The data that records road traffic accidents often hides important information. If we can analyze the main factors that affect the severity of the accident in the data, it has very good practical significance for avoiding the occurrence of major traffic accidents. In this paper, a method called CFS-BAS-BP is proposed to realize the identification of potential traffic accident risk factors based on combinatorial feature selection and bionic neural network. This method combines the advantages of Information Gain, Chi-square test and Random Forest in feature selection to screen the important features affecting traffic accidents to the greatest extent, and filter out irrelevant and redundant features in the data. As a result, 14 features that influence the occurrence and severity of traffic accidents are selected from 25 features in the dataset At the same time, the back propagation neural network optimized by the beetle antennae search algorithm is used to analyze the quantitative relationship between traffic data features and traffic accident risk level, so as to identify the main risk factors affecting the severity of traffic accidents in different scenarios. Through a large number of experiments on the real traffic accident data set STATS19, we prove that CFS-BAS-BP model has a significant effect on the selection of accident-related features, and its accuracy rate of identifying the risk factors affecting the severity of traffic accidents reaches 86.1%.

The emergence of cloud and edge computing has solidly established the Cloud-Edge collaborative architecture as an essential technology, enabling the deployment of large-scale distributed applications. However, despite the benefits conferred by Cloud-Edge collaborative systems, challenges arise from prolonged operation, rapid evolution, and resource constraints. These challenges may accelerate software aging, resulting in performance degradation, functional disruptions, and increased susceptibility to errors. Software rejuvenation acts as a preemptive and proactive maintenance strategy designed to alleviate the consequences of software aging. Although task migration is a commonly used method for software rejuvenation, addressing software aging in complex Cloud-Edge collaborative systems requires dynamic task migration to adapt to environmental changes. Therefore, to develop a task migration-based rejuvenation strategy with lower latency, energy consumption, and software rejuvenation cost, this paper introduces a method for generating software rejuvenation strategies based on Multi-Agent Deep Reinforcement Learning for Cloud-Edge collaboration systems, named GRCEM. Initially, the environment is modeled to define the state space, action space, and training rewards, meticulously designed to minimize rejuvenation costs for the Cloud-Edge collaborative system. Subsequently, a distributed execution algorithm for deep reinforcement learning is devised, treating each node as an autonomous agent responsible for acquiring knowledge and enhancing rejuvenation strategies through continuous interaction with the system environment. Finally, the rejuvenation strategy is formulated, characterized by its optimal rejuvenation cost. The experimental results demonstrate that the GRCEM method accomplishes software rejuvenation with reduced rejuvenation costs compared to the baseline method. Furthermore, the system’s performance transitions from the aging state to the robust state after rejuvenation, thereby significantly enhancing the availability and reliability of the Cloud-Edge collaboration system.

Deep neural networks have demonstrated vulnerabilities to black-box adversarial patch attacks in image analysis tasks, raising concerns about their robustness in safety-critical applications. Current methods typically rely on randomized search strategies to determine patch locations and apply unrestricted pixel perturbations in the patch area, leading to high query costs and significant visual distortions that reduce imperceptibility. To address these limitations, we propose Black-Box Patch Attacks via Diffusion-Based Double Gradient and Sensitive Distribution Guidance (DiGradPatch), a novel method designed to generate highly imperceptible adversarial samples with minimal query cost. Our approach leverages the prior sensitive distribution of a surrogate model to efficiently locate the patch by maximizing the joint probability distribution between the global model prediction and selected image regions. Furthermore, we introduce the diffusion model into the black-box patch attack framework for the first time. The prior gradient from the diffusion model, in conjunction with the estimated gradient of the target model, is used to guide the direction of perturbations. This allows the generation of adversarial perturbations within the patch area that align with the distribution of natural samples, effectively reducing perceptual distortions. Extensive experiments on the ImageNet dataset show that DiGradPatch achieves superior imperceptibility with significantly reduced query costs, maintaining a high attack success rate. Compared to existing methods, our approach requires fewer than 10 queries and achieves an L∞norm that is only 1/10 of state-of-the-art techniques, while maintaining the highest attack success rate.

