Multivariate time series classification (MTSC) based on deep learning (DL) has attracted increasingly more research attention. The performance of a DL-based MTSC algorithm is heavily dependent on the quality of the learned representations providing semantic information for downstream tasks, e.g., classification. Hence, a model’s representation learning ability is critical for enhancing its performance. This article proposes a densely knowledge-aware network (DKN) for MTSC. The DKN’s feature extractor consists of a residual multihead convolutional network (ResMulti) and a transformer-based network (Trans), called ResMulti-Trans. ResMulti has five residual multihead blocks for capturing the local patterns of data while Trans has three transformer blocks for extracting the global patterns of data. Besides, to enable dense mutual supervision between lower-and higher-level semantic information, this article adapts densely dual self-distillation (DDSD) for mining rich regularizations and relationships hidden in the data. Experimental results show that compared with 5 state-of-the-art self-distillation variants, the proposed DDSD obtains 13/4/13 in terms of “win”/“tie”/“lose” and gains the lowest-AVG_rank score. In particular, compared with pure ResMulti-Trans, DKN results in 20/1/9 regarding win/tie/lose. Last but not least, DKN overweighs 18 existing MTSC algorithms on 10 UEA2018 datasets and achieves the lowest-AVG_rank score.

The ubiquitous adoption of public clouds has resulted in the bloom of big data, Internet of Things, and artificial intelligence (AI) and a great capacity for applications. However, the efficient operation of these applications is challenging due to the infrastructure heterogeneity, computing hierarchy, scale distribution, and stochastic user behaviors. Among these challenges, reliability has received intense interest in the cloud community. Most existing research on the reliability of clouds is on fault discovery or fault tolerance. However, a public cloud has a wider range of failures, which makes it difficult to analyze the association between reliability and other indicators. More accurate reliability modeling methods and advanced optimization approaches are needed to ensure the reliable operation of a public cloud. To address these challenges, this article models the reliability of cloud computing from the service perspective and reasonably divides cloud services into the request processing phase and request execution phase when facing multiple users and service types. Moreover, this article combines a variety of AI-based methods, such as autonomous scheduling mechanisms, anomaly detection, and autonomous learning improvement, which can effectively adapt to the dynamic and complex cloud service environment and ensure the service reliability of a public cloud. Numerical results clearly indicate that the proposed service reliability model and autonomous optimizations are efficient for recovering the reliability of cloud computing systems in terms of multiple AI-based failure prognostics and intelligent learning ability.

Mission aborting has recently attracted great attention, where mission abort rules (MARs) have been modeled and optimized for different types of technological systems aiming to effectively mitigate the risk of system losses. However, none of the existing works have considered systems with multiple work-sharing units. This article makes advancements in the state of the art by modeling condition-based MARs for nonrepairable work-sharing systems that must perform a specified amount of work during the primary mission (PM). The MAR considered presumes aborting the PM to prevent considerable damage when the number of available units reduces to a certain number${k}$while the amount of work accomplished in the PM is less than${L}$(${k}$). After the PM abortion, a rescue procedure (RP) is executed by the remaining units to survive the system. Dynamic operating conditions during PM and RP are considered. A probabilistic model-based numerical algorithm is proposed to evaluate several performance metrics, including mission success probability, RP success probability, damage avoidance probability, and expected cost of losses. The MAR optimization problem for minimizing the expected cost of losses is formulated and solved using the genetic algorithm. An example of a chemical reactor system is provided to demonstrate the proposed algorithm as well as the benefit of MARs optimized in comparison to the “no abort” policy and the most conservative abort policy (the PM aborts upon the failure of any unit). Effects of several model parameters on the system performance metrics and optimization solutions are also examined through examples.

The IaaS (Infrastructure as a Service) is one of the most popular services from todays cloud service providers, where the virtual machines (VM) are rented by users who can deploy any program they want in the VMs to make their own websites or use as their remote desktops. However, this poses a major challenge for cloud IaaS providers who cannot control the software programs that users develop, install or download on their rented VMs. Those programs may not be well developed with various bugs or even downloaded/installed together with virus, which often make damages to the VMs or infect the cloud platform. To keep the health of a cloud IaaS platform, it is very important to implement the PHM (Prognostics and Health Management) technology for detecting those software problems and self-healing them in an intelligent and timely way. This paper realized a novel PHM technology inspired by biological autonomic nervous system to deal with the memory anomalies of those programs running on the cloud IaaS platform. We first present an innovative autonomic computing technology called Bionic Autonomic Nervous System (BANS) to endow the cloud system with distinctive capabilities of perception, detection, reflection, and learning. Then, we propose a BANS-based Prognostics and Health Management (BPHM) technology to enable the cloud system self-dealing with various memory anomalies. AI-based failure prognostics, immediate self-healing, self-learning ability and self-improvement functions are implemented. Experimental results illustrate that the designed BPHM can automatically and intelligently deal with complex memory anomalies in a real cloud system for IaaS, to keep the system much more reliable and healthier.

In medical cloud computing, a patient can remotely outsource her medical data to the cloud server. In this case, only authorized doctors are allowed to access the data since the medical data is highly sensitive. Before outsourcing, the data is commonly encrypted, where the corresponding secret key is sent to authorized doctors. However, performing searches on encrypted medical data is difficult without decryption. In this paper, we propose two Secure and Efficient Dynamic Searchable Symmetric Encryption (SEDSSE) schemes over medical cloud data. First, we utilize the secure k-Nearest Neighbor (kNN) and Attribute-Based Encryption (ABE) techniques to construct a dynamic searchable symmetric encryption scheme, which can achieve forward privacy and backward privacy simultaneously. These tow security properties are vital and very challenging in the area of dynamic searchable symmetric encryption. Then, we propose an enhanced scheme to solve the key sharing problem which widely exists in the kNN based searchable encryption scheme. Compared with existing proposals, our schemes are better in terms of storage, search and updating complexity. Extensive experiments demonstrate the efficiency of our schemes on storage overhead, index building, trapdoor generating and query.

Energy-efficient cloud computing has recently attracted much attention, where not only performance but also energy consumption are important metrics to be considered for designing rational resource scheduling strategies. Most of existing approaches for achieving energy efficient computing focus on connecting these two metrics and balancing the tradeoff between them, which however is inadequate because another important factor reliability is not considered. In fact, both virtual machine (VM) failures and server failures inevitably interrupt execution of a cloud service, and eventually result in spending more time and consuming more energy on completing the cloud service. Therefore, reliability significantly affects service performance and energy consumption, and thus they should not be handled separately. Connecting these correlated metrics is essential for making more precise evaluation and further for developing rational cloud resource scheduling strategies. In this paper, we present a correlated modeling approach applying Semi-Markov models, the Laplace-Stieltjes transform (LST), a Bayesian approach to analyze reliability-performance (R-P) and reliability-energy (R-E) correlations for cloud services using a retrying fault recovery mechanism. A recursive method is also proposed for modeling the correlations for cloud services using a check-pointing fault recovery mechanism. The proposed correlation models can be used to calculate the expected service time and energy consumption for completing a cloud service. Moreover, the models can contribute to analyzing the expected performance-energy tradeoff. We formulate the expected performance-energy optimization problem by describing performance and energy consumption metrics as functions of assigned CPU frequencies. Finally, we use a derivation approach to determine Pareto optimal solutions for the formulated optimization problem. Illustrative examples are provided.

Many grid-computing systems adopt voting-based techniques to resist sabotage. However, these techniques become ineffective in grid systems subject to collusion behavior, where some malicious resources can collectively sabotage a job execution by returning identical wrong results. Spot-checking has been used to detect and tackle the collusive issue by sending randomly chosen resources a certain number of spotter jobs with known correct results to estimate resource credibility based on the returned result. This paper makes original contributions by formulating and solving a new spot-checking optimization problem for grid systems subject to collusion attacks, with the objective to minimize probability of the genuine task failure (PGTF, i.e., the wrong output probability) while meeting an expected overhead constraint. The problem solution contains an optimal combination of task distribution policy parameters, including the number of deployed spotter tasks, the number of resources tested by each spotter task, and the number of resources assigned to perform the genuine task. The optimization procedure encompasses a new iterative method for evaluating system performance metrics of PGTF and expected overhead in terms of the total number of task assignments. Both fixed and uncertain attack parameters are considered. Illustrative examples are provided to demonstrate the proposed optimization problem and solution methodology.

