Through exploiting decentralized data from multi-source Internet-of-Things (IoT) devices, federated learning (FL) can accomplish the training of deep neural network (DNN) models in a privacy-preserving manner to provide premium intelligent services. Due to portability considerations, most IoT devices are computing-constrained which cannot afford frequent DNN model training in FL. Existing approaches use model compression techniques to reduce computing cost of IoT devices, whereas accuracy degradation is inevitably incurred. To address this challenge, we propose an elastic federated learning collaboration framework, namely EFLCF, to accommodate limited computing resources of IoT devices. Specifically, we first design an FL-oriented elastic neural network model with multiple-width subnets, and couple it with an FL device-server collaboration framework to form EFLCF, thereby releasing computing cost pressure of IoT devices. We then develop a freezing-assisted wide-to-narrow training mechanism to realize efficient device-server distributed training and further reduce device computing cost. Finally, we design an entropy-based narrow-to-wide elastic inference mechanism to decrease computing cost of inference without compromising accuracy. Experiments demonstrate that compared to well-known benchmarks, our EFLCF can reduce up to 97.65% device computing cost and improve up to 48.3% accuracy in training, while reducing up to 42.5% computing cost in inference.

Humans are capable of inferring dynamic context from a still image and, with the provision of additional commonsense knowledge, can accurately complete visual commonsense reasoning tasks. Nevertheless, this remains a highly challenging cognitive-level task for current vision-language models. Previous work has primarily focused on utilizing models fine-tuned for specific downstream tasks and introduces external world knowledge to tackle these challenging tasks, while neglecting the importance of accurate context and the key role of commonsense knowledge in reasoning. In this paper, we propose a novel framework to enhance visual commonsense reasoning by incorporating context and commonsense knowledge. We decompose the visual commonsense reasoning problem into four distinct but interrelated sub-problems and combine visual language models with a large language model to enable zero-shot reasoning. The uniqueness of this work lies in the proposed commonsense knowledge filtering module, which filters out relevant commonsense knowledge through the causal strength of visual context. This process constructs Visual Context and Commonsense-guided Causal Chain-of-Thought ($\mathrm{VC^{3}}$-CoT) reasoning paths, thereby providing double robustness to visual commonsense reasoning by incorporating weighted majority voting strategy. Extensive experiments on several downstream tasks demonstrate that the proposed method significantly improves performance compared to baseline models and the state-of-the-art method, and confirm the effectiveness of the proposed components.

Federated learning (FL), an emerging data-secure distributed training paradigm, unites massive isolated Internet of Things (IoT) device nodes to collaboratively train a global neural network (NN) model without the exposure of their local multimedia data. However, constrained by the synchronous NN model integration nature of FL, there is a training latency inconsistency among heterogeneous devices, which significantly deteriorates FL training efficiency. Meanwhile, frequent local NN training and transmission impose high energy consumption pressure on users. To tackle these issues, this paper proposes a premium multi-width NN-assisted hierarchical FL (HFL) framework in heterogeneous cloud-edge-device computing to achieve remarkable training speedup and energy conservation. Specifically, a heterogeneity-aware NN width coefficient determination algorithm, which flexibly assigns a subnet with a suitable width to each user device based on its computing ability, is first applied to shorten the HFL training latency. Subsequently, to integrate subnets with different width topologies, we design a width-aware adaptive NN model integration approach to effectively ensure the accuracy of the integrated global NN model. Finally, a latency-aware energy saving strategy is introduced to reduce energy consumption. Experimental results demonstrate that our proposed framework outperforms state-of-the-art benchmarks, and attains up to 42.42% enhancement in accuracy, 81.5% reduction in training latency, and 40.9% optimization in energy cost.

Federated learning (FL) is envisioned as a pioneering framework for the distributed training of artificial intelligence models in mobile edge computing (MEC) environments. Traditional MEC-empowered FL approaches commonly neglect the inherent competition for computation resources between uncertain user-own tasks and FL training activities on individual Internet-of-Things (IoT) devices. Meanwhile, these approaches fail to address the personalized reward perception that dominates the active participation of IoT devices in FL training. As a result, both the resource utilization on IoT devices and the overall performance of FL models are significantly degraded in practical MEC systems. To address these challenges, this paper investigates the personalized FL with state-adaptive IoT device scheduling in MEC scenarios. We first develop a collaborative device-state estimation method to effectively capture the uncertainty in future states of FL candidates. Subsequently, we design a user-personality inspired degree-of-satisfaction (DoS) prediction scheme to quantify the impact of computation resource competition on the satisfaction levels of personalized FL participants. Building on these efforts, we propose a state-adaptive IoT device scheduling technique to optimize the accuracy of FL models at the offline stage. An FL runtime management policy is also designed to deal with the timing failures of unsuccessful return of local training results at the online stage. Evaluations show that our approach enhances the accuracy of WideResNet FL model by up to 35.96% on CIFAR-10 and EuroSAT datasets. Our source code is available at https://github.com/superguymj/ACE.

Deep learning as a service (DLaaS) that promotes deep learning-based applications by selling computing services from IT companies to end-users has introduced potential privacy leaks from users and cloud servers. Federated learning (FL) provides an emerging distributed paradigm that enables numerous users to collaboratively train deep-learning models while protecting user privacy and data security. However, many FL-related existing works only focus on improving communication bottlenecks due to frequent model parameter transmission, but ignore the performance degradation incurred by imbalanced user distribution and high inference latency due to the high complexity of deep-learning models in the emerging IoT-edge-cloud FL. In this paper, we propose an efficient user-distribution-aware hierarchical FL for communication-efficient training and fast inference in the IoT-edge-cloud DLaaS architecture. Specifically, we propose a user-distribution-aware hierarchical FL architecture to cope with the performance degradation owing to the imbalanced user distribution. The proposed architecture also features a lightweight deep neural network that adopts the designed lightweight fire modules as components and has a side branch for communication-efficient training and fast inference. Extensive experiments demonstrate that the proposed schemes significantly boost the accuracy by up to 67.12%, save 47.98% communication costs, and accelerate inference by up to 87.24$\boldsymbol{\times}$compared to benchmarking methods.

The market penetration of Infrastructure-as-a-Service (IaaS) in cloud computing is increasing benefiting from its flexibility and scalability. One of the most important issues for IaaS cloud service providers is to minimize the monetary cost while meeting cloud user experience requirements such as makespan and security. Prior works on cloud service cost minimization ignore either security or makespan which is very important for user experience. In this paper, we propose a two-stage algorithm to solve the cloud service cost minimization problem at the premise of satisfying the security and makespan requirements of cloud users. Specifically, in the first stage, we propose a novel security service selection scheme to ensure system security by judiciously selecting security services with low cost for tasks under the constraints of time and security. In the second stage, to further reduce the cloud service cost, we design a workflow scheduling method based on an improved firefly algorithm (IFA). The IFA-based method schedules cloud service workflows to virtual machines of small cost at the premise of guaranteeing security and makespan. It can quickly find the workflow scheduling solution with minimized cost using our designed updating scheme and mapping operator. Extensive simulations are conducted on real-world workflows to verify the efficacy of the proposed two-stage method. Simulation results show that the proposed two-stage method outperforms the baseline and two benchmarking methods in terms of cost minimization without violating security and time constraints. Compared to benchmarking methods, the cloud service cost can be reduced by up to 57.6% by using our proposed approach.

