations. Compared with existing blockchains, Porygon boosts throughput by up to 20x, reduces network usage by more than 50%, and simultaneously requires only 5MB of storage consumption per node.

Since the advent of decentralized financial applications based on blockchains, new attacks that take advantage of manipulating the order of transactions have emerged. To this end, order fairness protocols are devised to prevent such order manipulations. However, existing order fairness protocols adopt time-consuming mechanisms that bring huge computation overheads and defer the finalization of transactions to the following rounds, eventually compromising system performance. In this work, we present Auncel, a novel consensus protocol that achieves both order fairness and high performance. Auncel leverages a weight-based strategy to order transactions, enabling all transactions in a block to be committed within one consensus round, without cost computation and further delays. Furthermore, Auncel achieves censorship resistance by integrating the consensus protocol with the fair ordering strategy, ensuring all transactions can be ordered fairly. To reduce the overheads introduced by the fair ordering strategy, we also design optimization mechanisms, including dynamic transaction compression and adjustable replica proposal strategy. We implement a prototype of Auncel based on HotStuff and construct extensive experiments. Experimental results show that Auncel can increase the throughput by 6× and reduce the confirmation latency by 3× compared with state-of-the-art order fairness protocols.

Eclipse attack, one of the most common attacks in blockchain networks, not only affects the overall throughput and latency of the network, but also leads to a large number of redundant synchronization messages, resulting in a serious waste of resources. Existing studies propose auto-tuning to improve the performance of blockchains by fine-tuning the system parameters, which can also reduce the loss from eclipse attacks. However, these techniques only consider throughput and latency in the parameter search and ignore the wasted resources due to eclipse attacks, resulting in their inability to retrieve the optimal parameter configuration under eclipse attack. To address this issue, we propose Apollo, an end-to-end permissioned blockchain network auto-tuning system based on deep reinforcement learning (DRL) against eclipse attacks, and take Hyperledger Fabric as an example to verify the effectiveness of our system. The key component of Apollo is the enhanced Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm: DPB-MADDPG, where the number of agents is dynamically determined according to the number of node organizations and the type of nodes. In addition, we select two metrics to measure the resource waste of an eclipse attack based on preliminary experiments, including cluster common message sending time (CCMST) and gossip state commit duration (GSCD). In a Fabric system with 4 peers and 3 orderers, our Apollo can increase system throughput by 125.55% and reduce latency, CCMST, and GSCD by 67.24%, 67.82%, and 32.63%, respectively, compared to traditional auto-tuning.

Global navigation satellite systems (GNSSs) provide efficient positioning services for location-aware Internet of Things (IoT) devices. However, GNSS non-line-of-sight (NLOS) signals can result in severe positioning errors in urban canyon areas. Existing deep-learning-based NLOS signal classification methods cannot appropriately model the spatiotemporal information of NLOS interference, resulting in limited accuracy across multiple locations. This study presents a spatiotemporal information-driven model that can capture environmental characteristics and signal temporal information simultaneously to improve NLOS classification accuracy across multiple locations. First, a visualization analysis of the signal distribution across multiple locations demonstrates the impact of environmental characteristics. In addition, the significance of both the spatial environmental features and the signal temporal features for NLOS classification is clarified by constructing a tree diagram of the data set. Second, we propose an airspace attention mechanism module and a long short-term memory (LSTM)-based temporal feature extraction module to model both types of features, respectively. Third, the learnable sparse regularizer is utilized to reduce feature redundancy and thereby realize a sparse representation, which improves model generalization performance. Finally, the spatiotemporal information-driven cross-attention model is developed to perform NLOS classification, which uses a cross-attention fusion strategy to integrate the two modules. We use real-world data sets collected across multiple urban canyon locations to test our model. Experiments show that the proposed model can achieve 98% classification accuracy across multiple locations. Generalization performance in unknown environments can be improved over 7% compared to several state-of-the-art models.

Sharding is a promising solution to enhance the scalability of blockchain. However, previous sharding systems adopt the lock-based cross-shard protocol to exclusively handle one-shot cross-shard transactions, leading to low-efficiency executions and unavailable calls when handling complex cross-shard contracts that introduce multi-shot cross-shard transactions to invoke multiple contracts managed by different shards.In this paper, we aim to enable efficient execution of arbitrarily complex cross-shard contracts in blockchain sharding systems. First, we perform a calling-flow analysis on Ethereum contracts with more than 180 million real-world transactions and find that about 30% transactions invoke complex contracts. Then, motivated by the properties of these complex contracts, we propose an off-chain execution model, called ShardCon, to achieve efficient executions for complex cross-shard contracts by decoupling the contract execution from the cross-shard consensus. Next, we introduce a cross-shard contract execution engine and a contract-driven deployment rule to the overheads introduced by off-chain executions. Moreover, to adapt to the multi-chain property of a sharding system, we introduce an off-chain state atomic commit protocol. Finally, we implement a prototype and evaluate it with concrete cross-shard contracts, showing that ShardCon can achieve more than 10x increase in throughput and 2x decrease in confirmation latency than the state-of-the-art sharding systems.

Building on top of blockchain, payment channel networks-backed (PCNs) cryptocurrencies emerge as a promising solution for a mobile payment system with fewer intermediaries, more security, higher speed, and lower cost. A key problem for PCN is payment channel rebalancing, that is, finding a set of circular transactions that restore a PCN with skewed channel balances back into an equilibrium state. However, existing practice on payment channel rebalancing either has a hard limit on the problem size or tends to fall into local optimum. To address these challenges, we propose DRL-PCR, aDeepReinforcementLearning-basedPaymentChannelRebalancing algorithm. On one hand, DRL-PCR leverages the strong approximation ability of deep neural networks to handle large problem spaces. On the other hand, DRL-PCR decomposes the rebalancing problem into a sequence of decision-making problems and progressively builds the final solution. By aiming to find a globally optimized solution and solving the long-term optimization model of DRL, DRL-PCR is superior to greedy-based algorithms and can mitigate the risk of getting trapped in a local optimum. In particular, payment channel rebalancing typically involves dealing with graph-structured data, where the major obstacle lies in understanding the sophisticated circular dependencies between payment channels and routing paths. DRL-PCR achieves this by encoding the input data with a novel graph neural network-based model and capturing the circular dependencies through a customized message passing process. In addition, considering the distributed nature of PCN, DRL-PCR uses a leadership election protocol to elect leaders for decision-making. Evaluations on the historical data of two real-world PCNs demonstrate that DRL-PCR can restore the PCN to a more balanced state and improve the transaction throughput and success ratios by up to 2.1x and 1.6x, respectively.

Blockchain store states in Log-Structured Merge (LSM) tree-based database. Due to blockchain traceability, the growing ancient states are inevitably stored in the databases. Unfortunately, by default, this process mixescurrentandancientstates in the data layout, increasing unnecessary disk I/O access and slowing transaction execution. This paper proposes MoltDB, a scalable LSM-based database for efficient transaction execution through a novel idea ofancient state segregation, i.e., to segregate current and ancient states in the data layout. However, the frequently generated and uncertainly accessed characteristics of ancient states make the segregation challenging. Thus, we develop an “extract-compact” mechanism to batch extraction process for frequently generated ancient states and the LSM compaction process to relieve additional disk I/O overhead. Moreover, we design an adaptive LSM-based storage for the uncertainly accessed ancient states extracted for on-demand access. We implement MoltDB as a database engine compatible with many mainstream blockchains and integrate it into Ethereum for evaluation. Experimental results show that MoltDB achieves 1.3 × transaction throughput and 30% disk I/O latency savings over the state-of-the-art works.

