MobileEdgeComputing (MEC) has incentivized App vendors to outsource various services and applications to distributed edge nodes for low access latency. However, the data cached on these nodes is vulnerable to both intentional and accidental corruption, necessitating periodic audits ofEdgeDataIntegrity (EDI). Existing solutions either rely on a “fully trustworthy”ThirdPartyAuditor (TPA) or leverage blockchain to enhance trust. However, they overlook the security risks brought by the use of blockchain, particularly collusion attacks. Furthermore, while they employ achallenge-responsemechanism to enhance efficiency by batch verification, they fail to account for the heterogeneity of edge nodes. To address these challenges, we propose$\mathtt {CTCV}$, aCollusion-resistant andTime-awareCollaborativeVerification framework.$\mathtt {CTCV}$aims to accommodate edge node heterogeneity while enabling public audits and batch verification without introducing additional security risks. Specifically, it incorporates blockchain to allow edge nodes to collaboratively verify EDI without trust dependencies, while mitigating collusion attacks through a carefully designed proof generation and verification approach. Considering the resource and state heterogeneity of edge nodes,$\mathtt {CTCV}$employs atime-constrained challenge-responsemechanism that sets a time threshold$\mathcal {T}$between the verification request issuance and the integrity proof inspection to avoid excessive delays. The selection guideline of$\mathcal {T}$, along with the correctness, efficiency, and collusion resistance of$\mathtt {CTCV}$, are rigorously analyzed. Extensive experiments validate that$\mathtt {CTCV}$is computationally and communicationally efficient compared to three baselines: EdgeWatch, EDI-S, and EDI-V. On average, given 10 edge nodes,$\mathtt {CTCV}$outperforms EdgeWatch, EDI-S, and EDI-V with computation efficiency improvements of 7.9, 9.0, and 5.0 times, and communication efficiency improvement of 2063.0, 4.8, and 2.6 times, respectively.

Multiaccess edge computing (MEC) enables low-latency service delivery by deploying application instances on edge servers. However, edge servers are prone to failures, making it challenging to meet diverse user reliability requirements. A common approach is to deploy redundant instances across multiple edge servers, which improves reliability but increases costs and limits the number of users that can be served within budget. Therefore, efficient deployment strategies are needed to balance cost-effectiveness and reliability guarantees, thereby maximizing the app vendor’s revenue. In this article, we investigate the problem ofRevenue maximization forReliability-awareEdgeApplicationDeployment ($\text{R}^{2}\text{EAD}$). Our objective is to maximize the app vendor’s revenue by deploying its applications on heterogeneous edge servers, subject to budget and resource constraints and users’ diverse reliability requirements. We prove that the$\text{R}^{2}\text{EAD}$problem is$\mathcal {\text{NP}}$-hard and propose an efficient approximation algorithm named$\text{R}^{2}\text{EAD}$-A. By reducing the problem to a nonmonotone submodular maximization problem with curvature$\alpha$under multiple knapsack constraints, we prove that$\text{R}^{2}\text{EAD}$-A achieves a constant approximation ratio of$\frac{1}{\alpha }(1 - e^{-\alpha })$. Extensive evaluations demonstrate that$\text{R}^{2}\text{EAD}$-A outperforms the representative approaches across all tested cases.

Satellite edge computing (SEC) extends the capabilities of task offloading from the ground to near-Earth space by leveraging the wide coverage of satellites. However, the high-speed movement and long-range communication of satellites lead to Doppler shifts and signal loss, which can result in a high bit error rate (BER) and degrade communication link quality. As a result of the high BER, error correction is required, inevitably incurring additional delay during task offloading. Therefore, in this paper, we focus on the problem ofErrorCorrection awareDependencyTaskOffloading (EC-DTO) in satellite environments. By jointly considering task dependencies and BER-induced error correction delays, we formulate the EC-DTO problem as a constrained optimization problem with the objective of minimizing the makespan of users' tasks. To solve this problem, we propose APOS, which is an event-driven offloading framework based on task-AdaptivePrioritization andOptimistic-finish-time-based nodeSelection. Specifically, APOS adopts an online offloading framework that triggers scheduling upon task arrivals and completions with task dependency awareness. It iteratively updates task readiness and performs offloading decisions to reduce the overall makespan in the SEC environment. Simulation results show that APOS outperforms the representative methods, lowering the average makespan by 20.7% and the deadline violation ratio by 84.5% on average across all cases.