As a basic query function, range query has been exploited in many scenarios such as SQL retrieves, location-based services, and computational geometry. Meanwhile, with explosive growth of data volume, users are increasingly inclining to store data on the cloud for saving local storage and computational cost. However, a long-standing problem is that the user's data may be completely revealed to the cloud server because it has full data access right. To cope with this problem, a frequently-used method is to encrypt raw data before outsourcing them, but the availability and operability of data will be reduced significantly. In this paper, we propose an efficient and geometric range query scheme (EGRQ) supporting searching and data access control over encrypted spatial data. We employ secure KNN computation, polynomial fitting technique, and order-preserving encryption to achieve secure, efficient, and accurate geometric range query over cloud data. Then, we propose a novel spatial data access control strategy to refine user's rights in our EGRQ. To improve the efficiency, R-tree is adopted to reduce the searching space and matching times in whole search process. Finally, we theoretically prove the security of our proposed scheme in terms of confidentiality of spatial data, privacy protection of index and trapdoor, and the unlinkability of trapdoors. In addition, extensive experiments demonstrate the high efficiency of our proposed model compared with existing schemes.

Large-scale cloud computing systems have served as the fundamental supporting platform for big data, Internet of Things, and artificial intelligence applications for the past decade. With the scale and complexity of these systems increasing dramatically, various hardware and software failures will inevitably occur and may not be detected and repaired in a timely manner. Besides, sophisticated architectural features of cloud computing may also have an adverse impact on system reliability. In response to these challenges, this paper proposes a simulation-driven framework based on real cloud computing system operation logs for improving failure tolerance in large-scale cloud computing systems. For a given cloud computing system, we first conduct a systematic analysis of its structure and operation characteristics. A Markov-based model is used to examine the system's potential failures, assess their severities, and suggest quick recoveries. During this process, the proposed reliability-aware resource scheduling algorithm is adopted to optimize resources so that the system's reliability can be improved cost-effectively. We also report a case study to demonstrate the application of our algorithm in improving failure tolerance of a large-scale cloud computing system.

Range search of spatial data, has been applied in many scenarios such as geometric queries, location-based services, and computational geometry, etc. Due to the increasing amount of spatial data, which are usually outsourced to the cloud for saving storage and computational overhead. However, a common privacy issue is that the cloud server may steal user's sensitive information utilizing its powerful computing advantages. A feasible way of managing this bottleneck is to encrypt spatial data before outsourcing it. Nevertheless, the availability of data will be significantly reduced because of the query difficulty over the encrypted cloud data. In this paper, we propose an Efficient Range Search scheme (EFRS) which can achieve fine- grained query over encrypted spatial data. We original contributions are threefold. First, polynomial fitting technique and orderpreserving encryption are introduced to realize the efficient and fine- grained range query over encrypted cloud data. Then, in order to improve the search efficiency, we exploit the Rtree to significantly decreased the search space. Finally, we theoretically proved the security of our proposed scheme in terms of confidentially of spatial data, privacy protection of index and trapdoor, and the unlinkability of trapdoor. Besides, extensive experiments demonstrate the high efficiency of our proposed model compared with existing schemes.

In recent years, the rapid development of Internet of Things (IoT) and big data shows the huge demand of outsourcing computing in cloud computing to assist some clients with low ability devices to fulfill massive data processing. In the mean time, considering the uncertainty of the Internet devices, we should persist security and high-efficiency. Uploaded data and returned results should be protected from attack of adversary, which guarantees the security, while efficiency requires low overhead of clients to finish the whole calculation procedure. In this paper, we main focus on the public verifiable outsourcing scheme on matrix multiplication, which can be applied in many IoT scenes, such as path planning and aggregation operation in Internet of Vehicles and smart grids, respectively. Specially, this paper presents schemes for two different functions of matrix multiplication, which strengthens the applicability. Moreover, security analysis and performance evaluation in this paper properly present the superiority of this paper.

Many real-world critical systems, such as aircraft and human space flight systems, utilize mission aborts to enhance the survivability of the system. Specifically, the mission objectives of these systems can be aborted in cases where a certain malfunction condition is met, and a rescue or recovery procedure is then initiated for system survival. Traditional system reliability models typically cannot address the effects of mission aborts, and thus are not applicable to analyzing systems subject to mission abort requirements. In this paper, we first develop a numerical methodology to model and evaluate mission success probability and system survivability of 1-out-of-N warm standby systems subject to constant or adaptive mission abort policies. The system components are heterogeneous, characterized by different performances and different types of time-to-failure distributions. Based on the proposed evaluation method, we make another new contribution by formulating and solving the optimal mission abort problem, as well as a combined optimization problem that identifies the mission abort policy and component activation sequence maximizing mission success probability while achieving the desired level of system survivability. Efficiencies of constant and adaptive mission abort policies are compared through examples. Examples also demonstrate the tradeoff between system survivability and mission success probability due to the utilization of a mission abort policy. Such a tradeoff analysis can help identify optimal decisions on system mission abort and standby policies, promoting safe and reliable operation of warm standby systems.

This paper models and optimizes dynamic performance of multistate systems with a general series parallel structure. Each system component is a 1-out-of-N warm standby configuration of heterogeneous functional elements, which can be characterized by different time-to-failure distributions, performances, and costs. The entire system must satisfy a random demand specified by a time-dependent distribution. An iterative algorithm is developed for determining performance stochastic processes of particular components. A universal generating function technique is used for evaluating expected system availability and unsupplied demand over a particular mission time for the considered system. Two types of optimization problems are then identified and solved, with the objective of finding component structures and element activation sequences to maximize system availability, or minimize unsupplied system demand, or minimize total cost. Optimization results can facilitate the optimal decision on design and operation of multistate series parallel systems. A practical example of a power station coal transportation system is provided to illustrate application of the proposed methodology and optimization problems.

This paper models a warm standby system where a single element is online performing a specified mission task (e.g., a computing task) and subject to corrective replacement (CR) by an available standby element upon its failure. During the mission, preventive replacements (PRs) are also performed to renew the aged or worn online operating element before its actual failure according to a predetermined policy. In addition, to facilitate an effective restoration of system function in case of CR or PR happening, backups are also performed periodically so that the mission task can be resumed from the last successful backup point instead of from scratch. The mission succeeds if the specified mission task is accomplished; in other words, the mission fails when no operating elements remain prior to the mission task completion. In this paper, we make new contributions by first proposing an event transition-based numerical method to evaluate mission performance indices of the considered standby system subject to periodic backups, CR and PR. Mission success probability (MSP), expected mission completion time, expected mission operation cost (EMC), and expected uncompleted work fraction are evaluated. Based on the suggested evaluation algorithm, we make another contribution by formulating and solving optimization problems that help to determine the optimal backup-PR policy or the optimal combination of element activation sequencing and backup-PR policy to maximize MSP or minimize EMC. Influence of element performance and reliability parameters, data backup and retrieval complexity parameters on the optimal operation policy is investigated. Findings from this paper can guide the optimal decision making on policies related to element sequencing, backups as well as preventive maintenance planning, contributing toward reliable and cost-effective design and operation of standby computing systems.

A clique model is one of the most important techniques on the cohesive subgraph detection; however, its applications are rather limited due to restrictive conditions of the model. Hence much research resorts to k-plex — a graph in which any vertex is adjacent to all but at most k vertices — which is a relaxation model of the clique. In this paper, we study the maximum k-plex problem and propose a fast algorithm to compute maximum k-plexes by exploiting structural properties of the problem. In an n-vertex graph, the algorithm computes optimal solutions in cnnO(1) time for a constant c < 2 depending only on k. To the best of our knowledge, this is the first algorithm that breaks the trivial theoretical bound of 2n for each k ≥ 3. We also provide experimental results over multiple real-world social network instances in support.

In cloud computing, outsourcing computation , as an economically promising paradigm, enables clients with limited computing resources to delegate intensive tasks to the powerful cloud server. Nevertheless, outsourcing the involved data without encryption to public brings a high risk of revealing the confidential information of the client, and no matter the returned results from server are valid or not. Due to the low trustiness of the server, it is crucial to guarantee both the security and efficiency in delegation. Motivated by those challenges, a rapidly-growing body of research mostly focuses on how to delegate mostly utilizing a feasible mechanism-verifiable computation, where the server returns results along with corresponding verification proofs. In this paper, we propose an efficient and privacyprotected delegation scheme for matrix multiplication (EPPDMM) of amortized model. EPP-DMM can realize efficient public verification together with sensitive protected data. Security analysis proves the reliability and security of our scheme, while performance evaluation presents the efficiency compared with other schemes.