Federated learning (FL) is an attractive industrial paradigm to accomplish distributed artificial intelligence (AI) training collaboratively in a data privacy-preserving manner. Most existing designs for FL systems assume that industrial user equipments (UEs) participate voluntarily in FL training. However, since both AI model training and transmission consume considerable energy, UEs are reluctant to participate without economic rewards. Hence, the lack of proper economic reward incentive mechanism results in low UE utility and frustrates UEs' enthusiasm for participating in training. To address the above challenge, in this article, we propose a two-phase energy-aware reward incentive mechanism for the edge-cloud-assisted hierarchical federated learning (HFL) system to optimize the overall UE utility, thereby, incentivizing UEs to participate more actively. Specifically, at the cloud server phase, we design an energy quantity-aware incentive mechanism for reasonably distributing rewards to its sub-edge-assisted FL systems. Subsequently, at the edge server phase, based on the quantitative analysis for the optimal reward allocation solution, we develop an energy-aware water filling-based reward incentive mechanism to adapt to individual needs of UEs and maximize the overall UE utility. Experiments verify that, compared to well-known benchmarks, our incentive mechanism can improve the overall UE utility by up to 55.94% and better incentivize UEs to participate in training.

In the era of the Internet of Things (IoT), spatially distributed IoT devices collect and store data in a distributed fashion for computational efficiency. However, in IoT networks, due to the fragile device, harsh deployment environment, and unreliable transmission, the possibility of missing data is increasing, which may significantly affect subsequent data processing. Traditional approaches to impute missing data in IoT distributed data sets bring huge communication overheads. In this article, we develop an efficient architecture for distributed IoT data imputation based on a designed multidiscriminator conditional generative adversarial network. The architecture intelligently learns the characteristics of the distributed data sets to accurately impute missing values. Our experiments are performed using three data sets under two different data missing mechanisms. The experimental results demonstrate that using three data sets, the proposed imputation technique can drastically reduce the imputation error by up to 88.66%, 94.27%, and 95.53% at the premise of low transmission cost, respectively, compared to five state-of-the-art methods.

Artificial intelligence-based cyber-physical systems (CPS) applications have been spread across various fields such as smart cities, medical services, and industrial controls. When CPS devices are connected to a cloud server, big data streams generated by CPS devices impose enormous bandwidth pressure and exert excessive compute loads to the cloud server. Due to unpredictable environments and uncertainty in reality, these issues are mainly attributed to a large amount of high noise data captured and uploaded by CPS devices. To overcome these issues, this paper proposes a cyber-physical-cloud based framework for distributed deep neural networks (DDNNs) to prevent high noise data from being uploaded to the cloud. The proposed framework features a lightweight data filtering module enabled by depthwise separable convolutions to identify and filter out the high noise data that the cloud cannot recognize. Extensive experimental results demonstrate that the proposed data filtering module can achieve an accuracy of up to 83.72% in identifying high noise data and the proposed framework can effectively save bandwidth of up to 63.42% as compared to benchmarking methods. Note to Practitioners—This paper is motivated by the problems of enormous bandwidth pressure and excessive cloud compute loads in cyber-physical-cloud distributed computing paradigms. These problems are mainly caused by high noise data generated by CPS devices, because CPS devices often work in disturbing and unstable environments and there are uncontrollable uncertainties in reality. Especially for the emerging artificial intelligence-driven cyber-physical-cloud distributed paradigms, there is no existing research to solve the unnecessary transmission and cloud compute loads caused by high noise data. To tackle the challenge, this paper develops a novel cyber-physical-cloud distributed framework with data filtering capabilities to prevent high noise data from being uploaded. The proposed framework supports two popular loosely coupled and closely coupled distributed computing paradigms. Extensive experiments confirm that the proposed cyber-physical-cloud distributed framework can efficiently filter out high noise data and alleviate unnecessary transmission and needless cloud compute loads introduced by high noise data.

Federated learning (FL), an emerging distributed machine learning (ML) technique, allows massive embedded devices and a server to work together for training a global ML model without collecting user data on a server. Most existing approaches adopt the traditional centralized FL paradigm with a single server: one is the cloud-centric FL paradigm and the other is the edge-centric FL paradigm. The cloud-centric FL paradigm is able to manage a large-scale FL system across massive user devices with high communication cost, whereas the edge-centric FL paradigm is capable of coordinating a small-scale FL system benefiting from the low communication delay over wireless networks. To fully exploit the advantages of both, in this article, we develop a distinctive hierarchical FL framework for the promising mobile-edge cloud computing (MECC) system, called HELCHFL, to achieve high-efficiency and low-cost hierarchical FL training. In particular, we formulate the corresponding theoretical foundation for our HELCHFL to ensure hierarchical training performance. Furthermore, to address the inherent communication and user heterogeneity issues of FL training, our HELCHFL develops a utility-driven and heterogeneity-aware heuristic user selection strategy to enhance training performance and reduce training delay. Subsequently, by analyzing and utilizing the slack time in FL training, our HELCHFL introduces a device operating frequency determination approach to reduce training energy cost. Experiments demonstrate that our HELCHFL can enhance the highest accuracy by up to 52.93%, gain the training speedup of up to 483.74%, and obtain up to 45.59% training energy savings compared to state-of-the-art baselines.

Due to the increasing complexity, the execution of workflow applications on cloud typically involves a large number of virtual machines (VMs), which makes the cost as well as energy consumption a great concern. To alleviate this issue, more and more cloud service providers introduce new pricing policies considering Dynamic Voltage and Frequency Scaling (DVFS), where users are charged on the basis of allocated CPU frequencies together with various combinations of VM configurations and prices. However, the customizable CPU frequencies make resource provisioning and scheduling harder to achieve a cost-optimal solution. The things become even worse, since lowering CPU voltages of VMs will increase their chance of suffering soft errors, which results in a high rate of completion time failures of workflow applications. To address the above problem, this paper proposes a novel task scheduling method for the purpose of cost optimization based on the genetic algorithm. By introducing new genetic operators and frequency scaling scheme for DVFS-enabled cloud workflows, our approach can quickly figure out cost-optimal resource provisioning and task scheduling solutions by allocating tasks to appropriate VMs with specific operating frequencies under energy, reliability, makespan and memory constraints. Extensive experiments on various well-known scientific workflow benchmarks validate the effectiveness of the proposed method. Comparing with state-of-the-art methods, our approach can significantly reduce the overall cost and energy consumption without violating the given constraints.

Due to its unique privacy protection advantages, emerging federated learning (FL) is regarded as a significant technique to enable Industry 4.0. However, the industrial deployment of FL encounters the primary obstacles of limited device energy and system communication resources. Nowadays, renewable energy-powered devices have been deployed in various industrial fields to tackle the challenges of unsustainable and limited energy of battery-powered devices. Inspired by this, this article proposes a novel FL protocol to groundbreakingly improve the performance of renewable energy-powered FL systems. Specifically, with the underlying theory of FL as the guide, the proposed protocol features a reinforcement learning-based device scheduling solution to adapt to intermittent renewable energy supply. Following this device scheduling solution, an integer linear programming-based bandwidth management scheme is introduced to optimize communication efficiency. Experimental results on two representative data distribution situations demonstrate that compared with the state-of-the-art schemes, our FL protocol can boost up to 36.63% and 50.99% accuracy, respectively.