Sharding technologies allow the Internet of Things (IoT) to deploy blockchains in large-scale applications with good scalability. However, conventional sharding strategies in IoT blockchain are highly restricted because most IoT devices are dynamic and heterogeneous. They fail to partition and reconfigure shards with a fine-balanced tradeoff between throughput and security. Therefore, we propose SmartChain, which is a dynamic and self-adaptive sharding framework devised for making sharding decisions on the IoT blockchain featured with dynamics and heterogeneity. Specifically, we elaborate on how SmartChain performs reconfiguration and provide a quantitative analysis of shard performance. We then formulate the long-term tradeoff of throughput and security as a Markov decision process. Considering the nature of time-varying devices (e.g., amount of computing power, location), we develop a Transferable Proximal Policy Optimization (PPO) with Demonstrations algorithm, namely TPPOD, to help quickly reconfigure shards when the environment changes. Thus, based on current state, SmartChain can adaptively and dynamically select shard number, partition structure, and primary selection mode. Evaluations show that SmartChain enables high throughput and low risk of security, and reduces 70% of the training time averaged over baselines. Our implementation of TPPOD is 8.3 times of average system reward compared with the PPO-based sharding strategy with uniform sampling.

Sharding scales throughput by splitting blockchain nodes into parallel groups. However, different shards’ independent and random scheduling for cross-shard transactions results in numerous conflicts and aborts, since cross-shard transactions from different shards may access the same account. A deterministic ordering can eliminate conflicts by determining a global order for transactions before processing, as proved in the database field. Unfortunately, due to the intertwining of the Byzantine environment and information isolation among shards, there is no trusted party able to predetermine such an order for cross-shard transactions. To tackle this challenge, this paper proposes Prophet, a conflict-free sharding blockchain based on Byzantine-tolerant deterministic ordering. It first depends on untrusted self-organizing coalitions of nodes from different shards to pre-execute cross-shard transactions for prerequisite information about ordering. It then determines a trusted global order based on stateless ordering and post-verification for pre-executed results, through shard cooperation. Following the order, the shards thus orderly execute and commit transactions without conflicts. Prophet orchestrates the pre-execution, ordering, and execution processes in the sharding consensus for minimal overhead. We rigorously prove the determinism and serializability of transactions under the Byzantine and sharded environment. An evaluation of our prototype shows that Prophet improves the throughput by 3.11× and achieves nearly no aborts on 1 million Ethereum transactions compared with state-of-the-art sharding.

Blockchain databases have attracted widespread attention but suffer from poor scalability due to underlying non-scalable blockchains. While blockchain sharding is necessary for a scalable blockchain database, it poses a new challenge named on-chain cross-shard database services. Each cross-shard database service (e.g., cross-shard queries or inter-shard load balancing) involves massive cross-shard data exchanges, while the existing cross-shard mechanisms need to process each cross-shard data exchange via the consensus of all nodes in the related shards (i.e., on-chain) to resist a Byzantine environment of blockchain, which eliminates sharding benefits. To tackle the challenge, this paper presents GriDB, the first scalable blockchain database, by designing a novel off-chain cross-shard mechanism for efficient cross-shard database services. Borrowing the idea of off-chain payments, GriDB delegates massive cross-shard data exchange to a few nodes, each of which is randomly picked from a different shard. Considering the Byzantine environment, the untrusted delegates cooperate to generate succinct proof for cross-shard data exchanges, while the consensus is only responsible for the low-cost proof verification. However, different from payments, the database services' verification has more requirements (e.g., completeness, correctness, freshness, and availability); thus, we introduce several new authenticated data structures (ADS). Particularly, we utilize consensus to extend the threat model and reduce the complexity of traditional accumulator-based ADS for verifiable cross-shard queries with a rich set of relational operators. Moreover, we study the necessity of inter-shard load balancing for a scalable blockchain database and design an off-chain and live approach for both efficiency and availability during balancing. An evaluation of our prototype shows the performance of GriDB in terms of scalability in workloads with queries and updates.

Deep reinforcement learning (DRL), which highly depends on the data representation, has shown its potential in many practical decision-making problems. However, the process of acquiring representations in DRL is easily affected by interference from models, and moreover leaves unnecessary parameters, leading to control performance reduction. In this article, we propose a double sparse DRL via multilayer sparse coding and nonconvex regularized pruning. To alleviate interference in DRL, we propose a multilayer sparse-coding-structural network to obtain deep sparse representation for control in reinforcement learning. Furthermore, we employ a nonconvex log regularizer to promote strong sparsity, efficiently removing the unnecessary weights with a regularizer-based pruning scheme. Hence, a double sparse DRL algorithm is developed, which can not only learn deep sparse representation to reduce the interference but also remove redundant weights while keeping the robust performance. The experimental results in five benchmark environments of the deep q network (DQN) architecture demonstrate that the proposed method with deep sparse representations from the multilayer sparse-coding structure can outperform existing sparse-coding-based DRL in control, for example, completing Mountain Car with 140.81 steps, achieving near 10% reward increase from the single-layer sparse-coding DRL algorithm, and obtaining 286.08 scores in Catcher, which are over two times the rewards of the other algorithms. Moreover, the proposed algorithm can reduce over 80% parameters while keeping performance improvements from deep sparse representations.

Payment channel networks (PCNs) are considered as a prominent solution for scaling blockchain, where users can establish payment channels and complete transactions in an off-chain manner. However, it is non-trivial to schedule transactions in PCNs and most existing routing algorithms suffer from the following challenges: 1) one-shot optimization, 2) privacy-invasive channel probing, 3) vulnerability to DoS attacks. To address these challenges, we propose a privacy-aware transaction scheduling algorithm with defence against DoS attacks based on deep reinforcement learning (DRL), namely PTRD. Specifically, considering both the privacy preservation and long-term throughput into the optimization criteria, we formulate the transaction-scheduling problem as a Constrained Markov Decision Process. We then design PTRD, which extends off-the-shelf DRL algorithms to constrained optimization with an additional cost critic-network and an adaptive Lagrangian multiplier. Moreover, considering the distribution nature of PCNs, in which each user schedules transactions independently, we develop a distributed training framework to collect the knowledge learned by each agent so as to enhance learning effectiveness. With the customized network design and the distributed training framework, PTRD achieves a good balance between the optimization of the throughput and the minimization of privacy risks. Evaluations show that PTRD outperforms the state-of-the-art PCN routing algorithms by 2.7%–62.5% in terms of the long-term throughput while satisfying privacy constraints.