As a platform for creating, deploying, and managing containers, Docker has long been tasked with handling high workloads, making it highly susceptible to aging-related bugs. As these bugs accumulate, the system may exhibit anomalies such as increased resource utilization, task scheduling failures, and response time delays. At this juncture, the system is subject to software aging. If left unresolved, this problem may escalate to more severe consequences such as system crashes and downtime, significantly diminishing the availability and reliability of the system. In order to address the software aging and restore system performance, it has become an urgent problem to accurately determine the aging state of the Docker platform and generate targeted rejuvenation operations reasonably and effectively. Therefore, this paper proposes a synthesis method for determining the aging state and generating rejuvenation operations, named FOQL. Firstly, the FS-OWA algorithm is employed to analyze resource usage according to the varying degrees of aging states, accurately determining whether the system is in an aging state. Secondly, if the system enters an aging state, the Q- Learning algorithm evaluates the value of each rejuvenation operation based on the degree of aging and the cost of rejuvenation operations (such as downtime), ultimately generating the optimal operation. Finally, the experimental results show that, in determining the aging state, the recognition accuracy of the FS-OWA algorithm reached 99.3%, surpassing baseline algorithms by up to 16.52 %. In generating rejuvenation operations, Q-learning algorithm generates a Q-table containing the value of each state-action pair. Based on this table, the optimal rejuvenation operation can be selected for execution. In conclusion, the utilization of the FOQL method effectively mitigates the aging problem and ensures the service quality of the system.

The Cloud-Edge collaborative system, which combines the advantages of cloud computing and edge computing, has become the preferred architecture for large-scale distributed systems. However, prolonged high-load operation of Cloud-Edge collaborative system may result in software aging, significantly impacting the reliability of the Cloud-Edge collaborative system, especially in resource-constrained edge environments. Proactive rejuvenation can help restore system robustness, but it comes with costs and affects normal system operation. Determining the appropriate rejuvenation timing and implementing an efficient rejuvenation strategy are crucial. Since the reliability of the edge system is closely related to the stable operation of the entire Cloud-Edge collaborative system, this paper proposes a comprehensive rejuvenation model named SM-OLR. This model calculates the rejuvenation time for the edge system and performs the rejuvenation operation, which is divided into two stages. The first stage involves determining the rejuvenation timing. A Semi-Markov model is used to represent the system state. Specific distribution functions are fitted based on measured values to accurately describe the system's state transitions. This enables a more scientific modeling of the system's state and ensures precise calculation of rejuvenation timing. The second stage focuses on the rejuvenation strategy. In the Cloud-Edge collaborative environment, interactions between edge and cloud, and edge and edge are easily facilitated, making task offloading a highly effective rejuvenation method. The paper adopts task offloading as the rejuvenation strategy. The reinforcement learning algorithm SARSA is employed to dynamically decide the offloading decision for specific tasks. Experiments were conducted in KubeEdge, a representative Cloud-Edge system. The results demonstrate that SM-OLR incurs lower rejuvenation overhead compared to traditional reboot rejuvenation, and the edge system can continue to provide services during the rejuvenation process. By performing effective rejuvenation operations, the performance of the edge system can be improved by 57 %.

As the adoption of Deep Neural Networks (DNNs) continues to expand in various AI application fields, the concerns about their security and robustness increase. Adversarial samples can test the security and robustness of DNNs. The existing adversarial samples with low transferability and therefore cannot effectively test unknown DNNs. To improve transferability, some adversarial attacks have employed attention mechanisms to obtain and modify the feature information. Though the single attention mechanism has demonstrated good performance, it may fail to obtain complete and accurate feature information. Employing stack attention mechanisms is crucial in obtaining fully complete and accurate feature information, resulting in further enhancement transferability. This paper proposes a novel adversarial attack method, which implements stacked layer-wise relevance propagation technologies, called ASL. Specifically, the ASL implements stacked attention mechanisms at the pixel level to comprehensively explore feature information. The comprehensive feature information is used as the loss function to generate the adversarial perturbation, which is minimized to create adversarial samples. Through extensive experiments on ImageNet, we demonstrate that ASL improves transferability and generates efficiently on victim models owing to its ability to obtain and modify comprehensive feature information.