In big data era, data are usually stored in databases for easy access and utilization, which are now woven into every aspect of our lives. However, traditional relational databases cannot address users' demands for quick data access and calculating, since they cannot process data in a distributed way. To tackle this problem, non-relational databases such as MongoDB have emerged up and been applied in various Scenarios. Nevertheless, it should be noted that most MongoDB products fail to consider user's data privacy. In this paper, we propose a practical encrypted MongoDB (i.e., CryptMDB). Specifically, we utilize an additive homomorphic asymmetric cryptosystem to encrypt user's data and achieve strong privacy protection. Security analysis indicates that the CryptMDB can achieve confidentiality of user's data and prevent adversaries from illegally gaining access to the database. Furthermore, extensive experiments demonstrate that the CryptMDB achieves better efficiency than existing relational database in terms of data access and calculating.

This paper models 1-out-of-N standby computing systems with a dynamic checkpointing policy. The system performs a real-time mission task that has to be accomplished within an allowed mission time. During the mission, to facilitate an effective failure recovery the system undergoes checkpointing procedures according to a policy that dynamically determines a checkpointing frequency based on the activated element and remaining work for completing the mission. System elements are heterogeneous; they can follow different, arbitrary types of time-to-failure distributions, have different performance and wait in different standby modes before their activation. A new numerical algorithm based on state space event transitions is first proposed to evaluate mission success probability of the real-time standby systems considered in this work. Additional new contributions are made by formulating and solving optimal dynamic checkpointing policy problems, as well as an integrated optimization problem that finds the optimal combination of checkpointing policy and element activation sequence maximizing mission success probability. Advantages of using the dynamic checkpointing policy over fixed even checkpoints are demonstrated through examples. Examples and results are also provided to illustrate effects of different mission and element parameters on mission success probability as well as on the optimal dynamic checkpointing policy.

Remote data integrity checking (RDIC) enables a data storage server, say a cloud server, to prove to a verifier that it is actually storing a data owner's data honestly. To date, a number of RDIC protocols have been proposed in the literature, but most of the constructions suffer from the issue of a complex key management, that is, they rely on the expensive public key infrastructure (PKI), which might hinder the deployment of RDIC in practice. In this paper, we propose a new construction of identity-based (ID-based) RDIC protocol by making use of key-homomorphic cryptographic primitive to reduce the system complexity and the cost for establishing and managing the public key authentication framework in PKI-based RDIC schemes. We formalize ID-based RDIC and its security model, including security against a malicious cloud server and zero knowledge privacy against a third party verifier. The proposed ID-based RDIC protocol leaks no information of the stored data to the verifier during the RDIC process. The new construction is proven secure against the malicious server in the generic group model and achieves zero knowledge privacy against a verifier. Extensive security analysis and implementation results demonstrate that the proposed protocol is provably secure and practical in the real-world applications.

This paper models a hybrid standby system subject to periodic inspections and condition-based standby mode transfers during a mission. At the beginning of the mission only one element is online and operating. The second element waits in a hot standby mode being ready to replace the failed online element at any time. Other elements wait in less-stressful and less-costly warm standby mode. During the mission periodic inspections are performed for checking conditions of the online and hot standby elements and subsequently triggering necessary mode transfer(s) of available warm standby element(s) to replace the failed hot standby element and/or online element. We suggest an efficient numerical method to assess availability and expected total mission cost (including standby cost, operation cost and mode transfer cost of system elements, inspection cost, system interruption or idle cost) of the considered system. The algorithm is flexible and applicable to arbitrary type of time-to-failure distributions. Then we formulate and solve new optimization problems that identify the optimal combination of inter-inspection interval and element activation sequence to minimize expected total mission cost while satisfying a certain constraint on system availability. As illustrated through examples, the optimization results can facilitate cost-effective and availability-aware planning of system inspection and operation.

This paper models a real-time warm standby system that has to accomplish a specified amount of task by a hard deadline. The system is subject to corrective replacements (CRs) upon failure of its operating element. It can also be renewed according to a predetermined schedule through preventive replacements (PRs). To facilitate an effective recovery of system operation after replacements, periodic backups are performed so that warm standby elements, upon being activated, can take over the mission task from the last backup point instead of from scratch. This paper presents a novel integrated model that considers effects of periodic backups, CRs and PRs in analyzing and optimizing real-time warm standby systems. Mission success probability and expected mission completion time are evaluated. Impacts of different mission and element parameters on mission success probability, optimal backup and PR policies, and optimal element activation sequence are investigated. It is shown that in warm standby systems with periodic backups and tight deadlines, PRs can improve the mission success probability even when they take the same time as CRs. When the maximum allowed mission time exceeds a certain level, PRs become ineffective and the optimal policy can involve only periodic backups.

Existing works on redundancy allocation problems have typically focused on active or cold standby redundancies or a mix of them; little research is dedicated to warm standby systems but with an assumption of allocating the same choice of components within each subsystem. Motivated by the fact that components with different costs and failure time distributions from different vendors can be available for the design of the same subsystem in practice, this paper advances the state-of-the-art by presenting a solution methodology to determine combined optimal design configuration and optimal operation of heterogeneous warm standby series-parallel systems. Particularly, based on a proposed numerical reliability evaluation algorithm, two combined optimization problems (component allocation and sequencing problem, and component distribution and sequencing problem) are formulated and solved. Necessity and significance of the proposed methodology are illustrated through examples. Efficiency of the methodology is also successfully demonstrated on large warm standby series-parallel systems containing 14 subsystems of different choices of components.

In large-scale cloud computing environments, a multiagent cloud system (MACS) can be developed for effectively managing cloud infrastructure. In principle, a MACS has two essential components, a global agent (GA) and multiple local agents (LAs). The GA dispatches user requests to multiple LAs, while the LAs make optimal resource management strategies for serving user requests received. To develop optimal request scheduling strategies and resource management strategies for the MACS, multiple correlated metrics, particularly reliability, performance, and energy consumption need to be comprehensively considered. In this paper, a reliability-performance-energy correlation model is first proposed. The new model captures significant effects of random resource failures and recovery on MACS performance and energy consumption to ensure high fidelity and precise evaluation. A net profit optimization model depicting the tradeoff between performance and energy consumption is then developed based on the proposed theoretical model. Furthermore, an approach for optimization is proposed, where LAs can perform dynamic resource management for reaching optimum in the net profit metric. A genetic algorithm is also designed and implemented for searching the solution of global request scheduling for the GA. Numerical examples illustrate that the MACS can achieve efficient and comprehensive scheduling and management capability based on cooperation between the GA and LAs.

This paper considers 1-out-of-N : G warm standby (WS) systems subject to multiple-phased mission requirements, where performance, failure behavior, operation cost, replacement time, and cost of system elements can vary from phase to phase due to changing working conditions and stress levels. The system succeeds if its elements can accomplish a specified mission task within the maximum allowed time. A numerical algorithm is proposed for evaluating mission indices, including system reliability, expected mission cost, and expected uncompleted work of the heterogeneous, dynamic standby system considered. The influence of the maximum allowed mission time on mission indices is investigated. As demonstrated through examples, the proposed algorithm can also facilitate a determination of mission interruption based on reliability or cost-oriented criteria. Based on the proposed algorithm, an unconstrained optimal standby element sequencing problem is then formulated and solved for heterogeneous WS multiphase systems. The problem is to find the optimal activation sequence of system elements maximizing system reliability, or minimizing expected mission cost, or minimizing expected uncompleted work. The constrained optimization problems of minimizing expected mission cost subject to providing a desired level of mission reliability or uncompleted work are also solved. Illustrative examples of these optimization problems and their applications in estimating the role of each element in the mission success are presented.

Data backups play an important role in effective recovery of system operations, especially, those in computing and Information Technology-related applications. By saving copies of information associated with the completed part of a mission task, a failed system, upon being repaired, can resume its operation from the latest backup point instead of having to repeat the entire work from scratch. This paper considers a repairable, real-time system performing a sequence of nonperiodic full backup (FB) and incremental backup (IB) procedures during its mission. The mission succeeds if a specified amount of work can be accomplished by the system within a deadline. New contributions of this paper are twofold. First, a numerical recursive method is proposed to model effects of the nonperiodic, mixed backup strategy in assessing mission success probability, and expected completion time of the considered system. The method is applicable to any distribution of FBs and IBs and has no limitation on system time-to-failure distributions. Second, the optimal backup policy problem is formulated and solved, which finds the distribution of FBs and IBs maximizing the mission success probability. Examples are provided to illustrate the proposed methodology as well as influence of different parameters on the optimal solution. Advantages of adopting nonperiodic backups over periodic ones are also illustrated.