Multiview dictionary learning (DL) is attracting attention in multiview clustering due to the efficient feature learning ability. However, most existing multiview DL algorithms are facing problems in fully utilizing consistent and complementary information simultaneously in the multiview data and learning the most precise representation for multiview clustering because of gaps between views. This article proposes an efficient multiview DL algorithm for multiview clustering, which uses the partially shared DL model with a flexible ratio of shared sparse coefficients to excavate both consistency and complementarity in the multiview data. In particular, a differentiable scale-invariant function is used as the sparsity regularizer, which considers the absolute sparsity of coefficients as the$\ell _{0}$norm regularizer but is continuous and differentiable almost everywhere. The corresponding optimization problem is solved by the proximal splitting method with extrapolation technology; moreover, the proximal operator of the differentiable scale-invariant regularizer can be derived. The synthetic experiment results demonstrate that the proposed algorithm can recover the synthetic dictionary well with reasonable convergence time costs. Multiview clustering experiments include six real-world multiview datasets, and the performances show that the proposed algorithm is not sensitive to the regularizer parameter as the other algorithms. Furthermore, an appropriate coefficient sharing ratio can help to exploit consistent information while keeping complementary information from multiview data and thus enhance performances in multiview clustering. In addition, the convergence performances show that the proposed algorithm can obtain the best performances in multiview clustering among compared algorithms and can converge faster than compared multiview algorithms mostly.

Sharding has been considered as a prominent approach to enhance the limited performance of blockchain. However, most sharding systems leverage a non-cooperative design, which lowers the fault tolerance resilience due to the decreased mining power as the consensus execution is limited to each separated shard. To this end, we present Benzene, a novel sharding system that enhances the performance by cooperation-based sharding while defending the per-shard security. First, we establish a double-chain architecture for function decoupling. This architecture separates transaction-recording functions from consensus-execution functions, thereby enabling the cross-shard cooperation during consensus execution while preserving the concurrency nature of sharding. Second, we design a cross-shard block verification mechanism leveraging Trusted Execution Environment (TEE), via which miners can verify blocks from other shards during the cooperation process with the minimized overheads. Finally, we design a voting-based consensus protocol for cross-shard cooperation. Transactions in each shard are confirmed by all shards that simultaneously cast votes, consequently achieving an enhanced fault tolerance and lowering the confirmation latency. We implement Benzene and conduct both prototype experiments and large-scale simulations to evaluate the performance of Benzene. Results show that Benzene achieves superior performance than existing sharding/non-sharding blockchain protocols. In particular, Benzene achieves a linearly-improved throughput with the increased number of shards (e.g., 32,370 transactions per second with 50 shards) and maintains a lower confirmation latency than Bitcoin (with more than 50 shards). Meanwhile, Benzene maintains a fixed fault tolerance at 1/3 even with the increased number of shards.

Social data produced from widely emerged social media activities are expected to promote information dissemination and engagement, or even make business intelligence more powerful. However, the recent increase in social media incidents of illegal surveillance and data breaches raises questions about the current data ownership model, in which centralized applications collect and control large amounts of user data. In this paper, we present SocialChain, which is a decentralized social data storage and sharing system based on blockchain that decouples user data and social applications to return data ownership to the user. We adopt Personal Data Store to extend off-chain storage for the social data, set up an identity establishment mechanism that can support WebID-based authentication functions using a unique identity assignment (i.e., WebID) as well as certificateless cryptography, and design a general framework that leverages smart contracts to help securely store and share social data in an automated manner. We develop a software prototype based on Ethereum and conduct case studies to test the effects of the adopted techniques on the performance. Experimental results show that SocialChain can provide easy-to-use interfaces while introducing relatively low latency, cost, and overhead and that it can support real-world social media applications.

Blockchain technology has gained popularity owing to the success of cryptocurrencies such as Bitcoin and Ethereum. Nonetheless, the scalability challenge largely limits its applications in many real-world scenarios. Off-chain payment channel networks (PCNs) have recently emerged as a promising solution by conducting payments through off-chain channels. However, the throughput of current PCNs does not yet meet the growing demands of large-scale systems because: 1) most PCN systems only focus on maximizing the instantaneous throughput while failing to consider network dynamics in a long-term perspective; 2) transactions are re-actively routed in PCNs, in which intermediate nodes only passively forward every incoming transaction. These limitations of existing PCNs inevitably lead to channel imbalance and the failure of routing subsequent transactions. To address these challenges, we propose a novel proactive look-ahead algorithm (PLAC) that controls transaction flows from a long-term perspective and proactively prevents channel imbalance. In particular, we first conduct a measurement study on two real-world PCNs to explore their characteristics in terms of transaction distribution and topology. On that basis, we propose PLAC based on deep reinforcement learning (DRL), which directly learns the system dynamics from historical interactions of PCNs and aims at maximizing the long-term throughput. Furthermore, we develop a novel graph convolutional network-based model for PLAC, which extracts the inter-dependency between PCN nodes to consequently boost the performance. Extensive evaluations on real-world datasets show that PLAC improves state-of-the-art PCN routing schemes w.r.t the long-term throughput from 6.6% to 34.9%.

Payment channel network (PCN) is a promising off-chain technology for blockchain scalability, but it suffers from poor sustainability in practice. In other words, due to the imbalanced transfer in channels, the balance in one direction of channels gradually becomes exhausted until the PCN is rebalanced via a consensus-based rebalancing protocol, during which the involved channels must be suspended. This paper presents Cycle, the first off-chain protocol for a sustainable PCN. It not only keeps the PCN at a balanced level consistently but also avoids the channel freeze incurred by the rebalancing protocol, leading to minimum failed payments and sustained PCN service, respectively. Cycle achieves these benefits based on a novel idea of asynchronous rebalancing. During the normal off-chain running, the participants share the information about their payments and asynchronously rebalance the PCN following the principle that payments along circular channels can cancel each other out. To guarantee security, the protocol resolves the disputes resulting from network latency or malicious participants by a message mechanism for synchronization and a smart contract for arbitration. Moreover, to address the privacy concern during the information sharing, a truncated Laplace mechanism is designed to achieve differential privacy. Finally, we provide a proof-of-concept implementation in Ethereum, over which a real data-based simulation shows that Cycle satisfies 31% more payments than the state-of-the-art technique.

This article studies the PBFT-based sharded permissioned blockchain, which executes in either a local datacenter or a rented cloud platform. In such permissioned blockchain, the transaction (TX) assignment strategy could be malicious such that the network shards may possibly receive imbalanced transactions or even bursty-TX injection attacks. An imbalanced transaction assignment brings serious threats to the stability of the sharded blockchain. A stable sharded blockchain can ensure that each shard processes the arrived transactions timely. Since the system stability is closely related to the blockchain throughput, how to maintain a stable sharded blockchain becomes a challenge. To depict the transaction processing in each network shard, we adopt the Lyapunov Optimization framework. Exploitingdrift-plus-penalty(DPP) technique, we then propose an adaptive resource-allocation algorithm, which can yield the near-optimal solution for each network shard while the shard queues can also be stably maintained. We also rigorously analyze the theoretical boundaries of both the system objective and the queue length of shards. The numerical results show that the proposed algorithm can achieve a better balance between resource consumption and queue stability than other baselines. We particularly evaluate two representative cases of bursty-TX injection attacks, i.e., the continued attacks against all network shards and the drastic attacks against a single network shard. The evaluation results show that the DPP-based algorithm can well alleviate the imbalanced TX assignment, and simultaneously maintain high throughput while consuming fewer resources than other baselines.