Deep learning models have achieved unprecedented success in various visual tasks. However, they are highly susceptible to adversarial perturbations that are imperceptible to humans. The presence of this risk significantly hinders the deployment of deep learning models in real-world scenarios, especially in safety-critical contexts. Input reconstruction has emerged as an effective defense mechanism against adversarial examples, capable of countering both known and unknown adversarial attacks. Nonetheless, methods based on input reconstruction often suffer from slow training speeds and unsatisfactory reconstruction quality, limiting their practical applicability.To address these issues, we propose a region localization-guided input reconstruction defense method, named RPG-Diff. This approach utilizes image segmentation algorithms to locate key regions, which then guide the reconstruction process of the diffusion model. By leveraging information from these key regions, the diffusion model can more effectively eliminate adversarial perturbations while maintaining consistency between the reconstructed output and the original input. This significantly reduces the impact of adversarial perturbations on model accuracy. Experimental results demonstrate that, compared to existing methods, our approach exhibits superior defensive performance against various adversarial attacks and surpasses other preprocessing-based defense methods in terms of robust accuracy and average generation time.

Log anomaly detection is a fairly indispensable log analysis task for reliability and maintainability in cloud data center. By performing tasks such as log parsing and feature extraction on logs, which are common and valid data, a model with self-judgment capability can be trained for log anomaly detection. Improving the model used for anomaly detection is the main line of research in the current anomaly detection field. However, the data set partitioning method during anomaly detection also has an important impact on the results of anomaly detection, which should be given more considerations. Most of the existing anomaly detection models are single-architecture models, which cannot make full use of the multiple forms of information that logs have. This paper proposes a hybrid anomaly detection method, named LogKT, which is divided into two parts. First, a new dataset partitioning method is constructed based on time-series, randomness and imbalances of logs. It is a dataset partitioning method that fits the characteristics of log anomaly detection from the aspects of time-series feature preservation, sampling range expansion and training method change. Then, we further propose a hybrid anomaly detection model based on a Transformer and Bi-LSTM models, which can extract features from multiple information of logs and can fit well with the dataset partitioning method. Finally, we perform validation experiments on two public datasets, and the experimental results show that our LogKT approach has superior anomaly detection accuracy compared with baseline methods.

Accurate PM2.5 concentration forecasting service is essential for reducing the impacts of this air pollution. In this paper, we propose a novel PM2.5 pollution prediction service to achieve accurate PM2.5 forecasting effectiveness, which well integrates MLSTM (Mogrifier Long Short-Term Memory) prediction model and feature attention mechanism. Specifically, the MLSTM model captures long-term dependencies in PM2.5-related serial data through richer contextual interactions, while the feature selection module uses the attention mechanism to increase the overall prediction accuracy by allocating weights to each feature. We design representative experiments and the results show that compared to quintessential baseline approaches, our PM2.5 pollution prediction service achieves superior prediction accuracy.

Deep neural networks (DNNs) are susceptible to adversarial examples that arise due to adding small-magnitude perturbations to the input. Such adversarial examples can mislead DNNs to produce adversarially selected results. Different attack strategies have been proposed to generate adversarial examples, but more research is needed on effectively generating adversarial examples with high perceptual quality. This paper proposes an improved generative adversarial network, LpAdvGAN, to generate adversarial examples using generative adversarial networks (GANs). This algorithm learns and approximates the distribution of the original examples. For LpAdvGAN, the noise generator uses a differential evolution algorithm to generate high-quality noise. After training with relatively average GANs (RaGANs), it reduces the probability that real samples are recognized as real. Adversarial samples generated by LpAdvGAN perform well in terms of perceived similarity and have a higher attack success rate than the current mainstream attack strategies. Specifically, the success rate of LpAdvGAN is 9.88% and 8.7% higher than that of AdvGAN under the semi-white-box settings of MNIST and CIFAR 10, respectively.

