Federated Learning (FL) facilitates privacy-preserving fine-tuning of Large Language Models (LLMs) for mobile applications, termed FedLLM. A vital challenge of FedLLM is the tension between LLM complexity and resource constraint of mobile devices. In response to this challenge, we first introduceFwdLLM(our conference version), an innovative FL framework designed to enhance the FedLLM efficiency. The key idea ofFwdLLMis to employ backpropagation (BP)-free training methods, requiring devices only to execute memory-efficient “perturbed inference”. Enabled by mobile NPU acceleration and an expanded array of participating devices,FwdLLMdelivers substantially better wall-clock efficiency than BP-based FedLLM. However,FwdLLMbased on vanilla BP-free optimization theoretically requires more optimization steps to converge. In this work, we further enhanceFwdLLMtoFwdLLM+, which incorporates advanced zeroth-order optimization techniques and low-rank perturbation decomposition to reduce convergence steps. Finally, we conduct extensive experiments on 4 models (ranging from 110 M to 7B) and 8 more datasets, demonstrating thatFwdLLM+achieves up to 151× faster training, a 93$\%$memory reduction compared to vanilla BP-based FedLLM, and superior performance compared toFwdLLM, enabling efficient federated fine-tuning of billion-parameter LLMs on commodity mobile devices.

The rapid growth of Low Earth Orbit (LEO) satellites, particularly with the increasing deployment of intelligent computing capabilities using commercial off-the-shelf (COTS) hardware, presents significant opportunities to enhance the quality of in-orbit services. However, the current onboard conditions remain insufficient to enhance model accuracy by increasing model size, and inadequate accuracy hampers the effectiveness of in-orbit services. The satellite-ground federated learning (FL) paradigm, leveraging collaborative fine-tuning, offers a promising solution to continuously improve onboard model performance. Prior studies have focused on optimizing fine-tuning under constraints like limited bandwidth and computational resources, they often overlook two critical challenges: the scarcity and skewness of labeled onboard data and the long revisit cycles of satellites. To address these challenges and better support in-orbit services, this paper designs a realistic simulation methodology for the onboard fine-tuning process and conducts a comprehensive measurement study. Based on insights from the measurement results, we propose an efficient satellite-ground federated fine-tuning system,FedCLR+. In this system, we design a FedCLR algorithm to enhance system accuracy through representation optimization. Additionally, we propose a hybrid bias-compensated strategy to further mitigate accuracy loss by enriching the diversity of aggregation information. Experimental results show thatFedCLR+significantly enhances accuracy by up to 21.61×, reduces transmission volume by an average of 7.29%, and maintaining acceptable additional overhead compared to baselines.

Satellite computing is emerging as a promising domain for delivering mobile services that meet stringent Quality of Service (QoS) requirements, such as low latency, to users. However, the inherent mobility of satellites as computing nodes can precipitate QoS degradation, a challenge not encountered in terrestrial cloud systems. This discrepancy poses significant adaptation challenges for cloud service orchestration systems, such as Kubernetes, due to the rapid movement of satellites. This poster introduces a service orchestration system and a corresponding service placement strategy tailored for satellite computing environments. Our proposed architecture and strategy surpass traditional fixed instance deployment by not only achieving lower average latency but also maintaining an optimal balance between benefits and costs.

Age of Information (AoI) has gained widespread usage and emerged as a pivotal metric for assessing timeliness performance in information-update systems. Such systems often entail service requirements for rapidly obtaining requested data in real-time. For instance, in the financial market, users rely on up-to-date and low-latency information to make appropriate trading decisions to maximize profits within their financial budge. Much of the existing research on real-time services focuses on ensuring AoI or service-level latency, but there is a growing demand for joint optimization of these two metrics to accommodate a broader range of potential applications. Therefore, this article investigates the problem of minimizing AoI within the context of statistical latency guarantees. To tackle the critical challenges posed by the joint modeling of AoI and statistical latency, system uncertainty, as well as tradeoff between performance and user’s budget, we employ a replication scheme to ensure both AoI and statistical service-level latency. To address the critical challenges posed by the unknown distribution in data updating processes and response times across providers, we formulate the AoI minimization problem with statistical latency constraints as a combinatorial multiarmed bandit problem utilizing the Lyapunov optimization theory. Subsequently, we propose an online learning-based request replication algorithm to address this problem. Our proposed algorithm achieves a cumulative regret of$O(T\sqrt {\log (T)})$compared to the genie-aided algorithm. Simulation results demonstrate the superior performance of the proposed algorithm against benchmarks.