To support the increasing computation-intensive applications in the Internet of Things (IoT), edge computing is introduced to provide mobile devices computing resources for performing low-latency tasks. Therefore, how to design an effective and secure computing resource allocation mechanism is attracting increasing attention. A lot of works have been done to design an effective computational resource market for IoT, but the problems of vulnerability and inefficiency still exist. In this article, we propose a two-level Stackelberg game-based computing resource trading mechanism for mobile IoT devices with a credit-based payment approach, which is implemented by smart contracts on blockchain. In our model, the Stackelberg game consists of two levels, i.e., leader-level and user-level. In the leader-level, the computing service provider (CSP) and its agent constitute a composite leader. The agent purchases computing resource from CSP on credit and acts as a broker among leader-level and user-level reselling these computing resources to users. In the user-level, every user experiences social externality, which means users are interdependent. The leader-level subgame makes credit payment easier by making loaning and trading become a joint credit payment. The user-level subgame makes the market more active and closer to reality by introducing social externality. Besides, smart contracts can prevent malicious behaviors such as delay payment. We also conduct equilibrium analysis and prove the existence and uniqueness of the Nash equilibrium in our Stackelberg game-based model. Finally, we conduct numerical experiments to evaluate the cost of smart contracts and the performance of each entity with the proposed pricing mechanism.

Sharding can significantly improve the blockchain scalability, by dividing nodes into small groups called shards that can handle transactions in parallel. However, all existing sharding systems adopt complete sharding, i.e., shards are isolated. It raises additional overhead to guarantee the atomicity and consistency of cross-shard transactions and seriously degrades the sharding performance. In this paper, we present Pyramid, the first layered sharding blockchain system, in which some shards can store the full records of multiple shards thus the cross-shard transactions can be processed and validated in these shards internally. When committing cross-shard transactions, to achieve consistency among the related shards, a layered sharding consensus based on the collaboration among several shards is presented. Compared with complete sharding in which each cross-shard transaction is split into multiple sub-transactions and cost multiple consensus rounds to commit, the layered sharding consensus can commit cross-shard transactions in one round. Furthermore, the security, scalability, and performance of layered sharding with different sharding structures are theoretically analyzed. Finally, we implement a prototype for Pyramid and its evaluation results illustrate that compared with the state-of-the-art complete sharding systems, Pyramid can improve the transaction throughput by 2.95 times in a system with 17 shards and 3500 nodes.

Blockchain technologies allow the Internet of Things (IoT) to build trust among various interest parties. For the resource-limited IoT devices, offloading computation-intensive tasks (blockchain verification and mining tasks, and data process tasks) to edge servers for execution is considered as a promising solution in mobile-edge computing. However, conventional methods (such as linear programming or game theory) for the computation offloading problem cannot achieve long-term performance while the existing deep reinforcement learning (DRL)-based algorithms suffer from slow convergence, lack of robustness, and unstable performance. In this article, we propose a multiagent DRL framework to achieve long-term performance for cooperative computation offloading, in which a scatter network is adopted to improve its stability and league learning is introduced for agents to explore the environment collaboratively for fast convergence and robustness. First, we study the nonorthogonal multiple access-enabled cooperative computation offloading problem and formulate the joint problem as a Markov decision process by considering both the blockchain mining tasks and data processing tasks. Second, to avoid useless exploration and unstable performance, we initially train an intelligent agent represented by scatter networks using conventional expert strategies. Third, in order to enhance the performance, we subsequently establish a hierarchical league where agents collaborate with others to explore the environment. Finally, our experimental results demonstrate that our algorithm could perform better in terms of reducing energy cost and delay cost, and shortening almost 60% of the training time compared with the state-of-the-art approaches.

Software crowdsourcing is an emerging approach to software engineering with great potential for the subdivision and assignment of large-scale tasks. However, because of the centralization of the traditional crowdsourcing platform, information disclosure and nontransparent accounting may be difficult to avoid. To address this issue, we first introduce a novel blockchain-enabled crowdsourcing platform that integrates the functions of task assignment and resource lending via two dedicated smart contracts. Second, to ensure financial stability in the blockchain-enabled market and to match the difficulty of the received tasks with the ability of the workers, we design a dynamic, hierarchical pricing mechanism based on economic modeling methods and heterogeneous agent theory. With this mechanism, the market is divided dynamically into multiple levels according to the remuneration of the customers' offer and the market value of the workers' resources. Additional constraints are proposed to avoid possible malicious trading behavior from workers in the resource lending process. We prove theoretically the rationality of our model and demonstrate the dynamics of the model. We show that the market price and demand can be convergent and test the cost of executing the two smart contracts. Finally, extensive experimental results demonstrate the correctness and feasibility of the platform and confirm that the hierarchical pricing mechanism can maintain the stability of the market.

Mobile edge computing (MEC) is a promising solution to support resource-constrained devices by offloading tasks to the edge servers. However, traditional approaches (e.g., linear programming and game-theory methods) for computation offloading mainly focus on the immediate performance, potentially leading to performance degradation in the long run. Recent breakthroughs regarding deep reinforcement learning (DRL) provide alternative methods, which focus on maximizing the cumulative reward. Nonetheless, there exists a large gap to deploy real DRL applications in MEC. This is because: 1) training a well-performed DRL agent typically requires data with large quantities and high diversity, and 2) DRL training is usually accompanied by huge costs caused by trial-and-error. To address this mismatch, we study the applications of DRL on the multi-user computation offloading problem from a more practical perspective. In particular, we propose a distributed and collective DRL algorithm called DC-DRL with several improvements: 1) a distributed and collective training scheme that assimilates knowledge from multiple MEC environments, which not only greatly increases data amount and diversity but also spreads the exploration costs, 2) an updating method called adaptive n-step learning, which can improve training efficiency without suffering from the high variance caused by distributed training, and 3) combining the advantages of deep neuroevolution and policy gradient to maximize the utilization of multiple environments and prevent the premature convergence. Lastly, evaluation results demonstrate the effectiveness of our proposed algorithm. Compared with the baselines, the exploration costs and final system costs are reduced by at least 43 and 9.4 percent, respectively.

Computation offloading for cloud robotics is receiving considerable attention in academic and industrial communities. However, current solutions face challenges: 1) traditional approaches do not consider the characteristics of networked cloud robotics (NCR) (e.g., heterogeneity and robotic cooperation); 2) they fail to capture the characteristics of tasks in a robotic streaming workflow (RSW) (e.g., strict latency requirements and varying task semantics); and 3) they do not consider quality-of-service (QoS) issues for cloud robotics. In this paper, we address these issues by proposing a QoS-aware RSW allocation algorithm for NCR with joint optimization of latency, energy efficiency, and cost, while considering the characteristics of both RSW and NCR. We first propose a novel framework that combines individual robots, robot clusters, and a remote cloud for computation offloading. We then formulate the joint QoS optimization problem for RSW allocation in NCR while considering latency, energy consumption, and operating cost, and show that the problem is NP-hard. Next, we construct a data flow graph based on the characteristics of RSW and NCR, and transform the RSW allocation problem into a mixed-integer linear programming problem. To obtain a near-optimal solution in reasonable time, we also develop a heuristic algorithm. Experiments comparing our approach with others demonstrate significant performance gains, with improved QoS and reduced execution times.