With the development of artificial intelligence, the need for data sharing is becoming more and more urgent. However, the existing data sharing methods can no longer fully meet the data sharing needs. Privacy breaches, lack of motivation and mutual distrust have become obstacles to data sharing. We design a privacy-preserving, decentralized data sharing method based on blockchain smart contracts, named PPDS. To protect data privacy, we transform the data sharing problem into a model sharing problem. This means that the data owner does not need to directly share the raw data, but the AI model trained with such data. The data requester and the data owner interact on the blockchain through a smart contract. The data owner trains the model with local data according to the requester's requirements. To fairly assess model quality, we set up several model evaluators to assess the validity of the model through voting. After the model is verified, the data owner who trained the model will receive reward in return through a smart contract. The sharing of the model avoids direct exposure of the raw data, and the reasonable incentive provides a motivation for the data owner to share the data. We describe the design and workflow of our PPDS, and analyze the security using formal verification technology, that is, we use Coloured Petri Nets (CPN) to build a formal model for our approach, proving its security through simulation execution and model checking. Finally, we demonstrate effectiveness of PPDS by developing a prototype with its corresponding case application.

Android systems are prone to software aging due to the accumulation of numerical errors and storage-related bugs during long-term operation, resulting in gradual performance degradation and sudden system hang-ups. Thus, it is very critical to accurately identify the aging state for improving the running reliability of Android systems. In this paper, we propose a novel software aging state identification method, named EWDLL. It first introduces the exponential Weibull distribution to simulate the aging state transfer process of the Android system, then it uses Fuzzy Analytical Hierarchy Process (FAHP) to weight the model parameters and resource utilization parameters. Finally, the weighted dataset is fed into the LightGBM-LR model to identify the software state. The experimental results show that our EWDLL method performs better in identifying the software aging state for Android system, i.e., it is 0.86% to 1.09% higher in identification accuracy than the pure LightGBM-LR model, about 10.00% and 4.54% to 4.95% higher than the traditional models KNN and RF, and 1.97% to 3.09% higher than single LightGBM model. Compared with the LR model, it has a maximum accuracy improvement of about 33.29% to 35.64%.

Second-order mutation testing aims to simulate more actual and more complicated program defects by manually injecting two errors into the original programs, which is indispensable and significant in current mutation testing related studies. However, exponential growth of the number of second-order mutants and subtle second-order mutants detection are still major obstacles. In this paper, we propose a novel second-order mutants reduction method which well utilizes the Self-organizing Maps (SOM) neutral network model based mutants clustering tactic. First, a better combination strategy is used to generate feasible second-order mutants based on traditional first-order mutant generation, and then we construct accurate SOM neural network model according to the similarity of intermediate values in the execution of second-order mutants, and at last mutants are clustering based on such model to achieve second-order mutant reduction and subtle mutants detection. Through analysis of experiment results, our method could greatly reduce the number of second-order mutants without big loss of mutation scores, and correspondingly reduces the execution costs for second-order mutation testing. Besides, our method could effectively find subtle second-order mutants when considering the combination of any two faults in the original program.

In recent years, mobile applications have brought great convenience to people's lives. Most financial Apps need to bind user credit cards. Thus, if historical transaction records could be effectively used to predict the potential user groups of Apps that may be tied to credit cards in the future, financial institutions can take personalized recommendation and accurate marketing promotion for this part of potential users, which helps to expand their market share of mobile applications and increase corporate profits. Therefore, this paper proposes a method for predicting potential users of financial apps based on the random forest model, and confirms that this model has better classification prediction effects and higher accuracy when dealing with the problem of predicting potential users of financial apps.

The big data technology is reshaping the ecology of national governance, and also brought new opportunities for the development of discipline inspection and supervision system. Especially, in initial reviewing stage of the disciplinary inspection and supervision case investigation, the current processing methods are inefficient and invisible clues are difficult to find, and it takes a lot of manpower to conduct comparative investigations. In order to improve such latent clues discovering work, this paper well utilize the knowledge graph technology to propose a latent clues investigation method to promote the investigation efficiency for the anti-corruption cases in discipline inspection and supervision. Throughout the actual situation of such clues investigation, we proves that our proposed method could uncover meaningful latent clues during the initial reviewing stage of the investigation of corruption cases, thereby achieving the purpose of assisting the investigation of the discipline inspection and supervision.