The edge computing paradigm provides fine-grained and distributed resources to users with low service latency. To further utilize the advantage of edge computing to improve users' satisfaction, it is essential to jointly optimize service deployment, task offloading, and resource allocation. However, this is challenging because of limited edge resources, diverse task demands, and coupled decisions. In this paper, we propose EdgePro, a novel adaptive edge service provision approach based on safe deep reinforcement learning, aiming to maximize user satisfaction while fulfilling multiple constraints including deployment budget and edge server resources. Specifically, we formulate the optimization problem as a constrained Markov decision process. By designing a constraint-penalty function, we transform the original multi-constraint problem into an equivalent single-constraint problem, addressing the training oscillations caused by conflicts in satisfying multiple constraints. To handle the discrete-continuous coupled decisions, we employ multiple deep neural networks for coordinated control. Then, we propose a safe deep reinforcement learning algorithm based on augmented proximal policy optimization, which adaptively solves the formulated problem while satisfying safety constraints. Experimental results show that EdgePro significantly outperforms benchmark approaches in user satisfaction, convergence speed, and satisfying constraints.

The distributed deployment of Large Language Models (LLM) on edge servers close to users has unlocked the service provider's potential to deliver low-latency inference. To obtain more benefit by serving more resource-demanding inference tasks based on resource-limited edge servers, it is critical for the service provider to allocate inference to suitable edge servers. Three new challenges hinder existing approaches from being implemented: the distributed LLM inference requires edge servers to collaborate following a novel workflow different from other tasks; the generative nature of LLM incurs uncertainty in task resource occupations; considering the heterogeneity in users' latency requirements and service benefit, merely minimizing the total user-perceived latency can not maximize the benefit. In this paper, we make the first attempt to study the edge inference allocation problem for distributed LLM deployment while conquering these challenges. Specifically, we propose a collaborative workflow for edge servers to conduct distributed LLM inferences. Then, we estimate the resource occupations by employing Exact Conic Reformulation (ECR). Based on this, with the objective of maximizing the total service benefit, we formulate the inference allocation problem as a binary integer programming problem which is NP-hard. An approximate algorithm is proposed to find approximate solutions efficiently. Extensive experiments based on a real-world dataset demonstrate the performance of our approach.

As the demand for global computing coverage continues to surge, satellite edge computing emerges as a pivotal technology for the next generation of networks. Unlike ground-based edge computing, Low Earth Orbit (LEO) satellites face distinctive challenges, including high-speed mobility and resource limitations, etc. Therefore, effectively utilizing LEO satellites for global coverage services is crucial but challenging due to their dynamic coverage areas and diverse task requirements. To address these challenges, we introduce a novel dual-cloud edge collaborative task offloading architecture in the satellite network-assisted edge computing environment, namely,Satellite-GroundTaskOffloading (SGTO). The architecture employs a Geostationary Earth Orbit (GEO) satellite and a ground cloud computing center as satellite cloud and ground cloud, respectively, and LEO satellites as edge nodes. We formally define the task offloading problem in theSGTOwith the aim of minimizing the average latency and average energy consumption. We then propose an adaptive approach namedSGTO-Afrom the perspective of satellites to adaptively solve the problem leveraging deep reinforcement learning. Specifically, we transform the task offloading problem into a Markov decision process and adopt the generalized proximal policy optimization (GePPO) algorithm to solve the problem. Finally, experimental results demonstrate thatSGTOarchitecture andSGTO-Aoutperform the representative approaches in terms of average latency, average energy consumption and running time.

In edge computing, a user prefers offloading his/her task to nearby edge servers to maximize the offloading utility. However, this inevitably exposes the user's location privacy information when suffering from the side-channel attacks based on offloading decision behaviors and Received Signal Strength Indicators (RSSI). Existing works only consider the scenario with one untrusted edge server or defend only against one of the attacks. In this paper, we first study the edge task offloading problem with comprehensive privacy protection against these side-channel attacks from multiple edge servers. To address this problem while ensuring satisfactory offloading utility, we develop aLocationImitation-based EdgeTaskOffloading approachLITO. Specifically, we first determine a suitable perturbation region centered at the user's real location for a balance between offloading utility and privacy protection, and then propose a modified Laplace mechanism to generate a fake location meeting geo-indistinguishability within the region. Subsequently, to mislead the side-channel attacks to the fake location, we design an approximate algorithm and a transmit power control strategy to imitate the offloading decisions and RSSIs at the fake location, respectively. Theoretical analysis and experimental evaluations demonstrate the performance ofLITOin improving privacy protection and guaranteeing offloading utility.