To overcome the limitations on the scalability of current blockchain systems, sharding is widely considered as a promising solution that divides the network into multiple disjoint groups processing transactions in parallel to improve throughput while decreasing the overhead of communication, computation, and storage. However, most existing blockchain sharding systems adopt a static sharding policy that cannot efficiently deal with the dynamic environment in the blockchain system, i.e., joining and leaving of nodes, and malicious attack. This paper presents SkyChain, a novel dynamic sharding-based blockchain framework to achieve a good balance between performance and security without compromising scalability under the dynamic environment. We first propose an adaptive ledger protocol to guarantee that the ledgers can merge or split efficiently based on the dynamic sharding policy. Then, to optimize the sharding policy under dynamic environment with high dimensional system states, a deep reinforcement learning-based sharding approach has been proposed, the goals of which include: 1) building a framework to evaluate the blockchain sharding systems from the aspects of performance and security; 2) adjusting the re-sharding interval, shard number and block size to maintain a long-term balance of the system’s performance and security. Experimental results show that SkyChain can effectively improve the performance and security of the sharding system without compromising scalability under the dynamic environment in the blockchain system.

To achieve software crowdsourcing, it is significant to develop a suitable framework for software development and quality control to motivate workers to participate and well-perform in crowdsourcing. Although the existing works have achieved some positive results, they do not achieve secure and efficient software crowdsourcing due to the “trust” and “efficiency” issues. For example, the existing centralized software crowdsourcing suffers from lack of trust, low reliability, and high costs. We first exploit blockchain technologies for software crowdsourcing and propose a blockchain-empowered decentralized framework for software development and quality control to achieve secure and efficient software crowdsourcing. Then we describe and analyze the blockchain-empowered decentralized framework for software crowdsourcing in detail. Ultimately, we analyze the security and efficiency performance of the proposed blockchain-empowered decentralized crowdsourcing framework.

With the development of the internet technology, a large amount of softwares have emerged to meet users' increasing needs. At the mean time, software systems have been faced with a problem that they must adapt to the dynamic network environment. It is obvious that a variety of software development models have been proposed in the past few decades. However, the majority of these methods are gradually unadaptable to new circumstances. In this paper, we proposed a new software development model integrating collective intelligence, service and ecosystem. On the one hand, we have introduced the model in detail. On the other hand, We took a practical example to demonstrate the effectiveness of the proposed model.

Edge computing is a promising paradigm to expand the capability of Internet of Things (IoT) devices by computation offloading. To establish a distributed ledger to provide a secure and trusted environment for the resource allocation between edge servers and IoT devices, the emerging blockchain technology has attracted a lot of attention recently. However, in practice, edge resource allocation in IoT devices often involves multi-layer structures, which poses a challenge due to information incompleteness among different layers. Moreover, how to design a suitable and efficient blockchain framework for hierarchical resource allocation markets is a critical issue. In this paper, we apply blockchain to propose a secure and efficient hierarchical resource allocation framework for edge computing. First, we study the edge computing resource allocation problem in the hierarchical market of IoT devices, in which the IoT devices beyond the coverage of Access Points can participate in the resource allocation through middlemen. To solve the problem, a smart contract-based hierarchical auction mechanism is developed. The edge computing resources allocated in the top market can be continually reallocated to the sub-markets based on the mechanism, which then leads an efficient solution that maximizes the social welfare of the whole participants. Moreover, the mechanism is implemented as a smart contract in the blockchain, which enforces the rule of the hierarchical auction in a non-deniable and automated manner. Finally, the extensive simulations demonstrate the correctness and performance of the proposed mechanism.

Currently, data sharing for the Internet of Vehicles (IoV) applications has drawn much attention in the framework of developing smart cities and smart transportation. A critical challenge for data sharing is to incentivize users to participate in collecting and sharing data. The traditional incentive mechanism of crowdsourcing is not practical for IoV because of its trust issues. Although blockchain technology has been introduced to address trust issues and security challenges, ensuring trust in off-chain data for the blockchain-based approaches is still an open issue. In this article, we propose a quality-driven auction-based incentive mechanism based on a consortium blockchain that guarantees trust in both on-chain data and off-chain data. We first introduce a consortium blockchain that is used as an open and distributed hyperledger to address the security issue of on-chain data. Then, we formulate the problem as a reverse auction in which the platform acts as an auctioneer that purchases data from users. By utilizing a data quality-driven auction model, the evaluated data quality via expectation maximization is used to ensure the trust in off-chain data. The quality-driven, auction-based incentive mechanism can obtain the high-quality data and optimal social welfare with low social cost. Otherwise, we design a smart contract to perform the data sharing automatically. Finally, the extensive simulations show that our proposed algorithm achieves maximum social welfare, outperforms other solutions, and scales well when the number of users or tasks increase. Moreover, the performance of the smart contract shows its low computing cost.

With the great advance in the Internet-of-Things (IoT) sector, the recent years have witnessed an unprecedented wave of the proliferation of heterogeneous IoT devices and applications. Among them, some have stringent hard deadlines which can only be satisfied by the emerging paradigm of mobile-edge computing (MEC), while the others may pose elastic soft deadlines which can be flexibly fulfilled by cloud computing. However, with the presence of both temporal and spatial diversities of the resource cost of MEC and cloud, it remains a practical challenge how to efficiently provision the MEC and cloud resource to minimize the long-term operational cost, while still guaranteeing both hard and soft deadlines for heterogeneous IoT applications. To navigate such an inherent performance-cost tradeoff, an efficient online cloud-edge resource provisioning framework is proposed, based on the delay-aware Lyapunov optimization technique. Without requiring a priori knowledge of the statistics of the cloud-edge system, the proposed framework allows to make online greedy decisions on how much MEC and cloud resources to be provisioned to heterogeneous IoT applications. Through rigorous theoretical analysis, we prove that without violating both the hard and soft deadlines of heterogeneous IoT applications, the long-term operational cost can be pushed arbitrarily close to the offline optimum. With extensive evaluations driven by realistic traffic and cost traces, we empirically demonstrate the cost efficiency of the proposed cloud-edge resource provisioning framework.

As a fundamental problem in social network analysis, community detection has recently attracted wide attention, accompanied by the output of numerous community detection methods. However, most existing methods are developed by only exploiting link topology, without taking node homophily (i.e., node similarity) into consideration. Thus, much useful information that can be utilized to improve the quality of detected communities is ignored. To overcome this limitation, we propose a new community detection approach based on nonnegative matrix factorization (NMF), namely, homophily preserving NMF (HPNMF), which models not only link topology but also node homophily of networks. As such, HPNMF is able to better reflect the inherent properties of community structure. In order to capture node homophily from scratch, we provide three similarity measurements that naturally reveal the association relationships between nodes. We further present an efficient learning algorithm with convergence guarantee to solve the proposed model. Finally, extensive experiments are conducted, and the results demonstrate that HPNMF has strong ability to outperform the state-of-the-art baseline methods.

Offloading computation-intensive blockchain mining tasks to the edge servers (ESs) is a promising solution for blockchain-empowered Industrial Internet of Things (IIoT) because the computing capabilities in IIoT are usually limited, whereas the blockchain mining tasks are computationally intensive. However, the computation offloading solutions for data processing tasks and for blockchain mining tasks have been studied separately. Moreover, most of the existing solutions for offloading assume that all IIoT devices can directly connect to the ESs or cloud data centers. To address these issues, in this paper, we propose a multihop cooperative and distributed computation offloading algorithm that considers the data processing tasks and the mining tasks together for blockchain-empowered IIoT. First, we study the multihop computation offloading problem for both the data processing tasks and the mining tasks to minimize the economic cost of IIoT devices. Second, we formulate the offloading problem as a potential game in which the IIoT devices can make their decisions autonomously and prove the existence of Nash equilibrium (NE) for the game. Third, we design an efficient distributed algorithm based on exchanging messages between IIoT devices to achieve the NE with low computational complexity. Lastly, our experimental results demonstrate that our distributed algorithm scales well as the number of IIoT devices increases and has the minimum system cost compared with other approaches.