There are mainly three traditional data sharing methods. The first is the most direct data copy, the second is to share data based on a data sharing protocol, and the third is to share data through a data center. These methods have a common feature, that is, the data requester will get the data of the data owner. This may cause serious problems in data security, such as data leakage and data abuse. As a data center is a centralized organization, there are risks such as data loss and data tampering. In addition, various countries have also issued a series of policies on data security issues, such as the GDPR implemented by the European Union in 2018. The blockchain technology using a decentralized model can be used as a new attempt to solve the above problems. This paper studies a data sharing scheme based on blockchain, and proposes a model that combines the Ethereum blockchain and federated learning ideas, and uses off-chain storage methods to share data. In this model, users can upload data description information to the blockchain through smart contracts, and can retrieve the required data through keywords, and then send the data identification and data processing model to the data owner in the form of transactions. The data owner can use this model to process the data, and finally return the result to the data requester. Because the data owner is in full control of his data and does not expose the source data to the outside, the use of this model for data sharing can effectively avoid problems such as data leakage, data loss, and data abuse.

Because Virtual Machine Monitor (VMM) runs continuously with high load for a long time, the accumulated errors in the system can easily lead to software aging problems such as performance degradation, so that it can not provide high-quality software services. However, software rejuvenation techniques can improve the performance of software systems by cleaning up the aging factor. Therefore, how to accurately predict the occurrence time of software aging in VMM system is a crucial and valuable problem. This paper proposes a novel software aging prediction model, called the ACLM model, which integrates the Attention mechanism, the Convolutional Neural Network (CNN) and the Long Short-term Memory (LSTM) model. It can extract the temporal and spatial features of the time series more quickly and effectively. The experimental results show that compared with other models, the ACLM model has improved by 0.97% to 9.33 % and 1.48 % to 5.49 % on the MAE and the RMSE, which demonstrates that the ACLM model has higher accuracy in predicting software aging of the VMM.

Cloud computing and big data technologies have gained great popularity in recent years. MapReduce is still one of the most efficient and well-adopted computing paradigms for providing big data services. MapReduce applications need to be executed on cloud platform where failures are inevitable. Hadoop is the de facto implementation of MapReduce, but it deploys a coarse grained and unsatisfactory fault tolerant services. The failed tasks are rescheduled from scratch to re-execute from the very beginning, which apparently brings amount of overload for failure recovery, and the whole job would be heavily delayed as failures happen. In this paper, we propose a novel multi-trigger checkpointing approach for fast recovery of MapReduce tasks, named a Multi-trigger Checkpointing Tactic for fAst TAsk Recovery (McTAR). As a finer-grained and better fault tolerance tactic, our McTAR employs multi-trigger checkpoint generation, push-pull combined intermediate data distribution and optimized failure task prediction techniques together to make the recovery task attempt be able to start at a specific progress according to the valid checkpoint for intermediate data. In this way, McTAR could effectively speed up the recovery process of MapReduce jobs and highly reduce the task recovery delay.

Software rejuvenation are developed to mitigate serious consequences caused by software aging mainly through restarting software systems. As such restart actions will temporarily stop the software service, how to select the restart time precisely becomes the core research issue. Current main-stream machine learning based software rejuvenation methods predict the trend of resource usage of hardware parameters to determine the restart time. However the actual aging status in many software systems are not strongly related to the resource usage of hardware parameters, it is not rigorous to define the aging status with single hardware parameters. In this paper, we propose a novel real-time software rejuvenation decision method, named HARRD, where classic Weibull distribution in the field of reliability analysis is well utilized to simulate and model the state transition process of software aging. Then, based on this model with real-time resource usage of hardware monitoring parameters, and together integrating three model indicators, we construct the rejuvenation decision function using the analytic hierarchy process(AHP) to weight above parameters, which could finally be used as the rejuvenation decision basis for aging software systems. Our rejuvenation decision method could balance the unpredictable factors in software aging process by using accurate simulation models, and consider more indicators for rejuvenation time decision. The experimental results show that the software system based on our proposed method could achieve better software rejuvenation effects in terms of time consumption performance, average task processing speed and system stability.