With the rapid development of portable mobile devices, crowd sensing systems have been recognized as a key technology to utilize the data collected by the portable mobile devices towards scalable and flexible mobile services. However, since the information provided by devices may not be reliable, the aggregated results of the collected data may not be accurate. To tackle this challenge, various truth discovery schemes have been proposed. On the other hand, a practical issue of privacy protection is not considered in most existing truth discovery schemes. In this paper, we propose an Efficient Privacy-preserving Truth Discovery (EPTD) in Crowd Sensing Systems, which can protect the privacy of users' observed values and weights in truth discovery process. Finally, we show the performance of our scheme is better than existing models in terms of computation overhead.

With development of outsourcing computation, it is possible for clients with limited computing resources to outsource heavy computational tasks to the cloud server and thus relieve huge burden of the client. As continuous attention of delegation in recent years, requirements of security and efficiency are badly concerned undoubtedly, especially for matrix multiplication. Considering wide applications of matrix multiplication, e.g. graph processing and large date processing, in this paper, we present an identity-based publicly verifiable delegation scheme in amortized model which meets the need of security and efficiency both. Moreover, by using an secure encryption algorithm and a verification certification, the security analysis of the proposed scheme demonstrates the privacy of matrixes involved and the correctness. To demonstrate efficient properties, we compared our scheme with some existing works in terms of functionality as well as computation, storage and communication overhead.

This paper models repairable computing systems performing a mission that is successful if the system can accomplish a specified amount of work within the allowed mission time or deadline. During the mission the system is subject to a sequence of full and incremental data backup procedures to facilitate an effective system recovery and avoid repeating the entire mission work from the very beginning when a system failure happens. The repair time is fixed while the system time-to-failure can follow any arbitrary type of distributions. This paper makes novel contributions by first developing a new numerical algorithm to evaluate mission success probability and expected completion time of the considered repairable real-time computing systems subject to mixed full and incremental backups. Correctness of the proposed evaluation algorithm is verified using Monte Carlo simulations. We make another new contribution by formulating and solving the backup schedule optimization problem that finds the full and incremental backup frequencies maximizing the mission success probability. Through illustrative examples, effects of different parameters (including the system time-to-failure distribution parameter, maximum allowed mission time, data backup and retrieval times, storage availability, repair time and efficiency) on the mission success probability and expected completion time as well as on the optimal backup schedule solution are investigated.

Motivated by practical applications (for example production systems, flow transmission systems, and transportation systems), this paper considers 1-out-of- N: G warm standby systems subject to periodic inspections. Periodic inspections are performed to detect failures of system components. If an online operating component failure is detected during inspection, an available warm standby component is activated to take over the mission task. The mission succeeds if system components can complete a pre-specified amount of work. A state space event transition based numerical algorithm is first suggested for evaluating important mission performance indices including mission success probability, expected mission cost, expected mission time, expected mission completion delay over a desired time, and expected uncompleted work of the considered standby system. Based on the proposed evaluation algorithm, inspection interval optimization problems are formulated and solved, which find the optimal value of the inspection interval to minimize the expected total mission cost subject to providing a desired level of mission success probability, expected completion time, and delay. Further, combined component sequencing and inspection interval optimization problems are solved, for minimizing the expected total mission cost, or maximizing the mission success probability. As illustrated through examples, the proposed methodology can also facilitate solving dynamic optimization problems that update the optimal solution after each component failure, leading to further improvements of various mission indices.

Cold standby redundancy is a widely-applied design strategy to achieve high system reliability in numerous applications. To facilitate an effective system recovery in case of an online element failure occurring, the backup mechanism is typically implemented which enables a standby element to take over the mission task from a backup point instead of re-executing the entire mission task from the very beginning. However, the backup mechanism is not perfectly reliable in practice, and effect of its failure on the system reliability can be non-monotonic and is correlated to other system and element parameters. In this paper a new numerical approach is first proposed to evaluate reliability and expected mission completion time for 1-out-of- N: G cold standby systems subject to imperfect even backups. The system elements are non-repairable during the mission. Based on the proposed evaluation algorithm, effects of backup system reliability in connection with other system parameters including backup frequency, data backup and retrieval times, replacement failure probability, replacement time, and number of system elements are investigated through examples. Further, the optimal backup frequency and initiation sequencing problem is formulated and solved for heterogeneous cold standby systems, providing solutions that can maximize mission reliability or minimize expected mission completion time depending on design requirements.

While the majority of existing works on modeling and optimizing standby systems focus on single-phased missions, only a few works consider standby systems with phased-mission requirements, and these works are only applicable to restricted cold-standby configurations with assumptions of negligible replacement times and fixed phase durations. This paper makes new theoretical contributions by suggesting a general model of heterogeneous 1-out-of- N: G warm standby phased-mission systems (PMS) with dynamic phase durations. The model takes into account diverse, phase-dependent performances and time-to-failure distributions of system elements. It also considers non-zero replacement times specific for activated elements, and for the mission phase when the replacement occurs. Both cold and hot standby PMSs are special cases of the proposed model. An algorithm for evaluating the mission reliability and expected completion time is first presented. The algorithm is applicable to any type of time-to-failure distribution for system elements. The heterogeneous standby PMS can demonstrate non-coherent behavior where the reduction of reliability of some elements can cause the increase of the entire mission reliability. Then it is demonstrated that the sequence of the element activation affects the mission reliability and its expected completion time. Hence the element sequence optimization problem is further formulated and solved for the heterogeneous warm standby PMS. Illustrative examples of mission reliability and expected completion time analysis and optimization are presented.

Cloud computing is a new emerging technology aimed at large-scale resource sharing and service-oriented computing. To achieve the efficient use of cloud resources for supporting a cloud service, many important factors need to be considered, particularly, reliability, performance, and power consumption of the cloud service. Evaluation of these metrics is essential for further designing rational resource scheduling strategies. However, these metrics are closely related; they do affect one another. The cloud system should consider correlations among the metrics to make more precise evaluation. Most of the existing approaches and models handle these metrics separately, and thus they cannot be used to study the correlations. This paper presents a new hierarchical correlation model for analyzing and evaluating these correlated metrics, which encompasses Markov models, queuing theory, and a Bayesian approach. Various distinctive characteristics of the cloud system are investigated and captured in the model, such as multiple virtual machines (VMs) hosted on the same server, common cause failures of co-located VMs caused by server failures, and logical mapping mechanisms for multicore CPUs. Moreover, for evaluating and balancing the tradeoff between performance and power consumption, a tradeoff parameter and a pure profit optimization model are developed based on the presented correlation model. Numerical examples are provided.

Point-of-interest(POI) recommendation becomes a valuable service in location-based social networks. Based on the norm that similar users are likely to have similar preference of POIs, the current recommendation techniques mainly focus on users' preference to provide accurate recommendation results. This tends to generate a list of homogeneous POIs that are clustered into a narrow band of location categories(like food, museum, etc.) in a city. However, users are more interested to taste a wide range of flavors that are exposed in a global set of location categories in the city.In this paper, we formulate a new POI recommendation problem, namely top-K location category based POI recommendation, by introducing information coverage to encode the location categories of POIs in a city.The problem is NP-hard. We develop a greedy algorithm and further optimization to solve this challenging problem. The experimental results on two real-world datasets demonstrate the utility of new POI recommendations and the superior performance of the proposed algorithms.

E-health data release, which answers the statistical queries of the Electronic Health Records (EHRs), has been widely adopted in modern health care services. However, since the EHRs contain sensitive information of the patients, the data release procedure may lead to the leakage of the privacy of patients if it is done without necessary protection measures in place. On addressing this, existing research literature introduces differential privacy to provide the necessary privacy guarantee. However, it is not suitable in sensitive e-health environments because it lacks the efficiency for data processing and updating. In this paper, we propose an efficient e-health data release scheme with consistency guarantee under differential privacy. Specifically, we improve the performance of the previous work by designing a new private partition algorithm of histogram and also proposing a heuristic hierarchical query method. We conduct real experiments and compare our scheme with the existing one to show that the proposal is more efficient in terms of data processing and updating. Moreover, we increase the accuracy of data release through consistency and give proof of privacy to show that the proposed algorithm is under ϵ-differential privacy.