The introduction of edge computing into blockchain-enabled Internet of Things (IoT) for offloading computational tasks is attracting increasing attention. Computing resource trading unavoidably happens in edge-assisted IoT. However, efficient computing resource trading cannot be achieved because of the “cold start” and “long return” problems. To address these challenges, we propose to use a credit-based payment for fast computing resource trading in edge-assisted blockchain-enabled IoT; therefore, the IoT nodes can finish fast payment and frequent trading by borrowing resource coins from other IoT nodes based on their credit values. In our resource-coin loan problem, we propose an iterative double-auction-based algorithm, where a broker is introduced to solve the loan allocation problem and to determine the size of the loan each lender would provide to each borrower. Furthermore, the broker enforces specific loan pricing rules to induce the borrowers and lenders to bid truthfully. Then, the hidden privacy information could be extracted to achieve the optimal resource-coin allocation and loan pricing. The proposed algorithm can maximize the economic benefits while protecting privacy. Simulations showed that the proposed algorithm can maximize social welfare. In addition, we compared the proposed algorithm with the credit-bank-based method in terms of the satisfaction function and payments. The experimental results demonstrated that the proposed algorithm was individually rational, truthful, and budget-balanced.

With the advancement and emergence of diverse network services in Internet of Vehicles (IoV), large volume of data are collected and stored, making data important properties. Data will be one of the most important commodities in the future blockchain-based IoV systems. However, efficiency challenges have been commonly found in blockchain-based data markets, which is mainly caused by transaction confirmation delays and the cold-start problems for new users. To address the efficiency challenges, we propose a secure, decentralized IoV data-trading system by exploiting the blockchain technology, and design an efficient debt-credit mechamism to support efficient data-trading in IoV. In the debt-credit mechanism, a vehicle with loan demand could loan from multivehicles by promising to pay interest and reward. In particular, we encourage loaning among vehicles by a motivation-based investing and pricing mechanism. We formulate a two-stage Stackelberg game to maximize the profits of borrower vehicle and lender vehicles jointly. In the first stage, the borrower vehicle set the interest rate and reward for the loan as its pricing strategies. In the second stage, the lender vehicles decide on their investing strategies. We apply backward induction to analyze the subgame perfect equilibrium at each stage for both independent and uniform pricing schemes. We also validate the existence and uniqueness of Stackelberg equilibrium. The numerical results illustrate the efficiency of the proposed pricing schemes.

With the rapid development of the Internet of Things (IoT) in the era of big data, the amount of collected data has increased dramatically. Data are one of the most important commodities in IoT. To maximize the utility of the collected data, it is crucial to design an open IoT data market that enables data owners and consumers to carry out data trading securely and efficiently. To address the challenge of security presented by an untrusted and nontransparent data market, we propose an edge/cloud-computing-assisted, blockchain-enhanced data market framework to support secure and efficient IoT data trading, with a particular focus on an optimal pricing mechanism. In this mechanism, an authorized market-agency works as a scheduler, determining the win-owner and its pricing strategy to the consumer. We formulate a two-stage Stackelberg game to solve the pricing and purchasing problem of the data consumer and the market-agency. In the first stage of the game, the market-agency gives the win-owner and its pricing strategy. In the second stage, the data consumer decides on its purchasing quantity of data. We consider competition between data owners and propose a competition-enhanced pricing scheme (CPS). We apply backward induction to analyze the subgame perfect equilibrium at each stage for both independent and CPSs. Lastly, we validate the existence and uniqueness of Stackelberg equilibrium, and the numerical results show the efficiency of the CPS.

The blockchain-empowered mobile-edge computing (MEC) is a promising solution for enhancing the computation capabilities of mobile equipments (MEs) to process computation-intensive tasks such as the real-time data processing tasks and mining tasks. However, because of the “cold start” and “long return” problems, efficient computation offloading cannot be achieved in blockchain-empowered MEC because the MEs do not always have enough coins to afford the offloading service cost. In this article, we study the joint computation-offloading and coin-loaning problem for blockchain-empowered MEC to minimize the total cost of all MEs. We introduce the banks that can provide loan services to the MEs to address the above two issues. We formulate the problem as a noncooperative game to model the competitions between the myopic MEs. By using a potential game method, we prove the existence of a pure-strategy Nash equilibrium (NE) and design a distributed algorithm to achieve the NE point with low computational complexity. We also provide an upper bound on the price of anarchy of the game by theoretical proof. Besides, two smart contracts are designed to automatically perform the computing resource trading and coin loaning processes. Lastly, our simulation results show that our proposed algorithm can significantly reduce the total cost of all MEs, has better performance compared with other solutions, and scales well as the number of MEs increases. Moreover, the financial cost for executing the two smart contracts on the Ethereum network is low.

The concept of the industrial Internet of things (IIoT) is being widely applied to service provisioning in many domains, including smart healthcare, intelligent transportation, autopilot, and the smart grid. However, because of the IIoT devices' limited onboard resources, supporting resource-intensive applications, such as 3D sensing, navigation, AI processing, and big-data analytics, remains a challenging task. In this paper, we study the multi-hop computation-offloading problem for the IIoT-edge-cloud computing model and adopt a game-theoretic approach to achieving Quality of service (QoS)-aware computation offloading in a distributed manner. First, we study the computation-offloading and communication-routing problems with the goal of minimizing each task's computation time and energy consumption, formulating the joint problem as a potential game in which the IIoT devices determine their computation-offloading strategies. Second, we apply a free-bound mechanism that can ensure a finite improvement path to a Nash equilibrium. Third, we propose a multi-hop cooperative-messaging mechanism and develop two QoS-aware distributed algorithms that can achieve the Nash equilibrium. Our simulation results show that our algorithms offer a stable performance gain for IIoT in various scenarios and scale well as the device size increases.

The cost-minimization problem for streaming workflow (SW) has already become increasingly important and even critical in stream big data processing, particularly for geographically distributed datacenters, because of its huge demand on computing and communicating resources. Existing virtual machine (VM) allocation algorithms in cloud computing have been widely applied to batch-processing models; however, none of them can be successfully applied to SW because: 1) they failed to adapt the continuous execution characteristic of SW; and 2) most of them are all based on the assumption that the price of traffic and VMs among datacenters are uniform. In this paper, we propose a transformation-based SW allocation algorithm with the goal of cost-minimization for stream big data processing in geographically distributed datacenters, considering the characteristics of SW and price heterogeneity among geographically distributed datacenters. We first propose a cost-aware workflow transformation framework based on eight well-designed and verified transformation rules for cost reduction to adapt the continuous execution characteristic of SW. We then formulate the joint VM-traffic optimization problem and show that it is NP-hard. To produce the optimal solution in polynomial time, we then transform the SW allocation problem into the minimum-cost maximum-flow problem, considering both traffic and VMs price heterogeneity. Finally, our experimental results validate the high cost efficiency of our approach with lower computing and communicating costs by optimizing the workflow specification and joint VM-traffic cost optimization.