A smart contract is a computer protocol intended to digitally facilitate and enforce the negotiation of a contract in undependable environment. However, the number of attacks using the vulnerabilities of the smart contracts is also growing in recent years. Many solutions have been proposed in order to deal with them, such as documenting vulnerabilities or setting the security strategies. Among them, the most influential progress is made by the formal verification method. In this paper, we propose a formal verification method based on Colored Petri Nets (CPN) to verify smart contracts in blockchain system. First, we develop the smart contract models with possible attacker models based on hierarchical CPN modeling, then the smart contract models are executed by step-by-step simulation to validate their functional correctness, and finally we utilize the branch timing logic ASK-CTL based model checking technology in the CPN tools to detect latent vulnerabilities in smart contracts. We demonstrate that our CPN modeling based verification method can not only detect the logical vulnerabilities of the smart contract, but also consider the impacts of users behavior to find out potential non-logical vulnerabilities in the contracts, such as the vulnerabilities caused by the limitations of the Solidity language.

Cloud services typically compose of multiple distributed software components that communicate with each other through web service interfaces in the cloud environments. During their long time running, the accumulation of cloud software internal errors or large consumption of computing resources will very likely lead to software aging problems. In order to solve this problem, software rejuvenation technology is proposed to prevent them from causing more serious failures by restarting the services running. In the research field of software aging and rejuvenation for cloud services, how to accurately predict the cloud resource consumption in the aging software system for determining suitable time to perform rejuvenation is a significant and indispensable issue. In this paper, a novel hybrid aging prediction model named CSSAP is proposed, which well integrates the Autoregressive Integrated Moving Average (ARIMA) model and Long Short Term Memory (LSTM) model for better fitting the linear pattern and mining the nonlinear relationship in the time series of computing resource usage data for cloud services. The experiments results show that through such hybrid and unified time series analysis, our CASSP prediction method has 4% to 71% improvements in MAE evaluation criteria and 6% to 66% improvements in RMSE evaluation criteria under different time series scenarios compared with single model used, that is, the more accurate and more comprehensive aging prediction results achieved by CSSAP is definitely conducive to perform more effective and more efficient software aging and rejuvenation for cloud services.

It is indispensable for a cloud platform to provide flexible elasticity service. However, the cloud users do not know whether elastic resource allocation of cloud platform matches their resource requirement or not, and the inappropriate purchase plan will have an impact on effect of elasticity service. Thus, a fine-grained and suitable purchase plan of cloud resources is inevitably needed. In this paper, we propose a fine-grained elasticity measurement method for cloud resources allocation towards upper-level cloud applications, called FEMCRA. It is a feasible measurement method from the perspective of testing cloud applications in advance to find a fine-grained and suitable elastic resource allocation scheme. We construct an integrated environment for simulating various elasticity level scenarios based on OpenStack, and data analysis application from CloudSuite is deployed to act as applications under testing. By executing that application under different elasticity rule-sets, which is a group of testing strategies that makes the cloud platform to be automatically scaled, we get the optimal elasticity level with least quantity of cloud resource, and the best purchase plan is correspondingly obtained which could save the expense for cloud users.

In order to meet the demand of people's travel, all kinds of hailing-taxi apps have emerged. Although the advent of these apps brings people convenience greatly, their shortcomings and deficiencies are also increasingly apparent. Due to the subjectivity of the taxi drivers, some drivers may pick up a passenger without considering the problem of traffic congestion, which decrease the efficiency of taxis. So an efficient coordination of taxi network at a large scale becomes a new challenge in the intelligent transportation. In order to better solve the insufficiency of taxi-hailing apps, strengthen the passengers' experience and improve the efficiency of taxi service, an intelligent recommendation service for taxi based on real-time traffic in the road network is designed. And then a global optimization algorithm based on simulated annealing particle swarm optimization is presented to search a vacant taxi which can reach the location of the passenger in the shortest time. The comparative experiments with other algorithms prove that the algorithm has certain effectiveness and efficiency.

Lack of flexible and cost-effective fault tolerance mechanism is one of major obstacles for enhancing availability and efficiency of cloud application systems. We propose a novel fault tolerance as a service scheme, where cloud applications utilize suitable fault tolerance service as specific added services, such as the live migration of virtual machines, software rejuvenation, and real-time failure detection, to be worked cooperatively with cloud applications themselves. Our proactive fault tolerance as a service scheme tends to achieve promising improvements on the high availability for cloud applications.