Large constellations of Low Earth Orbit (LEO) satellites have been launched for Earth observation and satellite-ground communication, which collect massive imagery and sensor data. These data can enhance the AI capabilities of satellites to address global challenges such as real-time disaster navigation and mitigation. Prior studies proposed leveraging federated learning (FL) across satellite-ground to collaboratively train a share machine learning (ML) model in a privacy-preserving mechanism. However, they mostly focus on single unique challenges such as limited ground-to-satellite bandwidth, short connection window, and long connection cycle, while ignoring the completeness of these challenges in deploying efficient FL frameworks in space. In this paper, we propose an efficient satellite-ground FL framework, SatelliteFL, to address these three challenges collectively. Its key idea is to ensure that each satellite must complete per-round training within each connection window. Moreover, we design a progressive block-wise quantization algorithm that determines a unique bitwidth for each block of the ML model to maximize the model utility while not exceeding the connection window. We evaluate SatelliteFL by plugging an implemented FL platform into real-world satellite networks and satellite images. The results show that SatelliteFL highly accelerates the convergence by up to 2.8× and improves the bandwidth utilization ratio by up to 9.3× compared to the state-of-the-art methods.

The rapid development of Low Earth Orbit (LEO) satellite constellations offers significant potential for in-orbit services, particularly in mitigating the impact of sudden natural disasters. However, the massive data collected by these satellites are often large and severely constrained by limited transmission capabilities when sending data to the ground. Satellite computing, which utilizes onboard computational capacity to process data before transmission, presents a promising solution to alleviate the downlink burden. Nonetheless, this paradigm introduces another bottleneck: limited onboard computing capacity, resulting in slow in-orbit processing and poor results. Current satellite computing systems struggle to efficiently address both data transmission and computing bottlenecks, particularly for urgent disaster services that demand accurate and timely results. Thus, we introduce an efficient satellite computing system designed to jointly mitigate these bottlenecks, thereby providing better service. The core idea is to utilize onboard computing capacity for swift in-orbit annotation of image regions, enabling adaptive compression and download based on annotation confidence and perceived downlink availability. Once the data is downloaded, image restoration and re-inference are performed on the ground to enhance accuracy. Compared to satellite-only inference, our system demonstrates an average improvement in inference accuracy of 3.8%. Furthermore, compared to ground-only inference, with only a 2.8% accuracy loss, our system achieves a 38.4% reduction in response time and saves 71.6% of downlink volume on average.

Space networks composed of Low Earth Orbit (LEO) satellites play a significant role in Earth observation systems. LEO satellite constellations, which provide wide-ranging coverage, continuously observe the Earth and transmit the observed data to satellites in higher orbits or ground stations for further analysis, benefiting from their more powerful computation capacity. In such an observation and analysis network, the timeliness of data update and the quality of analysis are crucial factors, both constrained by limited energy resources. In this paper, we jointly optimize the timeliness and analysis quality by constructing an update scheduling problem with a trade-off between Age of Information (AoI) and accuracy. Considering the unpredictable and changeable environment dynamics, we formulate this problem into a Markov decision process and propose an algorithm based on deep reinforcement learning to obtain an optimal policy that effectively tackles the tradeoff problem. Simulation results demonstrate that our algorithm can jointly reduce AoI and increase accuracy, outperforming benchmark algorithms in different cases.

The rapid expansion of low-earth orbit satellite constellations poses a significant challenge for the operation and maintenance of thousands of satellites. In-orbit fault diagnosis helps minimize the cost of repairs, and ensure the overall reliability of the satellite constellation by identifying faults in their early stages. Compared to traditional fault detection methods, fault diagnosis requires knowledge to enhance very limited data and to infer cascading failures. In this paper, we propose a novel knowledge service framework that combines data-driven and knowledge-driven models to improve the efficiency and effectiveness of fault diagnosis. We implement a fault diagnosis service based on a constructed knowledge graph, which affords an assistant decision support function. The in-orbit deployment and simulation experiment verify the feasibility of the proposed framework, which shows great potential for fault diagnosis of low-earth orbit satellite constellation.