Traditional robotic systems are often incapable of handling complex tasks due to hardware constraints, such as computing ability, storage space, and battery capacity. Cloud robotic systems, characterized by allowing multi-robot systems to access the powerful cloud infrastructures, is a promising solution to fulfill complex tasks, such as disaster management, real-time object recognition, 3D Simultaneous Localization And Mapping (SLAM). However, the destabilizing factors of network could lead to high latency of data transmission in cloud robotic systems, which have made great challenges to the fields that have high real-time requirements. What's more, the existence of heterogeneity of robots further complicates cloud robotics cooperation. In order to minimize the average response time in latency-aware scenarios, we jointly investigate task assignment and scheduling in Collaborative Cloud Robotic Systems (CCRS). We first formulate the problem into a Mixed-Integer Non-Linear Programming (MINLP) and then linearize it into an Integer Linear Programming (ILP) using discrete time structure. To meet the extensibility requirement, we further propose a partitioning-based algorithm to deal with large-scale task graphs. The results show that our two approaches outperform the existing genetic algorithm and greedy algorithm.

Node influence refers to the ability of a node to disseminate information. The faster and wider the node spreads, the more influential it is. With its great theoretical and practical significance, identifying influential nodes in complex networks becomes one of the most attractive research topics in recent years. Consequently, a variety of different methods, such as betweenness, closeness, and pagerank, have been proposed to identify influential nodes. However, most of the existing methods cannot lead to a tradeoff between the ranking accuracy and time complexity, which limits their application on many real-world complex networks. In this paper, we propose a novel and efficient ranking method named semi-local centrality, to evaluate the influence of nodes more accurately. The method performs random walk to collect the influential surround nodes which are used to evaluate the nodes' influence. We use susceptible-infected-recovered (SIR) model to evaluate the performance of our method. The experimental results on four real-world networks show that the proposed method can identify influential nodes more effectively compared with five state-of-the-art methods.

The virtual machine (VM) allocation problem in cloud computing has been widely studied in recent years, and many algorithms have been proposed in the literature. Most of them have been successfully applied to batch processing models such as MapReduce; however, none of them can be applied to streaming workflow well because of the following weaknesses: 1) failure to capture the characteristics of tasks in streaming workflow for the short life cycle of data streams; 2) most algorithms are based on the assumptions that the price of VMs and traffic among data centers (DCs) are static and fixed. In this paper, we propose a streaming workflow allocation algorithm that takes into consideration the characteristics of streaming work and the price diversity among geo-distributed DCs, to further achieve the goal of cost minimization for streaming big data processing. First, we construct an extended streaming workflow graph (ESWG) based on the task semantics of streaming workflow and the price diversity of geo-distributed DCs, and the streaming workflow allocation problem is formulated into mixed integer linear programming based on the ESWG. Second, we propose two heuristic algorithms to reduce the computational space based on task combination and DC combination in order to meet the strict latency requirement. Finally, our experimental results demonstrate significant performance gains with lower total cost and execution time.

Nowadays, owing to the growth of quantity of data, the data mining techniques have been required on web exceedingly for extracting information from the data. Classification of text in data mining is very important and has been a hot issue on the topic. Especially, ontological taxonomy classification is important for more intelligent information reasoning. As it relates to data distribution of classes directly, we investigate relation between the data distribution and classification performance in this research. This paper shows the clue to improve taxonomy classification accuracy from a new viewpoint.

We propose a new optimal data placement technique to improve the performance of MapReduce in cloud data centers by considering not only the data locality but also the global data access costs. We first conducted an analytical and experimental study to identify the performance issues of MapReduce in data centers and to show that MapReduce tasks that are involved in unexpected remote data access have much greater communication costs and execution time, and can significantly deteriorate the overall performance. Next, we formulated the problem of optimal data placement and proposed a generative model to minimize global data access cost in data centers and showed that the optimal data placement problem is NP-hard. To solve the optimal data placement problem, we propose a topology-aware heuristic algorithm by first constructing a replica-balanced distribution tree for the abstract tree structure, and then building a replica-similarity distribution tree for detail tree construction, to construct an optimal replica distribution tree. The experimental results demonstrated that our optimal data placement approach can improve the performance of MapReduce with lower communication and computation costs by effectively minimizing global data access costs, more specifically reducing unexpected remote data access.

A Web service is defined as an autonomous unit of application logic that provides either some business functionality or information to other applications through an Internet connection. Web services are based on a set of eXtensible Markup Language (XML) standards such as Universal Description, Discovery and Integration (UDDI), Web Services Description Language (WSDL), and Simple Object Access Protocol (SOAP). Nowadays Web services are becoming more and more popular for supporting different social applications, thus there are also increasing demands and discussions about Web services privacy protection in information. In general, privacy policies describe an organization's data practices on what information they collect from individuals (e.g., consumers) and what (e.g., purposes) they do with it. To enable privacy protection for Web service consumers across multiple domains and services, the World Wide Web Consortium (W3C) published a document called “Web Services Architecture (WSA) Requirements” that defines some specific privacy requirements for Web services as a future research topic. This paper presents a mathematical model to construct the privacy policies in SOAP Message Exchange Patterns (MEP) for social services. Further, this paper also presents the privacy policies in security tokens with SOAP messages.

Automatic service composition can create new value-added services dynamically and automatically from existing services in an envisioned service-oriented architecture. However, despite considerable progress, web-scale uptake has been significantly less than initially anticipated because of several challenging issues, such as poor scalability, exponentially expanding search time in large search spaces, and the lack of service sociability caused by the isolation of services. In this paper, we propose an innovative methodology for moving from isolated service islands to a global social service network (GSSN) by developing a network model that supports service sociability. First, we propose the construction of a GSSN based on the quality of social links. We then propose an algorithm that maps the GSSN into a service cluster network to reduce the search space, and a quality-driven composition approach that enables exploitation of the service cluster network by providing workflow as a service. Finally, experimental results show that our GSSN-based approach can solve the service composition problem well, improving not only the response time but also the success rate.

Web services have had a tremendous impact on the Web for supporting a distributed service-based economy on a global scale. However, despite the outstanding progress, their uptake on a Web scale has been significantly less than initially anticipated. The isolation of services and the lack of social relationships among related services have been identified as reasons for the poor uptake. In this paper, we propose connecting the isolated service islands into a global social service network to enhance the services' sociability on a global scale. First, we propose linked social service-specific principles based on linked data principles for publishing services on the open Web as linked social services. Then, we suggest a new framework for constructing the global social service network following linked social service-specific principles based on complex network theories. Next, an approach is proposed to enable the exploitation of the global social service network, providing Linked Social Services as a Service. Finally, experimental results show that our approach can solve the quality of service discovery problem, improving both the service discovering time and the success rate by exploring service-to-service based on the global social service network.

Clustering Web services into functionally similar clusters is a very efficient approach to service discovery. A principal issue for clustering is computing the semantic similarity between services. Current approaches use similarity-distance measurement methods such as keyword, information-retrieval or ontology based methods. These approaches have problems that include discovering semantic characteristics, loss of semantic information and a shortage of high-quality ontologies. Further, current clustering approaches are considered only have simple data types in services' input and output. However, services that published on the web have input/ output parameter of complex data type. In this research, we propose clustering approach that considers the simple type as well as complex data type in measuring the service similarity. We use hybrid term similarity method which we proposed in our previous work to measure the similarity. We capture the semantic pattern exist in complex data types and simple data types to improve the ontology learning method. Experimental results show our clustering approach which uses complex data types in measuring similarity works efficiently.