As the successor to Sunway TaihuLight, the new Sunway supercomputer has ultra-high computing capacity, but the unique heterogeneous architecture presents performance optimization challenges for High Performance Computing (HPC) applications. Performance modeling is an effective way to discover the performance bottlenecks and then improve the performance of HPC applications. Existing performance modeling techniques do not work well on large-scale HPC applications due to high overhead and low accuracy, and are not suitable for the heterogeneous architecture due to a lack of support for multi-resource parameters. To address the above challenges, we propose an automatic multi-parameter performance modeling method for HPC applications on the new Sunway supercomputer. First, a lightweight performance profiling method is proposed to achieve low overhead performance profiling. Then, performance models with multiple resource parameters based on the Fourier neural operator are built, achieving high prediction accuracy and generalization ability. Finally, the Fourier neural operator is extended on the new Sunway supercomputer to realize the performance modeling automatically. Experimental results show that the average prediction error is less than 10% and the average overhead is less than 4%, and the results are superior to the baselines.

Emerging iris recognition techniques are highly dependent on high-resolution iris images. However, existing iris recognition methods cannot effectively extract local texture features in low-resolution application scenarios, resulting in low recognition accuracy below expected. In this paper, we propose DBANet, a novel dual branch attention-based deep neural network for biological iris recognition that can achieve high accuracy for both high and low-resolution images. Specifically, we first design a spatial feature module with a small stride to preserve lower-level spatial detail features. Then, since the high-level feature can provide rich global context information, we propose a context feature module to generate high-level features. Finally, we develop a novel spatial attention module to fuse features generated by the above modules. We conduct the experiments on UBIRIS. v2, CASIA-V4-Distance, and MICHE-I datasets. Experimental results show that as compared to state-of-art methods, our proposed method can reduce equal error rates by up to 38.7%, 53.4%, and 71.9%, respectively.

Federated Learning (FL), an emerging distributed machine learning (ML), empowers a large number of embedded devices (e.g., phones and cameras) and a server to jointly train a global ML model without centralizing user private data on a server. However, when deploying FL in a mobile-edge computing (MEC) system, restricted communication resources of the MEC system, heterogeneity and constrained energy of user devices have a severe impact on FL training efficiency. To address these issues, in this article, we design a distinctive FL framework, called HELCFL, to achieve high-efficiency and low-cost FL training. Specifically, by analyzing the theoretical foundation of FL, our HELCFL first develops a utility-driven and greedy-decay user selection strategy to enhance FL performance and reduce training delay. Subsequently, by analyzing and utilizing the slack time in FL training, our HELCFL introduces a device operating frequency determination approach to reduce training energy costs. Experiments verify that our HELCFL can enhance the highest accuracy by up to 43.45 %, realize the training speedup of up to 275.03%, and save up to 58.25% training energy costs compared to state-of-the-art baselines.

Distributed machine learning such as split learning can train a model using data on mobile devices while protecting privacy. However, its training time is impractical when the number of devices is large, and the model performance will decrease greatly if data are non-iid. To solve these problems, an efficient parallel split learning algorithm is proposed. Specifically, the parallel algorithm with a distillation loss function instead of parameter synchronization reduces the training time without losing the accuracy. And an incentive mechanism based on Stackelberg Game is designed to adapt to the training environment with non-iid mobile data. The experiments on the CIFAR-10 dataset demonstrate the superior performance of the proposed algorithm in terms of training time and model accuracy.

Federated learning (FL) offers a promising paradigm that empowers numerous Internet of Things (IoT) devices to implement distributed learning on the premise of ensuring user privacy and data security. However, since FL adopts a synchronous distributed training mode, the heterogeneity of participating IoT devices and limited communication resources make FL encounter serious issues of low training efficiency in actual deployment. In this article, we propose an excellent FL policy for the heterogeneous IoT-edge FL system to improve distributed training efficiency. Specifically, first, by borrowing the idea of clustering, we explore an iterative self-organizing data analysis techniques algorithm (ISODATA)-based heterogeneous-aware client scheduling strategy to alleviate the issue of low training efficiency incurred by the heterogeneity of clients. Subsequently, to tackle the challenge of limited communication resources in FL, we first analyze the characteristics of the optimal resource block allocation solution theoretically and then introduce a mixed-integer linear programming (MILP)-based strategy to judiciously allocate resource blocks for scheduled clients. Comprehensive experimental results demonstrate that, compared with benchmarking strategies, our proposed FL policy can achieve up to 55.22% accuracy improvement in a relaxed time scenario, and attain up to$3.62\times $acceleration for reaching the specific expected accuracy.

In-situ(InS) server systems are typically deployed in special environments to handleInSworkloads which are generated from environmentally sensitive areas or remote places lacking modern power supply infrastructure. This special operating environment ofInSservers urges such systems to be powered by renewable energy. In addition, theInSsystems are vulnerable to soft errors due to the harsh environments they deploy. This article tackles the problem of allocating harvested energy to renewable powered servers and assigning theInSworkloads to these servers for optimizing throughput of both the overall system and individual servers under energy and reliability constraints. We perform the energy allocation based on system state. In particular, for systems in low energy state, we propose a game theoretic approach that models the energy allocation as a cooperative game among multiple servers and derives a Nash bargaining solution. To meet the reliability constraint, we analyze the reliability optimality of assigning tasks to multiple servers and design a reliability-aware task assignment heuristic based on the analysis. Experimental results show that with a small time overhead, the proposed energy allocation approach achieves a high throughput from perspectives of both the overall system and individual servers, and the proposed task assignment approach ensures an increased system reliability.

The main goal of a cloud provider is to make profits by providing services to users. Existing profit optimization strategies employ homogeneous user models in which user personality is ignored, resulting in fewer profits and particularly notably lower user satisfaction that in turn, leads to fewer users and reduced profits. In this article, we propose efficient personality-aware request scheduling schemes to maximize the profit of the cloud provider under the constraint of user satisfaction. Specifically, we first model the service requests at the granularity of individual personality and propose a personalized user satisfaction prediction model based on questionnaires. Subsequently, we design a personality-guided integer linear programming (ILP)-based request scheduling algorithm to maximize the profit under the constraint of user satisfaction, which is followed by an approximate but lightweight value assessment and cross entropy (VACE)-based profit improvement scheme. The VACE-based scheme is especially tailored for applications with high scheduling resolution. Extensive simulation results show that our satisfaction prediction model can achieve the accuracy of up to 83 percent, and our profit optimization schemes can improve the profit by at least 3.96 percent as compared to the benchmarking methods while still obtaining a speedup of at least 1.68x.

As an efficient commercial computing paradigm, cloud computing provides various computing and storage resources to users in a pay-as-you-go manner. However, existing cloud pricing models and mechanisms are deterministic to some degree, thus, may not work well in a real-world environment where user perceived values with respect to cloud services are dynamically changing and highly personalized. In this article, we develop a reinforcement learning (RL)-based dynamic cloud pricing scheme to optimize both cloud provider’s profit and costs of heterogeneous users with distinct personalities. Specifically, we first propose a novel personality-guided user perceived value prediction scheme to proactively capture the dynamics of the users’ perceived values with respect to cloud services. The prediction scheme models the relationship among user personality, service price, quality of service (QoS), user satisfaction and perceived value in the cloud service market. Second, on the basis of the prediction model, a RL-based cloud pricing mechanism is developed to learn sequential service pricing decision-making for profit and costs optimization. Particularly, the profit and costs optimization problem is modeled as a discrete-time Markov decision process (MDP) that is solved by using Q-learning. Finally, extensive simulation experiments have been conducted to verify our user perceived value prediction scheme and RL-based cloud service pricing mechanism. Simulation results show that our perceived value prediction scheme can achieve up to 87.50 percent prediction accuracy, and our RL-based pricing mechanism can obtain up to 19.39 percent more profit than the state-of-the-art scheme.