Life critical applications like space missions and flight controls require their computing systems to be equipped with some fault-tolerance mechanism to meet stringent reliability requirements by performing the intended function even in the case of element failures. Such benefit, however, cannot come without extra time as well as extra overhead and capital costs. This paper for the first time considers the modeling and evaluation of mission reliability, expected mission time and cost simultaneously for 1-out-of-$N$: G non-repairable cold standby computing systems subject to periodic backup actions. Based on the suggested numerical evaluation method, the optimal backup frequency problems are formulated and solved, providing the optimal number of backup operations during the mission to maximize the system reliability or to minimize the mission cost or time. In the case of non-identical system elements, the optimal standby element sequencing problem arises as the order in which the system elements are initiated can impact the system reliability and mission cost and time greatly; such problems are formulated and solved for the 1-out-of-$N$: G cold standby computing systems with periodic backups. Furthermore, a combined optimization problem is considered, where a combination of the element initiation sequence and backup frequency providing the best combination of mission reliability, cost, and time is found. The proposed methodology can facilitate a reliability-cost-time tradeoff study in the practical design of cold standby systems, thus assist in making the optimal decision on the system's standby and backup policy. Examples are provided for illustrating the considered problems and suggested solution methodology.

This paper considers 1-out-of- N:G heterogeneous fault-tolerant systems that are designed with a mix of hot and cold standby redundancies to achieve the tradeoff between restoration and operation costs of standby elements. In such systems, the way in which the elements are distributed between hot and cold standby groups and the initiation sequence of all the cold standby elements can greatly affect the system reliability and mission cost. Therefore, it is significant to solve the optimal standby element distributing and sequencing problem (SE-DSP). The failure that occurs in a system element can propagate, causing the outage of other system elements, which complicates the solution to the SE-DSP problem. In this paper, we first propose a numerical method for evaluating the reliability and expected mission cost of 1-out-of- N:G systems with mixed hot and cold redundancy types and propagated failures. Two different failure propagation modes are considered: an element failure causing the outage of all the system elements, and an element failure causing the outage of only working or hot standby elements but not cold standby elements. A genetic algorithm is utilized as an optimization tool for solving the formulated SE-DSP problem, leading to a solution that can minimize the expected mission cost of the system while providing a desired level of the system reliability. Effects of the failure propagation probability on the system reliability, expected mission cost, as well as the optimization results are investigated. The suggested methodology can facilitate a reliability-cost tradeoff study of the considered systems, thus assisting in optimal decision making regarding the system's standby policy. Examples are provided for illustrating the considered problem as well as the proposed solution methodology.

In this paper we model and analyze non-repairable 1-out-of- N : G warm standby systems subject to periodic backups and dynamic reworking. Particularly, in such systems, a standby element must redo some portion of already performed work by the failed online element before taking over the mission task, which makes the actual mission time dynamic. The considered systems are widely used in applications such as computing and manufacturing, but have not been well studied in reliability theory. In this work, we make new contributions by suggesting a numerical algorithm to evaluate the reliability of the considered warm standby systems. It is revealed that these systems are non-coherent, where the system reliability has non-monotonic dependence on the reliability of individual elements. Numerical examples further show that the non-coherency phenomenon is more distinguished for elements initiated earlier than those initiated later in the warm standby list. Example results also imply that placing highly unreliable elements at the end of the warm standby waiting list, or even removing them from the system planning, can enhance the reliability of a warm standby system subject to reworking. Findings from this work can guide the reliability design of the considered warm standby systems in practice.

The paper proposes a new fault tolerant system design model, in particular a 1-out-of- N:G hybrid redundant system with standby elements subject to state-dependent standby mode transfers. Specifically, in such systems, one standby element always resides in the hot standby mode, and thus is ready to replace the failed online element at any time to make the system dependable. If the online operating element or the hot standby element fails, one of the warm standby elements is immediately transferred to the hot standby mode. A numerical algorithm is first suggested for evaluating the reliability and the expected mission cost of the considered system. The algorithm is based on a discrete approximation of element time-to-failure distributions, and can work with any type of distribution. Furthermore, based on the suggested algorithm, the problem of optimal sequencing of standby elements initiation is formulated and solved. The objective of this optimization problem is to minimize the expected mission cost associated with elements' standby and operation expenses, as well as the mode transfer expenses, while meeting a certain system reliability constraint. Illustrative examples are provided.

In standby systems, when an online working element fails, a replacement procedure is initiated to activate a standby unit which will take over the mission task to sustain the system function. Existing works on standby systems have mostly assumed that such replacement procedure takes a negligible or fixed amount of time. This assumption is not practical in many real-world systems, where the replacement procedure can take times that are random and different for different standby elements. This paper makes novel contributions by considering the effects of the random replacement times in analyzing and optimizing 1-out-of- N: G non-repairable warm standby systems. The system elements are not necessarily identical; different elements can have different time-to-failure distributions, different performance levels, and different replacement time distributions. The system is considered failed if the elements cannot complete the specified amount of work (mission task) within the maximum allowed mission time. A numerical algorithm is first proposed to simultaneously evaluate the mission reliability and expected mission completion time of the considered warm standby system. Influences of different mission and element parameters on the mission reliability and expected completion time are investigated. It is revealed that the considered warm standby systems exhibit non-coherent behavior as mission reliability may increase with the decrease of an element's reliability. Due to heterogeneity of the system elements, the order in which the elements are initiated and replaced can affect the mission reliability and actual completion time significantly. Therefore, based on the suggested numerical evaluation algorithm, we further formulate and solve the optimal element replacement sequencing problem for the considered warm standby system subject to random replacement times. Examples are given to demonstrate the considered problems and proposed methodology.

In this paper, mission reliability, expected mission completion time, and cost of non-repairable 1-out-of- N: G warm standby sparing systems subject to uneven backup actions are modeled and optimized. The backup actions are used to facilitate the data recovery process in the case of an online operating element failure, which enables an activated standby element to take over the mission task through subsequent data retrievals. Both data backup and retrieval times are dynamic, and physically dependent on the amount of work performed. The system elements are not necessarily identical; each element can be characterized by a different time-to-failure distribution, a different performance, and a different level of readiness to take over the system task during the warm standby mode. An iterative numerical method is first proposed to simultaneously evaluate mission reliability, expected mission completion time, and the cost of the considered heterogeneous warm standby systems. Due to the non-monotonic effect of the backup distribution on the mission reliability, time, and cost, we formulate and solve the optimal backup distribution problem considering different combinations of optimization objectives and constraints. In the case of system elements being non-identical, their activation order can influence the mission reliability, expected mission completion time, and mission cost significantly. Therefore, we also formulate and solve the optimal element sequencing problem for the considered system. Furthermore, new integrated optimization problems are formulated and addressed. The integrated optimization aims to identify the optimal combination of backup distribution and element activation order that maximizes the mission reliability, or minimizes the expected mission time or mission cost. As shown through examples, the proposed methodology can implement a tradeoff analysis among the three mission requirements of reliability, cost, and completion time, leading to the optimal decision on both backup and standby policies of warm standby systems.

This paper considers nonrepairable 1-out-of-N: G cold standby (CS) systems subject to uneven backup actions as well as dynamic backup and retrieval times. In such systems, only one element is online and operates with the rest of the elements waiting in the unpowered CS mode. The operating element performs data backup actions when certain fractions of the mission task are accomplished. The backup actions facilitate data recovery in case of an operating element failure, which allows an activated standby element to take over the task through data retrieval. Backup distribution can have a nonmonotonic effect on mission reliability, time, and cost, leading to the optimal backup distribution problem. In this paper, we first suggest a numerical method to model and evaluate mission reliability, expected time, and cost simultaneously for the considered CS systems with uneven backup actions and dynamic backup and retrieval times. Based on the suggested evaluation method and genetic algorithm, the optimal backup distribution problem is then formulated and solved with the objective to minimize the expected mission cost subject to meeting certain levels of mission reliability and expected mission time. Examples show that the proposed methodology can facilitate a tradeoff study between mission reliability, and time and cost, which assists in the optimal decisionmaking for the backup policy used in the practical design of CS systems.

Standby redundancy is a design technique that has been widely adopted to enhance system reliability and achieve fault tolerance in many critical applications. In this paper, we consider a 1-out-of-N: G hybrid standby redundant system with unrepairable elements being subject to delayed failure-driven standby mode transfers. Specifically, in the considered system, all the standby elements are initially in a warm standby mode (WSM) but can be transferred to a hot standby mode (HSM) so as to be ready to replace the online operating element when it fails. The WSM to HSM transfer is performed with a fixed time delay after no element resides in HSM either due to the element failure or because the element leaves the HSM to replace the failed online element for operation. A new iterative numerical algorithm is first proposed for evaluating reliability and expected mission cost (relevant to elements' standby expense, operation expense, as well as mode transfer expense) of the considered hybrid standby system. The algorithm has no restriction on element time-to-failure distribution types. Based on the proposed evaluation algorithm, we further formulate and solve a new optimization problem that finds the optimal delay and optimal sequence of standby elements with the objective to minimize expected mission cost while satisfying a certain level of mission reliability constraint. Examples are presented to demonstrate applications of the proposed methodology.