Edge computing enables low-latency services by deploying application instances near users. Application vendors tend to serve more users with higher service satisfaction and a limited budget. This raises a critical yet open problem - optimally deploying application instances and allocating users to edge servers to maximize service utility while fulfilling multiple constraints, including user latency requirements, budget limitations, and edge resource constraints. To address this problem, we formulate it as a constrained optimization problem that jointly solves two tightly coupled sub-problems:ApplicationDeployment andUserAllocation (named as ADUA problem). The objective is to maximize overall service utility by optimizing resource utilization and service satisfaction under latency, budget, and resource constraints. We reformulate the ADUA problem as a multi-armed bandit (MAB) problem and then propose a novel approach namedUMESPbased on penalized MAB framework. Specifically, we design a marginal utility–aware reward function to align bandit learning with the optimization objective. We introduce a penalty mechanism to effectively handle constraint violations. By further integrating the upper confidence bound policy,UMESPachieves adaptive and constraint-aware balance between exploration and exploitation. Theoretical analysis demonstrates thatUMESPachieves bounded regret. Extensive experiments verify thatUMESPexhibits stable convergence in all tests and, on average, outperforms six representative approaches in overall service utility.

Abstract In recent years, the transformative evolution of cloud computing has reshaped organizational practices by enabling the outsourcing of web service applications. This shift has led to the emergence of the cloud environment, characterized by the involvement of Cloud Service Providers (CSPs) and intelligent applications. Cloud Service Composition (CSC) has become pivotal in this context, playing a crucial role in enhancing efficiency, Quality of Service (QoS), and customer satisfaction through the aggregation of diverse Cloud Services (CSs) to create composite services. However, the vast array of available CSs presents a challenge in efficiently addressing specified QoS requirements, turning CSC into a recognized NP-hard problem. Existing solutions, often involving third-party brokers, struggle with scalability in large-scale systems and overlook crucial security concerns. To address these limitations, we propose the Hierarchical Service Composition (HSC) approach, leveraging blockchain and federated learning to minimize computational complexity. The integration of Blockchain-enabled Federated Learning (BFL) facilitates machine learning model training with decentralized data, ensuring practicality and fairness. HSC comprises an initialization phase and two selection layers. The first selection layer enables each CSP to efficiently select services using a pre-trained model, while the second selection layer employs a blockchain-based QoS-aware mechanism for the final composition result, addressing privacy concerns. HSC introduces a novel framework, collaborative service selection methods, and a smart selection algorithm, demonstrating remarkable composition efficiency in extensive simulations compared to the baseline approach.

Satellite edge computing, as an extension of ground edge computing, is a key technology for achieving seamless global computing coverage. However, the low earth orbit (LEO) satellites have limited computing resources and are moving at a high speed. This naturally poses a challenge to find more suitable computation offloading strategies with minimum network latency and energy consumption, especially when a large number of co-existing users are to offload their tasks. In this paper, therefore, we mainly focus on computation offloading in the satellite edge computing network (SECN) by jointly considering LEO satellites' mobility and SECN's heterogeneous resource constraints to explore more practical computation offloading strategies. We first formulate the problem ofMobility-awareComputationOffloading (MCO) in the SECN via specifying the effect of LEO satellites' high-speed movement on the computation offloading, aiming to minimize the network latency and energy consumption. Considering the MCO problem is discrete and non-convex as the objective function and constraints are associated with the binary decision variables. We then convert the original non-convex problem into a continuous convex problem which is proved to be feasible. To avoid a high computational complexity incurred by the extensive co-existing user offloading, we designMCO-A, a distributed algorithm based on ADMM (alternating direction method of multipliers) to solve the MCO problem efficiently. Finally, the performance ofMCO-Ais evaluated via extensive experiments including small-scale and large-scale scenarios. The experimental results show that MCO-A can achieve a lower network latency and energy consumption in an efficient way compared with the baseline and state-of-the-art approaches.