Cloud storage services give users a great facility in data management such as data collection, storage and sharing, but also bring some potential security hazards. An utmost importance is how to ensure the integrity of data files stored in the cloud, particular for user groups without trusted managers. Existing literature focuses on integrity checking for groups with managers who have lots of permissions. To overcome the shortage of public auditing for non-manager user groups in clouds, we develop a novel framework IPANM that integrates$(t,n)$threshold technology, blinding technology, and incentive mechanism to realize an incentive privacy-preserving public auditing scheme. In IPANM, the data integrity is guaranteed by our$(t,n)$threshold signature based public auditing and the data privacy during public auditing is protected by the blinding technology. The generation of signatures can be accelerated by our blockchain-aided incentive mechanism that mobilizes the initiative of signers in the signature generation by rewarding the contributed signers. We formally prove the security of our IPANM and conduct numerical analysis and evaluation study to validate its high efficiency. The experimental results demonstrate that IPANM has lower overheads of storage, communication, and computation as compared to the state-of-the-art technique IAID-PDP and NPP.

As an efficient commercial information technology, cloud computing has attracted more and more users and enterprises to use it. Faced with such a large number and variety of customers, it is necessary for cloud providers (CPs) with limited budget to provide satisfactory customized pricing services, profitable customer and system investments, and flexible system resource provisioning strategies to improve both customer experience and long-term profit. Existing profit optimization research rarely considers customer diversity and dynamics, which may have a negative impact on long-term profit growth due to poor management of customer relations. In this article, we implement customer relationship management by considering both customer diversity and dynamics, and propose a customer adaptive resource provisioning scheme to maximize long-term profit under constrained budget. We consider four customer types (i.e., loyal, old, new, and lost) that can transition to each other during the customer's lifetime of interaction with the CP. The CP builds multiple cloud service sub-platforms, each of which contains multiple multiserver systems and serves the same type of customers. For the cloud service platform, we first analyze single multiserver system using an analytical method to obtain its optimal profit, invested funding, and system configuration. In particular, for systems serving new and lost customers, we develop a novel customer lifetime value (CLV)-based customer investment scheme that selects valuable customers for investment under limited marketing budget. Based on the above analysis, we then present a customer retention rate (CRR)-driven three-stage heuristic scheme that prioritizes investment in multiserver systems with endangered customers under limited infrastructure budget for reducing customer churn and promoting long-term profit growth. We conduct extensive simulation experiments to validate the effectiveness of our method. Simulation results show that compared with the benchmark algorithms, our method can improve the long-term profit and CRR by up to 3.4x and 7.8x, respectively.

Although Reinforcement Learning (RL) has been successfully applied in traffic control, it suffers from the problems of high average vehicle travel time and slow convergence to optimized solutions. This is because, due to the scalability restriction, most existing RL-based methods focus on the optimization of individual intersections while the impact of their cooperation is neglected. Without taking all the correlated intersections as a whole into account, it is difficult to achieve global optimization goals for complex traffic scenarios. To address this issue, this paper proposes a novel federated reinforcement learning approach named FedLight to enable optimal signal control policy generation for multi-intersection traffic scenarios. Inspired by federated learning, our approach supports knowledge sharing among RL agents, whose models are trained using decentralized traffic data at intersections. Based on such model-level collaborations, both the overall convergence rate and control quality can be significantly improved. Comprehensive experimental results demonstrate that compared with the state-of-the-art techniques, our approach can not only achieve better average vehicle travel time for various multi-intersection configurations, but also converge to optimal solutions much faster.

Abstract In recent years, the advance in information technology has promoted a wide span of emerging cyber‐physical systems (CPS) applications such as autonomous automobile systems, healthcare monitoring, and process control systems. For these CPS applications, service latency management is extraordinarily important for the sake of providing high quality‐of‐experience to terminal users. Edge‐cloud computing, integrating both edge computing and cloud computing, is regarded as a promising computation paradigm to achieve low service latency for terminal users in CPS. However, existing latency‐aware edge‐cloud computing methods dedicated for CPS fail to jointly consider energy budgets and reliability requirements, which may greatly degrade the sustainability of CPS applications. In this article, we explore the problem of minimizing service latency of edge‐cloud computing coupled CPS under the constraints of energy budgets and reliability requirements. We propose a two‐stage approach composed of static and dynamic service latency optimization. At static stage, Monte‐Carlo simulation with integer‐linear‐programming technique is adopted to find the optimal computation offloading mapping and task backup number. At dynamic stage, a backup‐adaptive dynamic mechanism is developed to avoid redundant data transmissions and executions for achieving additional energy savings and service latency enhancement. Experimental results show that our solution is able to reduce system service latency by up to 18.3% compared with representative baseline solutions.

Modern software systems, either system or application software, are increasingly being developed on top of virtualized software platforms. They may simply intend to execute on virtual machines or they may be expected to port to physical machines eventually. In either case, the devices, virtual or silicon, in the target virtual or physical machines are expected to conform to the specifications based on which the software systems have been developed. Non-conformance of these devices to the specifications can cause catastrophic failures of the software systems. In this article, we propose a mutation-based framework for effective and efficient conformance checking between virtual/silicon device implementations and their specifications. Based on our defined mutation operators, device specifications can be automatically instrumented with weak mutant-killing constraints to model potential erroneous device behaviors. To kill all feasible mutants, our approach adopts a cooperative symbolic execution mechanism that can efficiently automate the test case generation and conformance checking for virtual/silicon devices. By symbolically executing the instrumented specifications with virtual/silicon device traces obtained from the cooperative execution, our method can accurately measure whether the designs have been sufficiently validated and report the inconsistencies between device specifications and implementations. Comprehensive experiments on two industrial network adapters and their virtual devices demonstrate the effectiveness of our proposed approach in conformance checking for both virtual and silicon devices.

As the density of integrated circuits continues to increase, the possibility that real-time systems suffer from soft and hard errors rises significantly, resulting in a degraded availability of system. In this article, we investigate the dynamic modeling of cross-layer soft error rate based on the Back Propagation (BP) neural network, and propose optimization strategies for system availability based on Cross Entropy (CE) and Q-learning algorithms. Specifically, the BP neural network is trained using cross-layer simulation data obtained from SPICE simulation while the optimization for system availability is achieved by judiciously selecting an optimal supply voltage for processors under timing constraints. Simulation results show that the CE-based method can improve system availability by up to 32 percent compared to state-of-the-art methods, and the Q-learning-based algorithm can further enhance system availability by up to 20 percent compared to the proposed CE-based method.