Cloud applications are typically composed of multiple cloud service components communicating with each other through web service interfaces, where each component fulfills specified functionalities. Lack of effective fault tolerance scheme is one of major obstacles for enhancing availability and efficiency of complex and aging cloud application systems. In this paper, we propose a holistic software rejuvenation based fault tolerance scheme for cloud applications, which contains three indispensible parts: adaptive failure detection, aging degree evaluation, and checkpoint with trace replay based component rejuvenation. Through a preliminary and qualitative evaluation, it shows that our new fault tolerance scheme brings promising improvement on the availability of cloud applications.

As the intricate communication and concurrency are intrinsic characteristics of BitTorrent protocol, it is difficult to perform its systematic conformance testing, because it lacks accurate formal model to specify the testing oriented functional behaviors for the protocol. In this paper, a Colored Petri Nets (CPN) model based testing approach is adopted to make the systematic conformance testing for the BitTorrent protocol feasible. Dynamic model simulation is well utilized to generate the completely feasible test cases for the actual test executions. Besides, as simulating the protocol CPN model to generate test cases is irrespective with the model size, so it can easily handle the complicated BitTorrent models to perform the testing.

P2P live streaming systems have been widely adopted nowadays. However, the flash crowd still poses challenges in such P2P systems, which often occurs when an enormous number of users suddenly arrive to view a newly released live program. Facing so many new users, a P2P streaming system usually can not provide reasonable quality of service and these new users often suffer from a long startup delay and a high service rejection rate. In this paper, we propose a bandwidth-aware peer selection method to alleviate the flash crowd. To use the rare available bandwidths more effectively, we let new peers send more requests to the high-bandwidth parents and less requests to the low-bandwidth parents, aiming to make the upload rate of each parent match well with its upload capacity. Moreover, two analytical models are also constructed to evaluate our method and the traditional random peer selection method. Both model analysis and simulation experiment reveal the merits of our method in tackling the flash crowd, in terms of growth of system scale, average startup delay and rejection rate, compared with the random peer selection method.

Modeling and testing for parallel software systems is difficult, because the number of states and execution fragments expand significantly caused by parallel behaviors, so that many traditional testing methods cannot work effectively for this kind of software. In this paper, a test sequence generation method based on model reduction for parallel software systems is shown. Firstly, a formal model for software system specification is constructed based on Coloured Petri Net (CPN), called system model; and a model reduction method based on trace-equivalent principle is shown and applied on system model, which could generate an external behavior equivalent model with smaller scale. Secondly, a linear behavior sequence of the system is specified using CPN, called LBS model, which represents testing purpose in a test case, and some operations between state space diagrams of system model and LBS model are defined, so that a sub-graph of system model state space diagram is generated, which could cover all executions of system model that involves behaviors of LBS. Finally, a performance analysis shows the effectiveness of the method.

Peer-to-Peer (P2P) technology has been widely adopted by various live streaming systems recently, due to its better scalability and lower costs compared with the client-server architecture. However, P2P live streaming systems are still challenged by the flash crowd scenarios, which often occur when a great number of users suddenly arrive and compete for the limited upload bandwidth of a P2P system. In this case, users are usually subject to a long startup delay and are likely to retry multiple times before leave out of impatience. Current studies mainly focus on the measurement of practical systems and model analysis on flash crowd, but there are few specific approaches so far. In this paper, we develop a capacity-aware user access control algorithm to relieve the flash crowd problem. Firstly, we control the peers to enter the system at a proper rate, which avoids too high arrival rate slowing down the increase of system scale. Secondly, to increase the system service capacity as soon as possible, we let the peers with higher capacity enter the system ahead of the peers with lower capacity. Finally, we also consider the waiting time of peers with low capacity and let them in before they lose patience. To evaluate our algorithm, a new analysis model is also proposed. Simulation experiments and model analysis reveal that our algorithm is more effective to increase the system scale, and can achieve shorter user waiting time as well as lower reject rate.