Federated learning (FL) has emerged as a prominent paradigm enabling collaborative model training without transmitting local data, thereby safeguarding data privacy. However, the practical implementation of FL systems on these devices faces a significant challenge: the heterogeneous corruption of data on individual clients, leading to unanticipated accuracy degradation during real-world deployment. In this work, we first introduce a realistic data corruption simulation framework to test the robustness of FL systems. In this framework, an in-depth analysis of potential data corruption patterns occurring on devices is conducted, followed by the construction of individual datasets with varying corruption types and degrees. Such data corruption results in the robustness degradation of conventional FL protocol (FedAVG) significantly higher than centralized learning (CL). Atop this key observation, we propose an adaptive FL protocol that emulates the CL training process. The protocol leverages imbalanced client data sampling to mitigate the negative impact of data corruption. Furthermore, a hybrid aggregation strategy is designed to accelerate model convergence and reduce additional communication overhead. Extensive experiments validate the effectiveness of our approach in enhancing the robustness of FL systems against client data corruption, which achieves up to 12% higher converge accuracy than FedAVG-based systems with acceptable overhead.

Researchers and practitioners are exploiting Remote Direct Memory Access (RDMA) technology to improve the efficiency of distributed machine learning and meet the demands of data-center applications. RDMA requires lossless network link to fully unleash its power. RDMA Over Converged Ethernet (RoCE) v2 focuses on congestion control, but fails to achieve efficient packet loss recovery; Improved RoCE NIC (IRN) addresses this issue based on RoCEv2, but does not use the Priority-based Flow Control (PFC) to maintain the advantage of RoCEv2 in detecting congestion. This paper proposes a method of congestion detection and link control via feedback in RDMA transmission, namely Feedback Data Flow Control (FDFC), that does not rely on PFC. FDFC detects and controls the link condition in real time to achieve the goals of precise detection, congestion control, and efficient packet loss recovering.

Recently, increasing investments in satellite-related technologies make the low earth orbit (LEO) satellite constellation a strong complement to terrestrial networks. To mitigate the limitations of the traditional satellite constellation “bent-pipe” architecture, satellite edge computing (SEC) has been proposed by placing computing resources at the LEO satellite constellation. Most existing works focus on space-air-ground integrated network architecture and SEC computing framework. Beyond these works, we are the first to investigate how to efficiently deploy services on the SEC nodes to realize robustness aware service coverage with constrained resources. Facing the challenges of spatial-temporal system dynamics and service coverage-robustness conflict, we propose a novel online service placement algorithm with a theoretical performance guarantee by leveraging Lyapunov optimization and Gibbs sampling. Extensive simulation results show that our algorithm can improve the service coverage by$4.3\times $compared with the baseline.

The emerging 6G network will make it possible for cybertwin, which relies deeply on the low latency and powerful computation provided by the edge network. To this end, the convergence of computing and network has been attached great importance. Most existing work study either placing edge servers or deploying user plane functions (UPFs), seldom considers the two processes jointly. In this article, we study how to minimize the latency with cost limitation by means of jointly deploying edge servers and UPFs in 6G scenario. We have shown that the problem is NP-hard. Then, we simplify the problem by analyzing the placement relationship between edge servers and UPFs and prune the solution space of the problem. To solve the problem effectively, a UPF and edge server placement algorithm is proposed. Massive experiments are conducted based on real-world data set and an edge core network emulator. The evaluation results show that our algorithm outperforms the benchmark algorithms.

Mobile-edge computing is a promising computing paradigm with the advantages of reduced delay and relieved outsourcing traffic to the core network. In mobile-edge computing, reducing the computation offloading cost of mobile users and maintaining fresh information at edge nodes are two critical while conflicted objectives, as both consume the limited wireless bandwidth of edge nodes. Although extensive efforts have been devoted to optimizing computation offloading decisions and some works have investigated freshness-aware channel allocation issues recently, no prior works have considered the above conflict. This article is the first work to jointly optimize the channel allocation and computation offloading decisions, aiming at reducing the computation offloading cost within freshness requirements of sensors. We analyze the recursiveness of Age of Information (AoI) in analogy to the evolvement of a queue and formulate the problem as a nonlinear integer dynamic optimization problem. To overcome the challenges of AoI-computation cost tradeoff, AoI time dependency and high complexity caused by the heterogeneity of users, we propose an algorithm to solve the problem with reduced computation complexity. Specifically, we first transform the original problem into a static optimization problem in each time slot (which is NP-hard) based on Lyapunov optimization techniques. To reduce the computation complexity, we exploit the finite improvement property of potential games and further enforce centralized control to reduce the number of improvement iterations. Simulations have been conducted and the results demonstrate that the proposed algorithm shows good effectiveness and scalability.