Computing systems and networks become increasingly large and complex with a variety of compromises and vulnerabilities. The network security and privacy are of great concern today, where self-defense against different kinds of attacks in an autonomous and holistic manner is a challenging topic. To address this problem, we developed an innovative technology called Bionic Autonomic Nervous System (BANS). The BANS is analogous to biological nervous system, which consists of basic modules like cyber axon, cyber neuron, peripheral nerve and central nerve. We also presented an innovative self-defense mechanism which utilizes the Fuzzy Logic, Neural Networks, and Entropy Awareness, etc. Equipped with the BANS, computer and network systems can intelligently self-defend against both known and unknown compromises/attacks including denial of services (DoS), spyware, malware, and virus. BANS also enabled multiple computers to collaboratively fight against some distributed intelligent attacks like DDoS. We have implemented the BANS in practice. Some case studies and experimental results exhibited the effectiveness and efficiency of the BANS and the self-defense mechanism.

This paper considers the optimal cold standby element sequencing problem (SESP) for 1-out-of- n: G heterogeneous non-repairable cold-standby systems that accomplish multi-phase missions. Given a fixed set of element choices, the objective of the optimal system design is to select the initiation sequence of the system elements so as to minimize the expected mission cost while providing a desired level of system reliability. It is assumed that during different mission phases the elements are exposed to different stresses, which affects their time-to-failure distributions. The startup and exploitation costs of system elements are also phase dependent. We suggest an algorithm for evaluating the mission reliability and expected mission cost based on a discrete approximation of time-to-failure distributions of the system elements. A genetic algorithm is used as an optimization tool for solving the formulated SESP for multi-phase cold-standby systems. Examples are given to illustrate the considered problem and the proposed solution methodology.

Many continuous transportation systems can be represented as multi-state linear consecutively connected systems consisting of N+1 linearly ordered nodes. Some of these nodes contain statistically independent multistate elements with different characteristics. Each element j can provide a connection between the node to which it belongs and Xjnext nodes, where Xjis a discrete random variable with known probability mass function. If the system contains nodes not connected with any previous node, then gaps exist that require bypass transportation solutions associated with considerable expenses. An algorithm based on the universal generating function method is suggested for evaluating the expected value of these expenses. A problem of finding the multi-state element allocation that minimizes the expected bypass transportation expenses is formulated and solved. Illustrative examples are presented.

System structure optimization is a well-studied problem in the field of reliability engineering, which aims to achieve the best possible reliability versus cost solutions for the system design. This problem is usually solved for systems that do not change their task and configuration during the mission. However, many practical systems are phased-mission systems (PMS), where the mission involves multiple, consecutive, and nonoverlapping phases of operation. An accurate analysis of PMS must consider the dynamics in system configuration, success criteria, and element behavior as well as statistical dependence of element states across phases. In this paper, we propose a method for solving the structure optimization problem of multistate PMS consisting of nonidentical nonrepairable binary elements. The system configuration and demand as well as the failure distributions of system elements can change from phase to phase. The proposed approach is based on a recursive algorithm for the reliability evaluation of PMS and a genetic algorithm for the structure optimization of PMS. The method is illustrated using an example of a six-phased mission running on an airborne distributed computing system.

In this paper, mission cost and reliability of 1-out-of-N: G nonrepairable warm standby systems with imperfect switching are modeled and analyzed using an iterative method. A general switching structure is considered, which consists of an overall fault detection mechanism (FDM) and a set of individual switches (one for each standby element). The failure of the FDM prevents the replacement of the failed online element by any standby element while the failure of an individual switch only makes the corresponding standby element unavailable. The entire mission fails either when the FDM fails before the failure of the online element during the mission, or when all the system elements have failed or become unavailable before the mission completion. Based on the proposed algorithm for the mission cost and reliability analysis, the optimal element sequencing problem is further formulated and solved for 1-out-of-N: G nonrepairable warm standby systems with nonidentical elements and imperfect switching mechanisms. The objective of the problem is to find the optimal initiation sequence of system elements that can minimize the expected mission cost while providing a certain level of system reliability. Examples are given to illustrate the considered problem and the proposed solution methodology.

This paper presents an algorithm for evaluating the reliability and performance distribution of complex non-repairable series-parallel multi-state systems with common cause failures caused by propagation of failures in system elements. The failure propagation can have a selective effect, which means that the failures originating from different elements can cause failures of different subsets of system elements. The failure propagation time is assumed to be a random value with a given distribution. The suggested algorithm is based on the universal generating function approach, and a generalized reliability block diagram method (recursive aggregation of pairs of elements and their replacement by an equivalent one). The evaluation procedure is repeated for each combination of elements affected by the common cause failures. Illustrative examples are provided.

Many continuous transportation systems and communication networks can be modeled as a linear consecutively connected system (LCCS) consisting of n+1 linearly ordered nodes. Some of these nodes contain connection elements (CEs) with different characteristics. Each CE i in its working state provides a connection between the node to which it belongs and L(i) next nodes, the set of nodes that follow the given node. If the system contains any node not connected with any previous node, then it fails. We consider multi-phase LCCS that should perform a sequence of transmission tasks. In each phase, it should provide continuous connection along a specific path of nodes. Different paths can contain the same nodes, which creates statistical dependence across the phases. Different nodes are characterized by specific conditions expressed by different failure acceleration factors affecting the CEs located at these nodes. Thus, an accurate reliability analysis of a multi-phase LCCS must consider the statistical dependencies of CE states across phases, and dynamics in path structures as well as in the failure acceleration factors. In this paper, we propose a method for optimal allocation of CEs in multi-phase LCCS. The method is based on a recursive algorithm for exact system reliability evaluation, and the genetic algorithm for the optimal allocation of CEs to nodes of LCCS. The proposed approach is illustrated using a practical example of wireless sensor networks.

This paper considers a system consisting ofnlinearly ordered multistate components. Each component can have different states: from complete failure, up to perfect functioning. A performance rate is associated with each state. The system fails if in each of at leastmconsecutive overlapping groups ofrconsecutive components (windows) the sum of the performance rates of components belonging to the group is lower than a minimum allowable level. It is shown that, in the case of different components, the system reliability depends on their arrangement. The optimal arrangement problem is formulated, and a numerical tool for solving this problem is suggested. The tool uses an extended universal moment generating function technique for system reliability evaluation, and a genetic algorithm for optimization. Examples of system reliability optimization are presented.

Phased-mission systems (PMSs) are systems supporting missions characterized by multiple, consecutive, and nonoverlapping phases of operation. Examples of PMSs abound in many practical applications such as aerospace, nuclear power, and airborne weapon systems. Reliability analysis of a PMS must consider statistical dependence of component states across different phases, as well as dynamics in system structure functions and component behavior. In this paper, we propose a recursive method for exact reliability evaluation of a binary-state or multistate PMS consisting of nonidentical, binary, and nonrepairable elements. The system elements can fail individually or due to common-cause failures (CCFs) caused by some external factors. The proposed method is based on the branch-and-bound principle, and can be fully automated. The method is applicable to PMSs with nonoverlapping or overlapping sets of elements that can fail as a result of CCFs. The method is illustrated using both analytical and numerical examples.

This paper suggests a method for the reliability analysis of binary-state systems subject to component failures with dependent propagation effect. A propagated failure originating from a system component causes extensive damage to the rest of the system. The level of the damage can be dependent upon the status of other system components and the order of the component failures. Such dependent propagation effect typically takes place in systems with some protection mechanism (e.g., firewalls, filters, and antivirus programs) or systems subject to functional dependence behavior where the failure of a system component, referred to as a trigger, causes other components within the same system to become inaccessible or isolated from the system. A combinatorial and analytical method is proposed for addressing the dependent propagation effect in the system reliability analysis. Basics and application of the proposed method are illustrated through analyses of an example of a network system in two different scenarios for propagation effects and an example of a memory system with multiple trigger events.