Multi-access edge computing (MEC) has emerged as a promising computing paradigm to push computing resources and services to the network edge. It allows applications/services to be deployed on edge servers for provisioning low-latency services to nearby users. However, in the MEC environment, edge servers may suffer from failures while the app vendor has to guarantee continuously available services to its users, thereby securing its revenue for application instances deployed. In this paper, we focus on available service provisioning when cost-effectively deploying application instances on edge servers. We first formulate a novelAvailability-awareRevenue-effectiveApplicationDeployment (ARAD) problem in the MEC environment with the aim to maximize the overall revenue by considering both service availability benefit and deployment cost. We prove that the ARAD problem is$\mathcal {NP}$-hard. Then, we propose an approximation algorithm namedARAD-Ato find the ARAD solution efficiently with a constant approximation ratio of$\frac{1}{2}$. We extensively evaluate the performance ofARAD-Aagainst five representative approaches. Experimental results demonstrate that ourARAD-Acan achieve the best performance in securing the app vendor's overall revenue.

Edge computing (EC), as an emerging technology, allows app vendors to deliver low-latency services by allocating users to edge servers nearby. Unfortunately, these heterogeneous and resource-constrained edge servers struggle to serve all the co-existing users with ever-growing service requirements, especially during peak periods. Existing approaches for the user allocation problem insisting on fulfilling prescribed service requirements often fail to tackle this issue and consequently result in a tremendous user loss that significantly impairs app vendors' service profit. We observe that a certain degree of violation of users' requirements can help spare resources to serve more users. Meanwhile, such a violation, which is also called service degradation, can be tolerated by users if they receive appropriate compensation. However, abusive degradation may introduce huge compensation, ultimately leading to reduced service profit. As a result, the service degradation and compensation must be carefully considered by app vendors. In this paper, we study theServiceDegradation-ToleratedOnlineUserAllocation (SD-OUA) problem in EC environment, aiming to maximize app vendors' service profit. We prove the$\mathcal {NP}$-hardness of the SD-OUA problem. Then, we propose theDegradation-awareUserAllocation (DUA) approach based on problem reformulation and primal-dual optimization to find its solutions in polynomial time. The performance ofDUAis theoretically guaranteed and evaluated against three representative approaches via extensive experiments.

The popularization and application of 5G technology is about to open the era of global "data explosion". The traditional cloud computing model shows insufficient service support for resource-sensitive applications, especially in terms of latency, and edge computing can solve this problem by providing service support close to the user request side. This article formalizes the single application service deployment problem (SASDP) using the E-CARGO (Environment-Class, Agent, Role, Group, and Object) model. Through group role assignment (GRA), a high-satisfaction service deployment scheme for single application service deployment is designed, and satisfaction evaluation is established through delay to provide providers with satisfactory deployment solutions and achieve economic benefits. Finally, large-scale simulation experiments are carried out based on Python PuLP platform, and experiments show that our method is better than the baseline method in terms of overall satisfaction, user coverage and economy.

Edge computing (EC) provides low-latency services by deploying edge servers close to users. However, these servers are prone to failures that can invalidate any predefined user allocation strategies. To ensure continuous services and maintain users' payments, affected users who are disconnected from the failed edge servers need to be reallocated. Unfortunately, due to the strict latency requirements of users and the limited resources on edge servers, many of them fail to be reallocated. Thus, we propose to migrate unaffected users from affected users' nearby edge servers to free up more resources for reallocation. In this paper, with the aim of maximizing the overall revenue and ensuring continuous service provisioning for users, we formulate the problem of Migration-Assisted _User _Reallocation (MUR) upon edge server failures and prove its NP-hardness. We then introduce an Integer Programming-based approach named MUR-O to find the optimal solution and a heuristic approach named MUR-H to efficiently find sub-optimal solutions. Experimental results on real-world datasets demonstrate that our approaches are superior to three representative approaches.