As a promising method for central model training on decentralized device data without compromising user privacy, federated learning (FL) is becoming more and more popular in Internet-of-Things (IoT) design. However, due to limited computing and memory resources of devices that restrict the capabilities of hosted deep learning models, existing FL approaches for artificial intelligence IoT (AIoT) applications suffer from inaccurate prediction results. To address this problem, this article presents a collaborativeBig.Littlebranch architecture to enable efficient FL for AIoT applications. Inspired by the architecture of BranchyNet which has multiple prediction branches, our approach deploys deep neural network (DNN) models across both cloud and AIoT devices. OurBig.Littlebranch model has two branches, where the big branch is deployed on cloud for strengthened prediction accuracy, and the little branches are used to fit for AIoT devices. When AIoT devices cannot make the prediction with high confidence using local little branches, they will resort to the big branch for further inference. To increase both prediction accuracy and early exit rate ofBig.Littlebranch model, we propose a two-stage training and coinference scheme, which considers the local characteristics of AIoT scenarios. Comprehensive experiment results obtained from a real AIoT environment demonstrate the efficiency and effectiveness of our approach in terms of prediction accuracy and average inference time.

In the past few years, the study on placing edge servers for response time optimization in mobile edge-cloud computing systems has become increasingly popular. Most of the existing schemes neglect two important aspects: one is the heterogeneity of edge/cloud servers and the other is the response time fairness of base stations, which may significantly degrade the system quality of services to mobile users. In this article, we conduct the study of deploying heterogeneous edge servers to optimize the expected response time of both the whole and individual base stations. We propose an approach consisting of offline and online stages. At the offline stage, the optimal placement strategy of heterogeneous edge servers is produced by using an integer linear programming technique. At the online stage, a mobility-aware game-theory-based method is developed to deal with the dynamic characteristic of user movement. Experimental results reveal that compared to benchmarking methods, our approach not only reduces system-expected response time by 47.37%, but also improves response time fairness of base stations by 71.60%.

Along with the proliferation of artificial intelligence and Internet of things (IoT) techniques, various kinds of adversarial attacks are increasingly emerging to fool deep neural networks (DNNs) used by industrial IoT (IIoT) applications. Due to biased training data or vulnerable underlying models, imperceptible modifications on inputs made by adversarial attacks may result in devastating consequences. Although existing methods are promising in defending such malicious attacks, most of them can only deal with limited existing attack types, which makes the deployment of large-scale IIoT devices a great challenge. To address this problem, in this article, we present an effective federated defense approach named FDA3that can aggregate defense knowledge against adversarial examples from different sources. Inspired by federated learning, our proposed cloud-based architecture enables the sharing of defense capabilities against different attacks among IIoT devices. Comprehensive experimental results show that the generated DNNs by our approach can not only resist more malicious attacks than existing attack-specific adversarial training methods, but also prevent IIoT applications from new attacks.

Sunway TaihuLight system is the first supercomputer offering a peak performance over 100 PFlops, which can be utilized to parallelize Non-dominated Sorting Genetic Algorithm II (NSGA-II), a standard approach to multi-objective optimization. However, insufficient off-chip memory bandwidth and limited scratchpad memory capacity of the supercomputer hinder the performance improvement of parallellizing NSGA-II. In this article, we propose an optimized parallel NSGA-II on Sunway TaihuLight system, called swNSGA-II, by utilizing process- and thread-level parallelism of the system based on an improved island/master-slave model. To overcome the hurdles of low memory bandwidth and capacity, we propose a data sharing scheme based on register-level communication that can efficiently parallelize non-dominated sorting and crowding-distance computation of NSGA-II. Several optimization techniques including vectorization, direct memory accessing, and double buffering are also adopted to further accelerate swNSGA-II. Experiment results show that the proposed swNSGA-II can achieve a speedup of 41284 on a use case of path planning, and a speedup of 62692 on ZDT1 as compared to conventional NSGA-II.

Convolutional Neural Networks (CNNs) are becoming popular in Internet-of-Things (IoT) based object tracking areas, e.g., autonomous driving, commercial surveillance, and intelligent traffic management. However, due to limited processing power of embedded devices and network bandwidth, how to simultaneously guarantee fast object tracking with high accuracy and low energy consumption is still a major challenge, which makes IoT-based vision applications unreliable and unsustainable. To address this problem, this article proposes a collaborative edge-cloud architecture that resorts to cloud for object tracking performance enhancement. By properly offloading computations to cloud and periodically checking tracking status of edge devices through convolutional Siamese networks, our novel edge-cloud architecture enables interactive collaborations between edge devices and cloud servers in order to quickly and accurately rectify tracking errors. Comprehensive experimental results on well-known video object tracking benchmarks show that our architecture can not only significantly improve the performance of object tracking, but also can save the energy consumption of edge devices.

Data mining techniques have been widely applied to various fields including industrial, business, and governmental applications. Missing data is a common occurrence in a number of real-world databases, which may substantially affect the accuracy of data processing. In this paper, we propose a novel approach for missing data estimation by efficiently exploring inter-sensor correlation. Namely, given multiple sensors for data collection, we attempt to recover the missing data of a few sensors by using the measurement data from other sensors. Towards this goal, we develop an iterative solver for missing data estimation. Our numerical experiments on two industrial datasets demonstrate that the proposed method can reduce the imputation error by up to 7.25× compared to a conventional method in the literature.

Streaming applications come from various application fields such as physics, and many can be represented as a series-parallel dependence graph. We aim at minimizing the energy consumption of such applications when executed on a hierarchical platform, by proposing novel mapping strategies. Dynamic voltage and frequency scaling (DVFS) is used to reduce the energy consumption, and we ensure a reliable execution by either executing a task at maximum speed, or by triplicating it. In this paper, we propose a structure rule to partition the series-parallel applications, and we prove that the optimization problem is NP-complete. We are able to derive a dynamic programming algorithm for the special case of linear chains, which provides an interesting heuristic and a building block for designing heuristics for the general case. The heuristics performance is compared to a baseline solution, where each task is executed at maximum speed. Simulations demonstrate that significant energy savings can be obtained.

Fog computing is an emerging architectural paradigm for the implementation of the Internet of Things, where computation moves from cloud servers to network edges. Fog computing systems are with three characteristics: 1) low latency; 2) strong presence of real-time applications; and 3) reusability of end devices. Most existing designs of fog computing systems concentrate on reducing application processing latency, but neglect real-time requirements of applications and reusability of end devices, which may drastically degrade both functionality and quality-of-service (QoS) of applications. In this article, we investigate QoS optimization of real-time applications in fog computing systems equipped with reusable end devices and powered by hybrid energy of renewable generations and grid electricity. We propose a renewable-adaptive computation offloading approach. At the end device layer, local energy allocation schemes are designed at the application-level and component-level, where techniques of the cooperative game and mixed-integer linear programming (MILP) are leveraged, respectively. At the fog layer, the local energy allocation method is augmented to a local-remote scheduling solution by judiciously judging whether or not the computation offloading of an application needs to be triggered. The experimental results demonstrate that compared to benchmarking algorithms, our approach improves the overall and individual application QoS by up to 101.93% and 59.30%, respectively.

3-D multiprocessor system-on-chip (MPSoC) systems can offer higher integration density, lower interaction cost, better bandwidth, and greater performance. However, vertically stacked silicon layers and limited heat dissipation paths result in high peak temperature and large temperature variation, which incur reliability reduction, lifetime decay, and performance degradation. In this article, we propose an offline augmented cross-entropy (CE)-based task scheduling strategy to jointly optimize peak temperature and temperature variation under the constraint of timeliness. Specifically, based on the conventional CE method, a heuristic iterative sampling method is designed to explore task-to-core assignment for balanced heat distribution between the top-layer and the bottom-layer cores. Subsequently, thermal characteristics of 3-D MPSoC systems are used to judiciously swap tasks between the two layers to improve the conventional CE-based task assignment and accelerate the iterative process. The peak temperature of individual cores is further reduced via sequencing, splitting, and slacking task execution. The experimental results demonstrate that compared to the existing state-of-the-art methods, the proposed scheme can reduce peak temperature by up to 8.02 °C and temperature variation by up to 24.78% without violating the timeliness of tasks.