This paper generalizes the linear multistate consecutively-connected system model by introducing allowable gaps. The new model consists of N +1 linearly ordered nodes. Some of these nodes contain statistically independent multistate elements with different characteristics. Each element j can provide a connection between the node to which it belongs and Xjnext nodes, where Xjis a discrete random variable with known probability mass function. The system fails if it contains at least m consecutive nodes not connected with any previous node (m consecutive gaps). An algorithm based on the universal generating function method is suggested for the system reliability evaluation. Illustrative examples are presented.

This paper proposes a new model that generalizes the linear multistate sliding window system to the case of multiple failures. In this model, the system consists of independent linearly ordered multistate elements. Each element can have different states: from complete failure up to perfect functioning. A performance rate is associated with each state. The system fails if, at least in groups of consecutive elements (windows), the sum of the performance rates of elements belonging to the group is less than a minimum allowable level. An algorithm for system reliability evaluation is suggested which is based on an extended universal moment generating function. Examples of evaluating system reliability and elements' reliability importance indices are presented.

Computing software and systems have become increasingly large and complex. As their dependability and autonomy are of great concern, self-healing is an ongoing challenge. This paper presents an innovative model and technology to realize the self-healing function under the real-time requirement. The proposed approach, different from existing technologies, is based on a new concept defined as consequence-oriented diagnosis and healing. Derived from the new concept, a prototype model for proactive self-healing actions is presented. Then, a hybrid diagnosis tool is proposed that takes advantages from the Multivariate Decision Diagram, Fuzzy Logic, and Neural Networks, achieving an efficient, effective, accurate, and intelligent result. The consequence-oriented diagnosis and self-healing function is also implemented. The experimental results exhibit that the innovative system is very effective and precise in predicting the consequence, and in preventing resulting software and system failures.

This paper proposes a new model that generalizes the linear consecutivek-out-of-r-from-nsystem to the case ofmconsecutive runs ofrelements. In this model, the system consists ofnlinearly ordered statistically independent elements, and fails iff in each of at leastmconsecutive overlapping groups ofrconsecutive elements at leastkelements fail. This system model is motivated by a heating system for moving parts. An algorithm for system reliability evaluation is suggested which is based on an extended universal moment generating function. Examples of system reliability evaluation are presented.

Multistate phased-mission systems (MS-PMS) are multistate systems subject to multiple, consecutive, and nonoverlapping phases of operation. The challenges in analyzing MS-PMS reside in the dynamic system configuration, failure criteria, and component state transition behavior in different phases, as well as thes-dependence across different phases and among different states of a given component. Existing methods for the reliability analysis of MS-PMS are either based on monolithic Markov models that suffer from the well-known state explosion problem, or using a hierarchical strategy that can only handle ordered component states. This paper presents integrated modeling approaches for the reliability analysis of repairable MS-PMS with both ordered and unordered component states. The proposed methods integrate efficient decision diagram models for representing the system structure function and incorporating the unordered/ordered component states at the system level, and Markov models for describing dependence and transition behaviors at the component level. The application and advantages of the proposed approaches are illustrated through a case study in which the reliability for a sequence of tasks in a multistate distributed computing system is analyzed.

Abstract Next generation communications (NGC) represents advanced communication environments featuring objects that are focused on users. It is crucial to protect information and digital forensics for NGC. In this paper, we aim to address network security for NGC, including IEEE standard 802.16 that are associated with NGC utilization. The IEEE standard 802.16 is a wireless air interface technology in Wireless Metropolitan Access Network (WMAN). Now in the new era of wireless networking, it is the solution for ‘last mile’ problem. Point‐to‐MultiPoint (PMP) network architecture network mode and mesh network mode are two optional networking modes proposed in this standard. However, the security problems in its original protocol may be becoming the most serious obstacle in its marketable producing process. In this paper, explanation and analysis of the security mechanism are based on PMP and mesh networking modes, respectively. This paper focuses on authentication & authorization, which is the most important part but not explicitly defined in original security design of IEEE standard 802.16. Two improved schemes focused on authentication & authorization based on the two modes are proposed to strengthen 802.16 WMAN security, respectively. The relevant analysis and simulation results prove that our schemes are effective. Copyright © 2009 John Wiley & Sons, Ltd.

Decision diagrams are graphical structures based on Shannon's decomposition. They have been extensively used for representing and manipulating logic functions in areas such as circuit verification, compact Markov chain representation, and symbolic model checking. However, their applicability in reliability modeling and analysis has only been recently studied. Moreover, the study had been mostly restricted to binary-state systems in which both the system and its components are either operational, or failed. Nevertheless, many practical systems are multi-state systems (MSS) in which both the system and its components may reside at multiple (more than two) performance levels (or states), varying from perfect operation to complete failure. This paper presents three forms of decision diagrams for the modeling and analysis of MSS: binary decision diagrams, logarithmically encoded binary decision diagrams, and multi-valued decision diagrams. We present both separated, and shared methods based on these decision diagrams. Comprehensive complexity analysis, and performance comparisons among these methods, are conducted with both mathematical, and empirical approaches.

Multistate systems can model many practical systems in a wide range of real applications. A distinct characteristic of these systems is that the systems and their components may assume more than two levels of performance (or states), varying from perfect operation to complete failure. The nonbinary property of multistate systems and their components makes the analysis of multistate systems difficult. This paper proposes a new decision-diagram-based method, called multistate multivalued decision diagrams (MMDD), for the analysis of multistate systems with multistate components. Examples show how the MMDD models are generated and evaluated to obtain the system-state probabilities. The MMDD method is compared with the existing binary decision diagram (BDD)-based method. Empirical results show that the MMDD method can offer less computational complexity and simpler model evaluation algorithm than the BDD-based method.

Trusted collaborative computing (TCC) is a new research and application paradigm. Two important challenges in such a context are represented by secure information transmission among the collaborating parties and selective differentiated access to data among members of collaborating groups. Addressing such challenges requires, among other things, developing techniques for secure group communication (SGQ), secure dynamic conferencing (SDC), differential access control (DIF-AC), and hierarchical access control (HAC). Cryptography and key management have been intensively investigated and widely applied in order to secure information. However, there is a lack of key management mechanisms which are general and flexible enough to address all requirements arising from information transmission and data access. This paper proposes the first holistic group key management scheme which can directly support all these functions yet retain efficiency. The proposed scheme is based on the innovative concept of access control polynomial (ACP) that can efficiently and effectively support full dynamics, flexible access control with fine-tuned granularity, and anonymity. The new scheme is immune from various attacks from both external and internal malicious parties.

Grid computing is a recently developed technique for complex systems with large-scale resource sharing, wide-area communication, and multi-institutional collaboration. Although the development tools and techniques for the grid have been extensively investigated, the availability of the grid resource management system (RMS) has not been comprehensively studied. In order to contribute to this lacking but important field, this paper first models the grid RMS availability by considering both the failures of resource management (RM) servers and the length limitation of request queues. A hierarchical Markov reward model is implemented to evaluate the grid RMS availability. Based on the availability model, an optimization problem for designing the grid RMS is studied in order to minimize the cost by determining the best number of RM servers. Then, the sensitivity analysis is conducted, and a dynamic switching scheduling method is further presented based on the sensitivity analysis.

NASA ANTS autonomous nano-technology swarm missions will be operating in the universe, and therefore rely much on high autonomy. This paper presents a novel technology for NASA's ANTS missions, named as model-driven autonomic computing. As the foundation for the technology, a new model is constructed for the ANTS system. Exceeding other existent models, the new hierarchical model overcomes the challenges of largeness, complexity, dynamicity and unexpectedness possessed by the ANTS system. Then, the paper exhibits the structure and functions of virtual neuron that is basic unit together with the model for the model-driven autonomic technology in ANTS missions. The paper also deploys self-configuration, self-healing, self-optimization and self-protection for ANTS. A case study, examples and simulations are illustrated.

Many combinatorial reliability models assume binary designation of states for both systems and their components. In many real applications, however, systems and their components may have more than two states (or levels of performance) varying from perfect operation to complete failure. In this paper, we present two combinatorial decision diagram based models that have been proposed for the analysis of multistate systems: multistate binary decision diagrams (MBDD) based approach and multistate multivalued decision diagrams (MMDD) based approach. And we conduct an empirical performance comparison between those two methods in terms of model size and computational complexity via two illustrative examples.