Efficient bipartite graph matching of orders and drivers is a central operational problem in industrial mobility-on-demand (MOD) systems. Traditional studies adopt pure combinatorial optimization models for order-and-driver matching, which do not consider the long-term rewards of the dynamic MOD decision making process. Toward long-term optimization, this article presents a systemic paradigm of online matching with federated neural temporal difference learning (FedTDLearning), which encompasses learning and matching phases. During the learning phase, the long-term matching process is modeled as a Markov decision process, which is typically solved by employing data-driven reinforcement learning in an offline central training scheme. Massive amounts of data would be generated on the network by industrial MOD systems. It is impossible to send all the large-scale industrial data to the cloud server for centralized model training due to network bandwidth limitations and safety concerns. Therefore, a generic and innovative FedTDLearning is proposed to achieve long-term matching in a distributed manner. During the matching phase, a real-time bipartite matching optimization problem is formulated to maximize the learned spatiotemporal value and minimize the pickup distance, which is reducible to the minimum-cost maximum weight bipartite graph matching problem. A distance-learned-value ratio algorithm is proposed to find an optimal matching in the bipartite graph based on the joint optimization of FedTDLearning and the combinatorial fractional programming approach. Furthermore, to pursue optimal computation efficiency, we solve the real-time matching problem by constructing a$k$-nearest neighbor$(k$NN) bipartite graph where each order is connected with$k$NN drivers. Using openly available real-world data, the prototype system and experimental evaluations show that our proposed algorithms have effective problem-solving capability in practice.

Influence maximization (IM) aims to find a subset of$k$nodes that can maximize the final active node set under an information diffusion model. With the development and popularity of social networks, the IM problem plays an essential role in various applications, such as public opinion analysis, viral marketing, and rumor early warning. However, most of the existing IM solutions have not accessed the risk of negative information from nodes in the future. In reality, a company may face economic loss when negative information breaks out of its spokesman. Therefore, a crisis assessment oriented and topic-based IM problem (TIM-CA) is proposed, which is utilized to model the IM problem by considering the crisis assessment (CA) and topics of users. To solve this problem, we propose a maximum influence arborescence model-based algorithm for TIM-CA, namely, MIA-TIM-CA. The proposed algorithm consists of crisis degree calculation, topic relevance calculation, and influence spread evaluation. More importantly, as for crisis degree calculation, it considers self-, topology-, and topic-based crisis degrees for each node. At the influence spread evaluation stage, MIA-TIM-CA proposes two functions to evaluate the node’s importance and node influence spread. Extensive experiments on two real-world social networks demonstrate that our MIA-TIM-CA outperforms all comparison algorithms on influence spread, crisis score, and running time.

Mobile-edge computing (MEC), as an emerging computing paradigm, allows app vendors to deploy their mobile and/or IoT applications on edge servers to deliver low-latency services to their app users. However, when an edge server needs to serve excessive app users concurrently, severe interference is incurred, which immediately reduces app users’ achievable data rates and, consequently, impacts their perceived service quality. This is a major challenge to the app vendor’s attempt to minimize the edge resources required for serving its app users with a satisfactory service quality. To tackle this challenge, in this article, we present and formulate this multiple edge application deployment (MEAD) problem in the MEC environment, aiming to maximize app users’ overall service quality at minimum deployment cost, considering application shareability and communication interference. We prove that the MEAD problem is$\mathcal {NP}$-hard. Then, we propose a heuristic approach, namely, the deployment-priority greedy via the divide-and-conquer strategy (DPG-D&C), to solve the MEAD problem effectively and efficiently. We evaluate our approach extensively by using a widely used real-world data set. The experimental results show that DPG-D&C significantly outperforms state-of-the-art approaches.

Mobile edge computing (MEC), as an emerging technology, allows application vendors to deploy application instances on edge servers to deliver low-latency services to nearby end-users. However, due to hardware faults, software exceptions, or cyberattacks, edge servers are prone to failures in the highly distributed and dynamic MEC environment. Hence service reliability must be ensured when failures occur. This raises a critical and open problem - improving service reliability when deploying application instances in the MEC environment. In this article, we jointly consider both user coverage and service reliability when deploying application instances on edge servers with a given application deployment budget$\mathcal {K}$. We formally define this jointCoverage-Reliability for$\mathcal {K}$-BudgetedEdgeApplicationDeployment (CR-BEAD) problem and model it as a constrained optimization problem. Next, we propose an optimal approach (namedBEAD-O) based on integer programming to find optimal solutions to small-scale CR-BEAD problems. We also propose a greedy approach namedBEAD-Gwith a constant approximation ratio of$1 - 1/e$to solve large-scale CR-BEAD problems efficiently. Extensive experimental evaluation against three representative approaches illustrates the effectiveness and efficiency of our approaches.