Due to the merits of intuitive and visual modeling of design requirements, unified modeling language (UML) sequence diagrams are widely used as scenario-based specifications in the design of cyber-physical systems (CPSs). However, when more and more CPS products are deployed within an uncertain environment, existing sequence diagram analysis approaches cannot be used to accurately capture and quantify their timing behaviors at an early design stage. To address this problem, this article extends UML sequence diagrams to allow the modeling of stochastic system inputs, message processing time, and network delays, which strongly affect the system timing behaviors. We develop a statistical model checking-based framework that can automatically convert stochastic sequence diagrams into networks of priced timed automata to enable the quantitative analysis under various performance queries. The experimental results of two industrial designs in the railway field demonstrate the effectiveness of our approach.

Along with the wide deployment of infrastructures and the rapid development of virtualization techniques in cloud computing, more and more enterprises begin to adopt cloud services, inspiring the emergence of various cloud service providers. The goal of cloud service providers is to pursue profit maximization. To achieve this goal, cloud service providers need to have a good understanding of the economics of cloud computing. However, the existing pricing strategies rarely consider the interaction between user requests for services and the cloud service provider and hence cannot accurately reflect the supply and demand law of the cloud service market. In addition, few previous pricing strategies take into account the risk involved in the pricing contract. In this article, we first propose a dynamic pricing strategy that is developed based on the customer perceived value (CPV) and is able to accurately capture the real situation of supply and demand in marketing. The strategy is utilized to estimate the user's demand for cloud services. We then design a profit maximization scheme that is developed based on the CPV-aware dynamic pricing strategy and considers the risk in the pricing contract. The scheme is utilized to derive the optimal multiserver configuration for maximizing the profit. Extensive simulations are carried out to verify the proposed customer perceived value and risk-aware profit maximization scheme. As compared to two state of the art benchmarking methods, the proposed scheme gains 31.6 and 30.8 percent more profit on average, respectively.

SQL injection attack (SQLIA) is among the most common security threats to web-based services that are deployed on cloud. By exploiting web software vulnerabilities, SQL injection attackers can run arbitrary malicious code on target databases to acquire or compromise sensitive data. Although web application firewalls (WAFs) are offered by most cloud service providers, tenants are reluctant to pay for them, since there are few approaches that can report accurate SQLIA statistics for their deployed services. Traditional WAFs focus on blocking suspicious SQL requests. Few of them can accurately decide whether an attack is really harmful and quickly answer how severe the attack is. To raise the tenants' awareness of the seriousness of SQLIAs, in this paper, we introduce a novel traffic-based SQLIA detection and vulnerability analysis framework named DIAVA, which can proactively send warnings to tenants promptly. By analyzing the bidirectional network traffic of SQL operations and applying our proposed multilevel regular expression model, DIAVA can accurately identify successful SQLIAs among all the suspects. Meanwhile, the severity of such SQLIAs and the vulnerabilities of the corresponding leaked data can be quickly evaluated by DIAVA based on its GPU-based dictionary attack analysis engine. Experimental results show that DIAVA not only outperforms state-of-the-art WAFs in detecting SQLAs from the perspectives of precision and recall, but also enables real-time vulnerability evaluation of leaked data caused by SQL injection.

Internet of Things (IoT) devices, such as intelligent road side units and video-based detectors, are being deployed in emerging applications like sustainable and intelligent transportation systems. The primary obstacles against the development of these IoT devices are various security threats and huge energy consumption. In this article, we study the problem of scheduling tasks onto a heterogeneous multiprocessor system on a chip (MPSoC) deployed in IoT for optimizing quality of security under energy, real-time, and task precedence constraints. We first provide a mixed-integer linear programming (MILP) formulation for allocating and scheduling dependent tasks with energy and real-time constraints on a heterogeneous MPSoC system to maximize system quality of security. In order to efficiently solve the formulated MILP, we then propose an analysis-based two-stage scheme that determines the allocation, operating frequency, and security service of tasks to maximize system quality of security while satisfying the design constraints. We finally carry out extensive simulation experiments to validate our proposed two-stage scheme and MILP approach. Simulation results demonstrate that the proposed two-stage scheme outperforms a number of representative existing approaches in saving energy and improving system quality of security. The results also show that the proposed MILP approach can achieve the best performance and the proposed two-stage scheme has a close performance to the MILP approach.

Software Defined Networking (SDN) provides flexibility and programmability for network management by using a layered structure composed of data plane, control plane, and application plane. A key enabling technique for the sustainability of SDN-based network infrastructure is the modeling of power consumed by SDN control planes. However, power modeling of control planes is not extensively investigated yet, and no generic methods have been developed for performance and power comparison of sustainable SDN control planes. In this paper, we propose analytical performance and power models for different network controllers by using queuing theory, and design a generic framework for performance and power evaluation of different sustainable SDN control planes. Extensive simulation results show that the proposed solution can precisely model the power and performance of the concerned SDN control planes such that different control planes can be benchmarked under a general framework, which enables the identification of suitable control planes for various SDN network applications.

The Clock Constraint Specification Language (CCSL) has been widely investigated in verifying causal and temporal timing behaviors of real-time embedded systems. However, due to limited expertise in formal modeling, it is difficult for requirement engineers to completely and accurately derive CCSL specifications from natural language-based design descriptions. To address this problem, we present a novel approach that facilitates automated synthesis of CCSL specifications under the guidance of sampled (expected) timing behaviors of target systems. By encoding sampled behaviors and incomplete CCSL constraints provided by requirement engineers using our proposed transformation templates, the CCSL specification synthesis problem can be naturally converted into a SKETCH synthesis problem, which enables the automated generation of CCSL specifications with high accuracy. Experiments on both well-known benchmarks and synthetic examples demonstrate the effectiveness and scalability of our approach.

As the density of integrated circuits continues to increase, the possibility that real-time systems suffer from transient and permanent failures rises significantly, resulting in a degraded availability of system functionality. In this paper, we investigate the dynamic modeling of transient failure rate based on Back Propagation (BP) neural network, and propose an optimization strategy for system availability based on Cross Entropy (CE). Specifically, the neural network is trained using cross-layer simulation data obtained from SPICE simulation while the CE-based optimization for system functionality availability is achieved by judiciously selecting an optimal supply voltage for processors under timing constraints. Simulation results show that the proposed method can achieve system availability improvement of up to 32% compared to benchmarking methods.