A novel Colored Petri Nets (CP-nets) model based test case generation approach is proposed to makes the best of advantages of the ioco testing theory and the CP-nets modeling, where the Conformance Testing orientated CP-nets (CT-CPN) is proposed for modeling certain software systems, and PN-ioco relation is defined as a new conformance relation, and finally test cases are generated through simulating the system CT-CPN models. CP-nets model simulation based test generation approach reflects the data-dependent control flow of the system behaviors, so all test cases are completely feasible for the actual test executions. Besides, better formal modeling and analytic capabilities in CP-nets modeling quite facilitate validating the accuracy of the system CT-CPN model. For effectively extending the applicability of the Petri nets based testing technologies, our novel CT-CPN model based test generation approach may well become a competent choice.

Adopting two independent models for functional verification and performance analysis respectively could not guarantee the performance models satisfying the functionality correctness. In this paper, a colored Petri nets (CP-nets) based method is proposed to integrate functional verification and performance analysis for network protocols. Firstly, a CP-nets based function model for the protocol is constructed and validated. Then, performance related temporal constrains are added into above model, and data monitor units are generated together to form a corresponding CP-nets based performance model. Finally, based on such performance model, simulation based performance evaluation is executed. Because such coessential CP-nets models are utilized where every occurrence sequence in the performance model corresponds to an occurrence sequence in the functional model, it is guaranteed that both models satisfy the functionality correctness requirements of that protocol. As a representative, an integrated analysis process of TRDP protocol is presented to illustrate the practical effectiveness of our proposed method.

Content distribution networks (CDNs) improve the performance of content delivery by replicating the popular content on surrogate servers deployed at the edge of the Internet. The CDN-P2P architecture, which combines the complementary advantages of both CDN and P2P networks, can improve the quality of service (QoS). In this paper, we propose a k-coordinated decentralized replica placement algorithm (DRPA) based on a gain formulation of the replica placement problem. Although the gain formulation is designed for different types of the CDN-P2P architecture, we focus on the robust ring-based architecture in this study. In our approach, each surrogate server makes the replica placement in terms of the content replicas on k closer surrogate servers, which enhances the system scalability compared to the centralized replica placement heuristics. In addition, according to the simulation results, the proposed algorithm is able to reduce the backbone traffic between the servers and the requesting peers compared to the traditional replica placement algorithms for the pure CDN.

It is very risky to improve the performance of network protocols without the assurance of its functional correctness, especially for protocols that with complicated and concurrent behaviors. However, in most of current model based protocol engineering projects, two independent models are adopted for individual functional verification and performance analysis, which could not guarantee the performance model satisfying the functionality correctness, and usually cost more in protocol design and maintenance. In this paper, we propose a colored Petri nets (CP-nets) based method to integrate functional verification and performance analysis procedures, and focus on the BitTorrent protocol as a representative example to illustrate the practical effectiveness of our proposed methodology. That is, the functional CP-nets models of BitTorrent protocol are constructed and validated firstly, and then performance related temporal constrains are added into above models to form its performance CP-nets models for corresponding simulation based performance analysis. Because such closely related CP-nets models are utilized where every occurrence sequence in the performance model corresponds to an occurrence sequence in its functional model, it is guaranteed that both models satisfy the functionality requirements of protocol systems. Besides, model maintenance becomes more convenient.

Key management is a significant part of secure wireless communication. In IEEE 802. Hi standard, 4-way handshake protocol is designed to exchange key materials and generate a fresh pairwise key for subsequent data transmissions between the mobile supplicant and the authenticator. Due to several design flaws, original 4-way handshake protocol cannot provide satisfying security and performance. In this study, we adopt formal specification and verification methods to analyze the 4-way handshake protocol. We give its formal models utilizing two kinds of High-level Petri Nets. Based on these formal models, we use two verification methods, model checking and insecure states deduction, to perform an integrated security verification process. The verification results confirm that the 4- way handshake protocol is vulnerable to Denial-of-Service attack during handshake. To repair such vulnerability, we propose an improved key management scheme named enhanced two-way handshake protocol. According to security analysis and performance evaluation, our proposal could provide stronger security capability and cost less computation and communication time.