Mobile crowdsensing, a new paradigm, has drawn much attention from the online community, in which mobile users are connected by using smartphones with sharing of information via mobile social networks. Multiple cooperative task allocation (MCTA) is a crucial problem in mobile crowdsensing, where each task requires more than one user to cooperatively complete. As more and more users join sensing tasks in groups, it is indispensable to develop a group-oriented crowdsensing mechanism supporting MCTA. However, existing studies generally focus on a group that can provide sufficient users to accomplish a task. Once these groups no longer exist, the corresponding task will be discarded or be performed with compromised quality. In this article, we propose a novel three-phase approach named Group-oriented Cooperative Crowdsensing (GoCC) to tackle the MCTA problem in social mobile crowdsensing. This approach exploits real-life relationships in the social network to form compatible groups, which improves the task coverage via group-oriented cooperation while achieving good task cooperation quality. Specifically, phase 1 selects a subset of users on the social network as initial leaders and directly pushes sensing tasks to them. Phase 2 utilizes the leaders to search for their socially connected users to model groups. Phase 3 presents the process of group-oriented task allocation for solving the MCTA problem. Experiments on the real-world dataset validate that our approach significantly outperforms the representative approaches.

With the rapid development of mobile devices, mobile social networks have drawn increasing attention from spatial crowdsourcing in which users sharing information via social networking applications can easily identify and participate in multiple cooperative tasks. Existing studies generally assume that all users are trustworthy and can reliably perform assigned tasks. However, such assumptions do not hold in real-world practices. In this article, we consider an essential crowdsourcing problem, namely Reliability-oriented Socially-Aware Crowdsourcing (R-SAC), which improves the reliability by recruiting users who are better matched to the tasks. Our R-SAC problem is to recruit reliable users for multiple cooperative tasks so that the overall reliability of task assignment is maximized. We prove that the R-SAC problem is$\mathcal {NP}$-hard. Then, we propose an approximation algorithm with a factor of$\ln {m} + 1$to solve the R-SAC problem, where$m$is the number of tasks. Specifically, user reliability refers to the probability that a user can reliably perform assigned tasks. To achieve reliable user recruitment during task assignment, we formulate the reliability of a user by combining the matching between the user and tasks, and the reliability feedback from neighbors who share similar behaviors with the user in the social network. Besides, the distributed collaborative filtering technique is utilized to select the reliability feedback from the neighbors. We evaluate the performance of our proposed approach experimentally on two widely-used real-world datasets and the results demonstrate that our approach significantly outperforms five representative approaches.

Most spatial crowdsourcing systems are designed in a static mode with tasks allocated based on the historical interactions data between crowd participants and crowdsourcing tasks. However, these task assignment algorithms usually ignore the long-term feedback on interactive spatial crowdsourcing systems, resulting in performance degradation. Though reinforcement learning naturally fits the problem of maximizing long term crowdsourcing rewards, deep reinforcement learning-based task assignment is still facing the challenge of interactive spatial crowdsourcing. To address these issues, this paper investigates a challenge problem which we study how to intelligently task assignments for interactive spatial crowdsourcing applications. Therefore, we develop an advanced Embedding-based Deterministic Policy Gradient learning framework to maximize long term crowdsourcing rewards for task assignments, called EDPG-Assignment. EDPG-Assignment is based on deep actor critic learning and combines the improvements of two advanced methods, action embedding and neighbor-based deep Deterministic Policy Gradient, and employed this to optimize the task assignment in interactive crowdsourcing. A matrix factorization method to learn spatial crowdsourcing action embedding for neighbor-based discrete actions similarities evaluation in deep actor critic learning-based task assignment from generated crowdsourcing trajectories without any prior knowledge. The EDPG-Assignment algorithm provided a more stable learning process and showed improved results in real-world dataset.

Universal Description, Discovery and Integration (UDDI), as the main technical support of Web service, plays an increasingly important role in SOA (Service Oriented Architecture). Smart contract technology based on blockchain is used to solve the problem of data replication and sharing between UDDI registries. The UDDI registry only stores the information of simple service description and reference to the blockchain, and provides identity management, authority management, and other functions. The detailed information of the service provider and the services it provides is stored in the blockchain. The cost of the UDDI registry is greatly reduced, and the sharing and storage of data can be easily realized. Meanwhile, the security and auditability of data are guaranteed by the characteristics of the blockchain. This paper innovatively introduces the blockchain into the field of service computing, which provides a new idea for future research.