Benefiting from rich resources and virtualization technologies, hybrid cloud has emerged as a promising solution to processing large-scale scientific workflow applications for users in a pay-as-you-go manner. However, considering the complexity of resource configuration and deployment in hybrid clouds, existing workflow scheduling strategies designed for traditional distributed computing systems are limited and powerless. Therefore, for profit-driven infrastructure-as-a-service (IaaS) cloud providers, minimizing makespan and monetary cost of scheduling scientific workflows is an imperative concern. In this paper, we propose two efficient workflow scheduling approaches for hybrid clouds that both consider makespan and monetary cost. Specifically, we first propose a single-objective workflow scheduling optimization approach called DCOH (deadline-constrained cost optimization for hybrid clouds) for minimizing the monetary cost of scheduling workflows under deadline constraint. Based on DCOH, we further propose a multi-objective workflow scheduling optimization approach called MOH (multi-objective optimization for hybrid clouds) for optimizing makespan and monetary cost of scheduling workflows simultaneously. Extensive simulation experiments have been conducted to validate the effectiveness of DCOH and MOH. Simulation results show that our DCOH approach can reduce up to 100.0% monetary cost for users as compared to the competing algorithms under the same deadline constraint and our MOH approach can achieve better cost-makespan trade-off solutions as compared to the competing algorithms.

CMOS scaling has greatly increased concerns for both lifetime reliability due to permanent faults and soft-error reliability due to transient faults. Most existing works only focus on one of the two reliability concerns, but often times techniques used to increase one type of reliability may adversely impact the other type. A few efforts do consider both types of reliability together and use two different metrics to quantify the two types of reliability. However, for many systems, the user's concern is to maximize system availability by improving the mean time to failure (MTTF), regardless of whether the failure is caused by permanent or transient faults. Addressing this concern requires a uniform metric to measure the effect due to both types of faults. This paper introduces a novel analytical expression for calculating the MTTF due to transient faults. Using this new formula and an existing method to evaluate system MTTF, we tackle the problem of maximizing availability for multicore real-time systems with consideration of permanent and transient faults. A framework is proposed to solve the system availability maximization problem. Experimental results on a hardware board and simulation results of synthetic tasks show that our scheme significantly improves system MTTF (and hence availability) compared with existing techniques.

In recent years, the Internet of Things (IoT) has promoted many battery-powered emerging applications, such as smart home, environmental monitoring, and human healthcare monitoring, where energy management is of particular importance. Meanwhile, there is an accelerated tendency toward mobility of IoT devices, either being transported by humans or being mobile by itself. Existing energy management mechanisms for battery-powered IoT fail to consider the two significant characteristics of IoT: 1) the approximate real-time computation and 2) the mobility of IoT devices, resulting in unnecessary energy waste and network lifetime decay. In this paper, we explore mobility-aware network lifetime maximization for battery-powered IoT applications that perform approximate real-time computation under the quality-of-service (QoS) constraint. The proposed scheme is composed of offline and online stages. At offline stage, an optimal mobility-aware task schedule that maximizes network lifetime is derived by using mixed-integer linear programming technique. Redundant executions due to mobility-incurred overlapping of a single task on different IoT devices are avoided for energy savings. At online stage, a performance-guaranteed and time-efficient QoS-adaptive heuristic based on cross-entropy method is developed to adapt task execution to the fluctuating QoS requirements. Extensive simulations based on synthetic applications and real-life benchmarks have been implemented to validate the effectiveness of our proposed scheme. Experimental results demonstrate that the proposed technique can achieve up to 169.52% network lifetime improvement compared to benchmarking solutions.

With the advent of heterogeneous multiprocessor architectures, efficient scheduling for high performance has been of significant importance. However, joint considerations of reliability, temperature, and stochastic characteristics of precedence-constrained tasks for performance optimization make task scheduling particularly challenging. In this paper, we tackle this challenge by using an affinity (i.e., probability)-driven task allocation and scheduling approach that decouples schedule lengths and thermal profiles of processors. Specifically, we separately model the affinity of a task for processors with respect to schedule lengths and the affinity of a task for processors with regard to chip thermal profiles considering task reliability and stochastic characteristics of task execution time and intertask communication time. Subsequently, we combine the two types of affinities, and design a scheduling heuristic that assigns a task to the processor with the highest joint affinity. Extensive simulations based on randomly generated stochastic and real-world applications are performed to validate the effectiveness of the proposed approach. Experiment results show that the proposed scheme can reduce the system makespan by up to 30.1% without violating the temperature and reliability constraints compared to benchmarking methods.

EtherCAT has become one of the leading real-time Ethernet solutions for networked industrial systems, where a reliable communication infrastructure is needed due to highly error-prone environments. However, existing work on EtherCAT mainly focuses on clock synchronization and timeliness improvement. The reliability of EtherCAT-based networked systems has largely been ignored. In this paper, we present a proportional integral derivative (PID)-based feedback control scheme that aims at enhancing reliability of networked systems under timing and system resource constraints. Instead of retransmitting data upon error detection, we use forward error control technique based on inequality of arithmetic and geometric means to achieve the required system reliability at a low deadline miss rate of messages. We further optimize the forward error control technique and design a fast and fair error resilient mechanism by using a cooperative game. In addition to reliability enhancement, our PID-based error control scheme can also improve the stability of a system in terms of deadline miss rate in the presence of burst errors. Simulation results show that the proposed scheme can achieve reliability enhancement of up to 91% compared to benchmarking methods.

Multiprocessor system-on-chip (MPSoC) has been widely used in many real-time embedded systems where both soft-error reliability (SER) and lifetime reliability (LTR) are key concerns. Many existing works have investigated them, but they focus either on handling one of the two reliability concerns or on improving one type of reliability under the constraint of the other. These techniques are thus not applicable to maximize SER and LTR simultaneously, which is highly desired in some real-world applications. In this paper, we study the joint optimization of SER and LTR for real-time MPSoCs. We propose a novel static task scheduling algorithm to simultaneously maximize SER and LTR for real-time homogeneous MPSoC systems under the constraints of deadline, energy budget, and task precedence. Specifically, we develop a new solution representation scheme and two evolutionary operators that are closely integrated with two popular multiobjective evolutionary optimization frameworks, namely NSGAII and SPEA2. Extensive experimental results on standard benchmarks and synthetic applications show the efficacy of our scheme. More specifically, our scheme can achieve significantly better solutions (i.e., LTR-SER tradeoff fronts) with remarkably higher hypervolume and can be dozens or even hundreds of times faster than the state-of-the-art algorithms. The results also demonstrate that our scheme can be applied to heterogeneous MPSoC systems and is effective in improving reliability for heterogeneous MPSoC systems.

No abstract available.

Due to the increasing complexity of System-on-Chip (SoC) design, how to ensure that silicon implementations conform to their high-level specifications is becoming a major challenge. To address this problem, we propose a novel specification-driven conformance checking approach that can automatically identify inconsistencies between different levels of designs. By extending SystemRDL specifications, our approach enables the generation of high-level Formal Device Models (FDMs) that specify access behaviors of interface registers triggered by driver requests. Based on the symbolic execution of the generated FDMs with the same driver requests to virtual/silicon devices, our approach can efficiently check whether the designs of an SoC at different levels exhibit unexpected behaviors that are not modeled in the given specification. Experiments on two industrial network adapters demonstrate the effectiveness of our approach in troubleshooting bugs caused by inconsistencies in both virtual and post-silicon prototypes.

EtherCAT has become one of the leading real-time Ethernet solutions for networked industrial systems where a reliable communication infrastructure is needed due to highly error-prone environments. However, existing work on EtherCAT mainly focuses on clock synchronization and timeliness improvement. The reliability of EtherCAT-based networked systems has largely been ignored. In this paper, we present a PID-based feedback control scheme that aims at enhancing reliability of networked systems under timing and system resource constraints. Instead of automatic repeat request method (ARQ), a forward error control technique is introduced to achieve the required system reliability at a lower deadline miss rate of messages. The PID-based feedback control scheme can also improve the stability of a system in terms of deadline miss rate in the presence of bursty errors. Simulation results show that the proposed scheme can achieve reliability enhancement of up to 79% compared to benchmarking methods.