Summary In recent decades, newly emerging mobile applications and services are becoming increasingly resource hungry and computation intensive. Portable size mobile devices fall short in providing such services. Cloud computing has been a core computation technology to provide visualized resources in a scalable way for mobile services. However, the unpredictable network transmission latency makes cloud computing not efficient enough for time‐sensitive mobile services, which requires major changes in underlying computing platform. Mobile edge computing is widely known as one of the novel technologies has emerged in recent years to address this issue. For being impractical to construct huge edge cloud in network edge, edge clouds can still be overloaded in rush time. Offloading to near‐user facilities using device‐to‐device (D2D) or other technologies becomes an augmentation approach. However, how to manage the facilities/resources effectively become a new issue. In this paper, we proposed a resource management scheme named BCEdge based on blockchain in D2D‐assisted mobile edge computing. BCEdge is a reliable scheme that operates in a distributed way to relieve the load of edge clouds. We illustrate the advantages and technical details of BCEdge using flow charts and interaction charts. The experiment results validate the effectiveness of our scheme. Finally, we discuss the possible future extensions.

5G communication is making much progress in achieving the Internet of Things and improving the quality of user experience in large bandwidth scenarios. By introducing a variety of new technologies, the performance of 5G has been greatly improved. However, emerging applications put forward more stringent requirements in terms of latency, reliability, peak data rate, service continuity, etc. Communication technology still needs to be further developed. In this article, the next generation of core networks is conceptualized. Inspired by the nervous system of the octopus, we propose a new cognitive service architecture. Cognitive service architecture is a new architecture designed for the 6G core network. It is proposed to enhance the core network so that it is qualified for the increasingly high requirement for quality of service and complicated scenarios. We first give a short vision of the 6G core network. Then cognitive service architecture is demonstrated in detail. A case study is demonstrated to show how cognitive service architecture enhances the performance of the system. Enabling technologies for 6G cognitive service architecture are discussed at last.

With the development of mobile Internet, mobile service is emerging one after another, and the problem of information overload is becoming ever more serious. As an important tool to alleviate information overload, mobile service recommendation has attracted more and more attention. However, traditional recommendation algorithms always recommend popular services to users, which result into a rich-get-richer problem and become a barrier for the unpopular services to startup and growth. In order to promote the healthy development of the service ecosystem, it is necessary to guarantee the fairness of unpopular services. To address this problem, this paper proposes a fairness-aware mobile service recommendation method (FMSR), which gives a relatively fair recommendation opportunity for unpopular services. FMSR makes a tradeoff between recommendation accuracy and fairness, and can recommend popular services and unpopular services respectively. For unpopular services, we design a fair efficiency function and use combinatorial optimization techniques to achieve recommendations. For popular services, bias matrix factorization is utilized to implement recommendations. Experimental results based on real-world demonstrate that FMSR significantly improve the fairness of mobile service recommendation in the evolving mobile service ecosystem.

In order to build highly reliable composite service via Service Oriented Architecture (SOA) in the Mobile Fog Computing environment, various fault tolerance strategies have been widely studied and got notable achievements. In this paper, we provide a comprehensive overview of key fault tolerance strategies. Firstly, fault tolerance strategies are categorized into static and dynamic fault tolerance according to the phase of their adoption. Secondly, we review various static fault tolerance strategies. Then, dynamic fault tolerance implementation mechanisms are analyzed. Finally, main challenges confronted by fault tolerance for composite service are reviewed.

The widespread utilization of cloud computing services has brought in the emergence of cloud service reliability as an important issue for both cloud providers and users. To enhance cloud service reliability and reduce the subsequent losses, the future status of virtual machines should be monitored in real time and predicted before they crash. However, most existing methods ignore the following two characteristics of actual cloud environment, and will result in bad performance of status prediction: 1. cloud environment is dynamically changing, 2. cloud environment consists of many heterogeneous physical and virtual machines. In this paper, we investigate the predictive power of collected data from cloud environment, and propose a simple yet general machine learning model StaP to predict multiple machine status. We introduce the motivation, the model development and optimization of the proposed StaP. The experimental results validated the effectiveness of the proposed StaP.

Given a service composition, it is challenging but important to have a runtime adaptation, due to the complicated execution environment and evolving feature of Web service. In this paper, we present a runtime adaptive service composition approach, taking execution time minimization and revenue maximization into consideration. Based on dynamic programming, we deduce the optimal policy. Through this policy, orchestrator selects one concrete service for per task on runtime. The experimental results show that the proposed approach outperforms previous approach.