Construction of Web service recommendation systems for users has become an important issue in service computing area. Content-based service recommendation is one category of recommendation systems. The system recommends services based on functionality of the services. Current content-based approaches use syntactic or semantic methods to calculate the similarity. However, syntactic methods are insufficient in expressing semantic concepts and semantic content-based methods only consider basic semantic level. Further, the approaches do not consider the domain specific context in measuring the similarity. Thus, they have been failed to capture the semantic similarity of Web services under a certain domain and this is affected to the performance of the recommendation. In this paper, we propose domain specific context aware recommendation approach that uses support vector machine and domain data set from search engine in similarity calculation process. Experimental results show that our approach works efficiently.

Web service filtering is an efficient approach to address some big challenges in service computing, such as discovery, clustering and recommendation. The key operation of the filtering process is measuring the similarity of services. Several methods are used in current similarity calculation approaches such as string-based, corpus-based, knowledge-based and hybrid methods. These approaches do not consider domain-specific contexts in measuring similarity because they have failed to capture the semantic similarity of Web services in a given domain and this has affected their filtering performance. In this paper, we propose a context-aware similarity method that uses a support vector machine and a domain dataset from a context-specific search engine query. Our filtering approach uses a spherical associated keyword space algorithm that projects filtering results from a three-dimensional sphere to a two-dimensional (2D) spherical surface for 2D visualization. Experimental results show that our filtering approach works efficiently.

This paper addresses automatic service composition (ASC) as a means to create new value-added services dynamically and automatically from existing services in service-oriented architecture and cloud computing environments. Manually composing services for relatively static applications has been successful, but automatically composing services requires advances in the semantics of processes and an architectural framework that can capture all stages of an application's lifecycle. A framework for ASC involves four stages: planning an execution workflow, discovering services from a registry, selecting the best candidate services, and executing the selected services. This four-stage architecture is the most widely used to describe ASC, but it is still abstract and incomplete in terms of scalable goal composition, property transformation for seamless automatic composition, and integration architecture. We present a workflow orchestration to enable nested multilevel composition for achieving scalability. We add to the four-stage composition framework a transformation method for abstract composition properties. A general model for the composition architecture is described herein and a complete and detailed composition framework is introduced using our model. Our ASC architecture achieves improved seamlessness and scalability in the integrated framework. The ASC architecture is analyzed and evaluated to show its efficacy.

Organizing Web services into functionally similar clusters, is an efficient approach to discovering Web services efficiently. An important aspect of the clustering process is calculating the semantic similarity of Web services. Most current clustering approaches are based on similarity-distance measurement, including keyword, ontology and information-retrieval-based methods. Problems with these approaches include a shortage of high quality ontologies and a loss of semantic information. In addition, there has been little fine-grained improvement in existing approaches to service clustering. In this paper, we present a new approach to grouping Web services into functionally similar clusters by mining Web service documents and generating an ontology via hidden semantic patterns present within the complex terms used in service features to measure similarity. If calculating the similarity using the generated ontology fails, the similarity is calculated by using an information-retrieval-based term-similarity method that adopts term-similarity measuring techniques used by thesaurus and search engines. Another important aspect of high performance in clustering is identifying the most suitable cluster center. To improve the utility of clusters, we propose an approach to identifying the cluster center that combines service similarity with the term frequency-inverse document frequency values of service names. Experimental results show that our clustering approach performs better than existing approaches.

It is considered that Web services have had a tremendous impact on the Web as a potential silver bullet for supporting a distributed service-based economy on a global scale. However, despite the outstanding progress, their uptake on a Web scale has been significantly less than initially anticipated. The reasons are: first, the existing Web service frameworks such as gtraditionalh Web services, semantic Web services, and Web APIs have had a limited impact, second, isolated service islands without links to related services have hampered service discovery and composition. In this paper, we propose a methodology to drive innovation from isolated service islands into the global social service network to connect the islands. First, we propose Linked social service-specific principles based on Linked Data principles for publishing services on the open Web as linked social services using our new service model, and suggest a new platform for constructing a global social service network. Then, an approach is proposed to enable exploitation of a global social service network, providing Linked social service as a service. Finally, experimental results show that the Linked social service can solve the service discovery problem by enabling exploring service to service based on the global social service network.

In this paper, we propose a methodology to drive innovation from isolated service islands into the global social service network to connect the islands. First, we propose Linked social service-specific principles based on Linked Data principles for publishing services on the open web as linked social services using our new service model, and then an approach is proposed to enable exploitation of a global social service network, providing Linked social service as a service.

Automatic Service Composition (ASC) provides a new value-added service from existing services by user's request dynamically and automatically. User's requests consist of functional and nonfunctional requirements. During service composition, services that fulfill the functional requirements are located at the discovery stage. Abstract nonfunctional requirements should be identified mainly before the selection stage for service execution. Our research was motivated by the identification of abstract nonfunctional properties (NFPs) for a seamless ASC and proposes transformation from the abstract NFPs to intermediate-level NFPs based on the model of three levels of abstractness of NFPs. To solve the vagueness of the abstractness, we adapt approaches based not only on ontology but also on term similarity. The transformation between the intermediate and the concrete levels is carried out by a deterministic algorithm based on mapping of domain ontology. To evaluate the effectiveness of term similarity metrics for nonterminal terms, vector-based and large corpus-based approaches were investigated. The transformation performance based on precision over our test data set and ontology was evaluated.

New value-added services are created by automatic service composition (ASC) dynamically and automatically from existing services at the user's request. Complete ASC system requests solving very large and complex realistic problems require consistent architecture. Most studies of service composition are based on four stages (planning, discovery, selection, and execution) and their variations such as integration of the stages or divergence in a stage. However, previous studies have not considered the functional scalability of ASC involving nested dynamic services in which the ASC calls inner ASCs internally during composition. In practice, there are many situations where dynamic services and existing services are combined in ASC. We present a blueprint for a modified four-stage composition architecture to allow for scalability in managing the nested composition flow. A general model for the composition architecture is described and a complete and detailed composition framework is introduced using our model. Finally, we analyze the proposed architecture to show its efficacy.

Automatic Service Composition (ASC) supports creation of a new value-added composite service from the existing services with automatic manner. There are two approaches, machine-oriented and human-oriented, for the composer. In the machine-oriented composer, every composition step is managed by the composer mainly. However, there are many possibilities of interventions by users for better composition performance. In this paper, design of user interface for the machine-oriented ASC based on our new composition architecture is suggested. Possible interactions at the all the stages of service composition are analyzed on the new architecture. Ontologies for ASC UI, visual component, data, and workflow are designed. The Selector UI is demonstrated as an example of UI for ASC, and whole composition scenario is illustrated. The design paradigm of ASC UI presented in the paper can be applied to human-oriented composition too.

Semantic similarity measures play important roles in information retrieval and natural language processing. Several researches calculate semantic similarity between two words using web search engines as corpus or manually compiled corpus. In this paper, a method to find the word Ri between two words P and Q and extract a relation of the words with PLSI (Probabilistic Latent Semantic Indexing) is proposed. The results of the experiments show that using the PLSI with smaller latent class such is effective in getting Ri which is more related to P and Q, and using the PLSI with over 5 latent class is effective in getting veiled relation between P and Q.