Grid computing is a newly emerging technology aimed at large-scale resource sharing and global-area collaboration. It is the next step in the evolution of parallel and distributed computing. Due to the largeness and complexity of the grid system, its performance and reliability are difficult to model, analyze, and evaluate. This paper presents a model that relaxes some assumptions made in prior research on distributed systems that were inappropriate for grid computing. The paper proposes a virtual tree-structured model of the grid service. This model simplifies the physical structure of a grid service, allows service performance (execution time) to be efficiently evaluated, and takes into account data dependence and failure correlation. Based on the model, an algorithm for evaluating the grid service time distribution and the service reliability indices is suggested. The algorithm is based on Graph theory and probability theory. Illustrative examples and a real case study of the BioGrid are presented.

Grid computing is a recently developed technology. Although the developmental tools and techniques for the grid have been extensively studied, grid reliability analysis is not easy because of its complexity. This paper is the first one that presents a hierarchical model for the grid service reliability analysis and evaluation. The hierarchical modeling is mapped to the physical and logical architecture of the grid service system and makes the evaluation and calculation tractable by identifying the independence among layers. Various types of failures are interleaved in the grid computing environment, such as blocking failures, time-out failures, matchmaking failures, network failures, program failures, and resource failures. This paper investigates all of them to achieve a complete picture about grid service reliability. Markov models, queuing theory, and graph theory are mainly used to model, evaluate, and analyze the grid service reliability. Numerical examples are illustrated

The paper considers a grid computing systems in which the resource management systems (RMS) can divide service tasks into execution blocks (EB), and send these blocks to different resources. To provide a desired level of service reliability, the RMS can assign the same EB to several independent resources for parallel (redundant) execution. According to the optimal schedule for service task partition, and distribution among resources, one can achieve the greatest possible expected service performance (i.e. least execution time), or reliability. For solving this optimization problem, the paper suggests an algorithm that is based on graph theory, Bayesian approach, and the evolutionary optimization approach. A virtual tree-structure model is constructed in which failure correlation in common communication channels is taken into account. Illustrative examples are presented.

In software reliability modeling, the parameters of the model are typically estimated from the test data of the corresponding component. However, the widely used point estimators are subject to random variations in the data, resulting in uncertainties in these estimated parameters. Ignoring the parameter uncertainty can result in grossly underestimating the uncertainty in the total system reliability. This paper attempts to study and quantify the uncertainties in the software reliability modeling of a single component with correlated parameters and in a large system with numerous components. Another characteristic challenge in software testing and reliability is the lack of available failure data from a single test, which often makes modeling difficult. This lack of data poses a bigger challenge in the uncertainty analysis of the software reliability modeling. To overcome this challenge, this paper proposes utilizing experts' opinions and historical data from previous projects to complement the small number of observations to quantify the uncertainties. This is done by combining the maximum-entropy principle (MEP) into the Bayesian approach. This paper further considers the uncertainty analysis at the system level, which contains multiple components, each with its respective model/parameter/ uncertainty, by using a Monte Carlo approach. Some examples with different modeling approaches (NHPP, Markov, Graph theory) are illustrated to show the generality and effectiveness of the proposed approach. Furthermore, we illustrate how the proposed approach for considering the uncertainties in various components improves a large-scale system reliability model.

Grid computing focuses on large-scale resource sharing. Using a general reliability model for grid computing to relax some impractical assumptions, a heuristic algorithm is presented to evaluate grid program/service reliability. The heuristic algorithm is based on two heuristic criteria that determine the significance of an entity and prune those insignificant ones. Through algorithm analysis, the heuristic algorithm is shown to have a linear complexity. This is much better than the previous algorithms, which are of exponential complexity. Another advantage of the heuristic algorithm is that the running time is controllable by adjusting the parameter of significant level (SL) and significant rate. A regression method is proposed to adjust the SL and predict the running time. Two examples are given

Generally, software reliability models can provide accurate reliability measurement in the later phase of testing. However, predictions in the early phase of software testing are useful as cost-effective and timely feedback. Early prediction is also feasible in practice with information from previous releases or similar projects. Such information has been utilized well for early reliability prediction with NHPP models by assuming the same failure rate between two similar projects. Alternatively, in this paper, we propose to "reuse" failure data from past projects/releases with ANN models to improve early reliability for current project/release. To illustrate the proposed approach, two numerical examples are developed. Better prediction performance is observed in early phase of testing compared with original ANN model without failure data reuse. Furthermore, the optimal switching point from proposed approach to original ANN model in the whole testing phase is studied, with specific analysis on the two examples

A swarm robotics system is a special type of wide-area and large-scale distributed system, which focuses on a group of robots cooperating to achieve the same goal using swarm intelligence. To treat the swarm robotics system with the self-healing and self-reproduction functions, this paper studied a prototype model based on the virtual neurons, autonomous self-diagnosis, consequence-oriented prescription, autonomous self-curing, and self-reproduction. This prototype system with self-healing has been implemented in the Trusted Electronics and Grid Obfuscation (TEGO) research center. Several practical cases were studied to show the effectiveness and efficiency of the model. The results demonstrate that the self-healing mechanism makes the system more reliable and the performance much improved, not only against failures, but also against failure propagations

The Internet and networks are not security-oriented by design so that myriad problems are compromising today's computer systems. This paper presented an autonomic security mechanism based on the virtual neurons and feature recognition. A prototype model of the virtual neuron is designed and the distributed virtual neurons are organized in a compound peer-to-peer and hierarchical structure. Then, the autonomic security mechanism is implemented via features recognized by the distributed virtual neurons. The paper presented how the feature recognition and virtual neurons work to automatically detect various security problems that are currently hard to defend against, including eavesdropping, replay, masquerading, spoofing, and DoS. A simulation system was developed and different cases were studied

Grid computing is a new emerging technology aiming at large-scale resource sharing, and global-area collaboration. It is a next step in an evolution of parallel and distributed computing. Due to the large scale and complexity of the grid system, its performance and reliability are difficult to model, analyse, and evaluate. This paper presents a model that relaxes some assumptions unsuitable for grid computing systems that have been made in the existed works studying the distributed systems. The paper proposes a virtual tree model of the grid service. This model simplifies the physical structure of a grid service, allows service performance (execution time) to be estimated, and takes into account the common cause failures in communication channels. Based on the model, an algorithm for evaluating the grid service performance distribution and the service reliability indices is suggested. The algorithm is based on graph theory, and Bayesian analysis. Illustrative examples are presented in which the results of the suggested algorithm are compared with simulation results.

The paper considers grid computing systems with star architectures in which the resource management system (RMS) divides service tasks into subtasks, and sends the subtasks to different specialized resources for execution. To provide the desired level of service reliability, the RMS can assign the same subtasks to several independent resources for parallel execution. Some subtasks cannot be executed until they have received input data, which can be the result of other subtasks. This imposes precedence constraints on the order of subtask execution. The service reliability & performance indices are introduced, and a fast numerical algorithm for their evaluation given any subtask distribution is suggested. Illustrative examples are presented

The rapidly increasing complexity of systems is driving the movement towards autonomic systems that are capable of managing themselves without the need for human intervention. Without autonomic technologies, many conventional systems suffer reliability degradation due to the accumulation of errors. The autonomic management techniques break the traditional reliability degradation trend. This paper comprehensively describes the roles and functions of various autonomic components, and systematically reviews past and current technologies that have been developed to address the specific areas of the autonomic computing environment. A new mechanism of model-driven autonomic management is further presented, which is more intelligent and efficient.

Grid is a type of large-scale distributed system. This paper develops a fast algorithm to efficiently evaluate the grid program/service reliability. It is shown to have a linearly increasing complexity. Compared to the previous exponential algorithms, it broadens the applicability of the generic model into large/complex grid service problems. Moreover, the running time of the fast algorithm is controllable. A regression method is proposed to predict and manage running time.

Most software reliability models assume independence of successive software runs. It is a strict assumption, and usually not valid in reality. Goseva-Popstojanova & Trivedi (2000) presented an interesting study on failure correlation among successive software runs. In this paper, by extending their results, a software reliability model is developed based on a Markov renewal process for the modeling of the dependence among successive software runs, where more than one type of failure is allowed in general formulation. Meanwhile, the cases of restarting with repair, and without repair, are considered. Although such a model is more complex than the traditional approach based on reliability growth, it incorporates more information about the failures, and system structure. A numerical example is also shown to illustrate the procedure, and provide some comparison.

Grid computing system is different from conventional distributed computing systems by its focus on large-scale resource sharing, where processors and communication have significant influence on grid computing reliability. Most previous research on conventional small-scale distributed systems ignored the communication time and processing time when studying the distributed program reliability, which is not practical in the analysis of grid computing systems. This paper describes the property of the grid computing systems and presents algorithms to analyze the grid program and system reliability.