Quality of service (QoS) guarantee is an important component of service recommendation. Generally, some QoS values of a service are unknown to its users who has never invoked it before, and therefore the accurate prediction of unknown QoS values is significant for the successful deployment of web service-based applications. Collaborative filtering is an important method for predicting missing values, and has thus been widely adopted in the prediction of unknown QoS values. However, collaborative filtering originated from the processing of subjective data, such as movie scores. The QoS data of web services are usually objective, meaning that existing collaborative filtering-based approaches are not always applicable for unknown QoS values. Based on real world web service QoS data and a number of experiments, in this paper, we determine some important characteristics of objective QoS datasets that have never been found before. We propose a prediction algorithm to realize these characteristics, allowing the unknown QoS values to be predicted accurately. Experimental results show that the proposed algorithm predicts unknown web service QoS values more accurately than other existing approaches.

Many factors affect the time cost of Cloud computing tasks. One of the most serious factors is data transmission latency, which reduces the efficiency of Cloud computing. Existing notable schemes ignore the communication cost among virtual machines (VMs) in the MapReduce environment. In this paper, we propose a VM placement approach to reduce data transmission latency with the communication cost among VMs. We first construct bipartite graph and classify VMs as two groups according to their transmission latency with data nodes. Then we propose two VM placement optimization algorithms to minimize the total data transmission latency (TDTL) and the maximum data transmission latency (MDTL) in the MapReduce environment. Finally, we place VMs for Reduce phase. The evaluation results show that our approach reduces the average data transmission latency by 26.3% compared with other approaches.

How to select the optimal composited service from a set of functionally equivalent services but different QoS attributes has become a hot research in service computing. However existing approaches are inefficient as they search all solution spaces. More importantly, they neglect the QoS inherently uncertainty due to the dynamic network environment. In this paper, we propose a fast and reliable Web service selection approach that attempts to select the best reliable composited service on the basis of filtering low reliable Web services according to the uncertainty of QoS. The approach first employs information theory and variance theory to abandon high QoS uncertainty services and downsize the solution spaces. A reliability fitness function is then designed to select the best reliable service for composited services. We experimented with real-world and synthetic datasets and compared our approach with other approaches. Our results show that our approach is not only fast, but also find more reliable composited services.

As cloud computing becomes widely deployed, more and more cloud services are offered to end users in a pay-as-you-go manner. Today’s increasing number of end user-oriented cloud services are generally operated by Software as a Service (SaaS) providers using rental virtual resources from third-party infrastructure vendors. As far as SaaS providers are concerned, how to process the dynamic user service requests more cost-effectively without any SLA violation is an intractable problem. To deal with this challenge, we first establish a cloud service request model with SLA constraints, and then present a cost-aware service request scheduling approach based on genetic algorithm. According to the personalized features of user requests and the current system load, our approach can not only lease and reuse virtual resources on demand to achieve optimal scheduling of dynamic cloud service requests in reasonable time, but can also minimize the rental cost of the overall infrastructure for maximizing SaaS providers ’ profits while meeting SLA constraints. The comparison of simulation experiments indicates that our proposed approach outperforms other revenue-aware algorithms in terms of virtual resource utilization, rate of return on investment and operation profit and provides a cost-effective solution for service request scheduling in cloud computing environments.

To maximize the profit of cloud service provider, we present a dynamic virtual resource renting method under SLA (service level agreement) constraints. According to the price distribution and current task emergency, the method attempts to adjust acceptable price of each virtual resource type at different price interval. If there is price acceptable resource, we choose to rent the most profitable one. Otherwise, if SLA permits, we even suspend the task and restart it when price falls. Partial experimental result in simulation environment is also presented.

A critical research issue is to lower the energy consumption of a virtualized data center by means of virtual machine placement optimization while satisfying the resource requirements of the cloud services. In this paper, we focus on different existing schemes and on the energy-aware virtual machine placement optimization problem of a heterogeneous virtualized data center. We attempt to explore a better alternative approach to minimizing the energy consumption, and we observe that particle swarm optimization (PSO) has considerable potential. However, the PSO must be improved to solve an optimization problem. The improvement includes redefining the parameters and operators of the PSO, adopting an energy-aware local fitness first strategy and designing a novel coding scheme. Using the improved PSO, an optimal virtual machine replacement scheme with the lowest energy consumption can be found. Experimental results indicate that our approach significantly outperforms other approaches, and can lessen 13%-23% energy consumption in the context of this paper.

With the rapid development of Cloud computing, more and more service providers could provide cloud services (applications) to users. Faced with mass Cloud services, trust and reputation mechanisms offer a promising way to solve the trust evaluation of Cloud services. Hence, trust and reputation play an important role in evaluating of Cloud services. In this paper, we propose a lightweight reputation measurement approach for Cloud services based on (user) feedback ratings. The proposed approach first adopts cloud model to obtain the trust vector of each cloud service by exploiting feedback ratings. The trust vector consists of Expected value, Entropy value and Hyper-Entropy value. Then we use fuzzy set theory to calculate the reputation scores of Cloud services. Simulation results show that the proposed approach is significantly effective for unstable feedback ratings.

With an increasing number of cloud service providers (CSP) delivering services to customers from the cloud, maximizing the profits of CSPs becomes a critical problem. Existing methods are difficult to solve the problem because they do not make full use of temporal price differences. This paper introduces a dynamic virtual resource renting method that attempts to dynamically adjust the virtual resource rental strategy according to price distribution and task urgency. We first pretreat the historical price series and adopt the outlier detection technique to filter the extreme price. Then, considering task urgency and price distribution, we design a weak equilibrium operator to calculate the acceptable price for each type of virtual resource. All types of virtual resources that are at an acceptable price are inserted into a set. Finally, we design a novel rental decision-making algorithm to select the most profitable resource from the set. We provide an extensive evaluation of our method using Amazon EC2 spot price dataset and normally distributed price dataset. The results demonstrate the effectiveness of our method.

ABSTRACT SYN flooding attacks are a common type of distributed denial‐of‐service attacks. Up to now, many defense schemes have been proposed against SYN flooding attacks. Traditional defense schemes rely on passively sniffing an attacking signature and are inaccurate in the early stages of an attack. These schemes are effective only at the later stages when attacking signatures are obvious. In this paper, we propose a detection approach that makes use of SYN traffic prediction to determine whether SYN flooding attacks happen at the early stage. We firstly adopt grey prediction model to predict SYN traffic, and then, we employ cumulative sum algorithm to detect SYN flooding attack traffic among forecasted SYN traffic. Trace‐driven simulation results demonstrate that our proposed detection approach can detect SYN flooding attacks effectively. Copyright © 2012 John Wiley & Sons, Ltd.

ABSTRACT Trusted quality of Web service (QoWS) issue is critical for e‐commerce applications. However, many existing studies have little work in situations that have insufficient or no historical information regarding QoWS data. In this study, we propose a trusted QoWS metric approach, that is, Bayesian Approach with Maximum Entropy Principle. The key of our proposed approach is to extract QoWS prior distribution of Web service by using Maximum Entropy Principle and then to infer QoWS posterior distribution of Web service by using Bayesian Approach. On the basis of the obtained QoWS posterior distribution, trusted QoWS can be measured. We conduct extensive simulations to evaluate our proposed approach. The simulation results demonstrate that our proposed approach can obtain trusted QoWS effectively. Copyright © 2012 John Wiley & Sons, Ltd.

In order to obtain trustworthy QoS (Quality of Service) in situations which have little or no information regarding a service's QoS, we propose a QoS measure approach, namely Bayesian Approach with Maximum-Entropy Principle (BA-MEP). BA-MEP firstly extracts the QoS prior distribution from the objective data (such as historical statistics data) and subjective data (such as the service providers and QoS experts) by Maximum Entropy Principle, and then Bayesian Approach is used to infer the QoS posterior distribution, finally, trustworthy QoS can be obtained from the QoS model. In addition, we also propose a trustworthy expert algorithm (TEA) and analysis three reasons that the QoS data of Web services are not always true. Some experiments are illustrated to show the effectiveness of BA-MEP.

This paper compares disease with network vulnerability by their definitions and characteristics. A mapping between disease and vulnerability is built based on their similarities. We put forward a novel model of vulnerabilities in computer networks by simulating the reverse of cause-result of disease. Based on the model, a quantitative metric for vulnerabilities of computer networks is presented. The complexity of the algorithm for computing the metric is O(|V|2X|S|), where V and S stand for set of vulnerabilities and set of network states. By analyzing different structures of the vulnerability model, we found that the value reflecting vulnerability decreases when the model is more linear.