Both soft-error reliability (SER) due to transient faults and lifetime reliability (LTR) due to permanent faults are key concerns in real-time MPSoCs. Existing works have investigated related problems, however, most of them only focus on one of the two reliability concerns. A few efforts do consider both types of reliability together, but ignore the impacts of hardware- and application-level variations on reliability, thus are not applicable to state-of-the-art MPSoCs under variations. In this paper, we focus on increasing SER without sacrificing LTR since transient faults occur much more frequently than permanent faults. Specifically, we propose a novel task allocation and scheduling scheme to maximize SER while satisfying a LTR constraint for soft real-time MPSoCs. Considering that SER is the objective while LTR is a constraint in our problem, and LTR is highly related to core temperature profiles, we dedicate to investigating the effects of variations in core soft-error rate, task vulnerability to soft errors, and task execution time on SER. To the best of our knowledge, our work is the first attempt that jointly handles the two reliability issues as well as taking into account the effects of variations on reliability. Experimental results show that our scheme improves the SER by up to 66% as compared to a number of representative existing approaches while meeting the same LTR constraint.

Multi-Processor System-on-Chip (MPSoC) has emerged as a promising platform to meet the increasing performance demand of embedded applications. However, due to limited energy budget, it is hard to guarantee that applications on MPSoC can be accomplished on time with a required throughput. The situation becomes even worse for applications with high reliability requirements, since extra energy will be inevitably consumed by task re-executions or duplicated tasks. Based on Dynamic Voltage and Frequency Scaling (DVFS) and task duplication techniques, this paper presents a novel energy-efficient scheduling model, which aims at minimizing the overall energy consumption of MPSoC applications under both throughput and reliability constraints. The problem is shown to be NP-complete, and several polynomial-time heuristics are proposed to tackle this problem. Comprehensive simulations on both synthetic and real application graphs show that our proposed heuristics can meet all the given constraints, while reducing the energy consumption.

The main goal of a cloud service provider is to make profits by providing services to users. Existing pricing strategies adopt a single revenue function for different types of service requests, which ignores the heterogeneity of users and leads to low profits. In this paper, we propose a heterogeneity-aware request scheduling scheme that maximizes profits of service providers by exploiting the heterogeneity of users. Specifically, we first model charge functions of requests at the granularity of individual users to capture their heterogeneity. Then an integer linear programming (ILP)-based optimal scheduling algorithm is designed to maximize profits, which is followed by an approximate but lightweight genetic algorithm (GA)-based profit improvement scheme. The GA-based scheme is particularly tailored for applications of high scheduling resolution. Extensive simulation results show that our schemes improve profits by at least 22.46% compared to benchmarking methods while achieving at least 13.51 times of speedup.

Internet of Things devices, such as video-based detectors or road side units are being deployed in emerging applications like sustainable and intelligent transportation systems. Oftentimes, stringent operation and energy cost constraints are exerted on this type of applications, necessitating a hybrid supply of renewable and grid energy. The key issue of a cost-constrained hybrid of renewable and grid power is its uncertainty in energy availability. The characteristic of approximate computation that accepts an approximate result when energy is limited and executes more computations yielding better results if more energy is available, can be exploited to intelligently handle the uncertainty. In this paper, we first propose an energy-adaptive task allocation scheme that optimally assigns real-time approximate-computation tasks to individual processors and subsequently enables a matching of the cost-constrained hybrid supply of energy with the energy demand of the resultant task schedule. We then present a quality of service (QoS)-driven task scheduling scheme that determines the optional execution cycles of tasks on individual processors for optimization of system QoS. A dynamic task scheduling scheme is also designed to adapt at runtime the task execution to the varying amount of the available energy. Simulation results show that our schemes can reduce system energy consumption by up to 29% and improve system QoS by up to 108% as compared to benchmarking algorithms.

With the rapid deployment of cloud computing infrastructures, understanding the economics of cloud computing has become a pressing issue for cloud service providers. However, existing pricing models rarely consider the dynamic interactions between user requests and the cloud service provider. Thus, the law of supply and demand in marketing is not fully explored in these pricing models. In this paper, we propose a dynamic pricing model based on the concept of user perceived value that accurately captures the real supply and demand relationship in the cloud service market. Subsequently, a profit maximization scheme is designed based on the dynamic pricing model that optimizes profit of the cloud service provider without violating service-level agreement. Finally, a dynamic closed loop control scheme is developed to adjust the cloud service price and multiserver configurations according to the dynamics of the cloud computing environment such as fluctuating electricity and rental fees. Extensive simulations using the data extracted from real-world applications validate the effectiveness of the proposed user perceived value-based pricing model and the dynamic profit maximization scheme. Our algorithm can achieve up to 31.32 percent profit improvement compared to a state-of-the-art approach.

Aiming at high performance, more and more Cyber-Physical Systems (CPSs) adopt Multiprocessor System-on-Chips (MPSoCs) as computation units. However, due to increasing integration of transistors on a die, the power densities together with performance variations of MPSoC chips have been increasing dramatically. Consequently, the MPSoC-based CPSs might become unsustainable and unreliable. Although various Task Allocation and Scheduling (TAS) heuristics have been proposed to minimize the hotspot time (i.e., duration of thermal emergency) and energy consumption of MPSoC designs, few of them can guarantee the highest performance yield under process variations without violating energy, thermal and timing constraints. To address these challenges, this paper proposes a novel energy- and thermal-aware TAS evaluation and optimization framework. Based on statistical model checking techniques, our approach enables accurate modeling and reasoning of the performance yield of real-time MPSoC designs under joint energy and thermal constraints. To enable system-level design space exploration, we propose a regression analysis-based method that can drastically reduce the overall exploration efforts. Experimental results show that our fully-automated approach can not only allow accurate sustainability-oriented reasoning of TAS solutions under specified thermal and energy constraints, but also enable the quick search of optimal TAS solutions on different MPSoC architectures with the highest performance yield.

The key issue of renewable generations such as solar and wind in energy harvesting system is the uncertainty of energy availability. The characteristic of imprecise computation that accepts an approximate result when energy is limited and executes more computations yielding better results if more energy is available, can be exploited to intelligently handle the uncertainty. In this paper, we first propose a task allocation scheme that adaptively assigns real-time imprecise computation tasks to individual processors considering uncertainties in renewable energy sources. The proposed task allocation scheme enhances energy efficiency by minimizing system energy consumption followed by adapting the execution of imprecise computation tasks to the energy availability. We then present a QoS-aware task scheduling scheme that determines the optional execution cycles of tasks allocated to processors. The proposed task scheduling scheme maximizes system QoS under the energy budget constraint.

With the rapid deployment of cloud computing infrastructures, understanding the economics of cloud computing has becoming a pressing issue for cloud service providers. However, existing pricing models rarely consider the dynamic interaction between user requests and the cloud service provider, thus can not accurately reflect the law of supply and demand in marketing. In this paper, we propose a pricing model based on the concept of user perceived value in the domain of economics that accurately capture the real supply and demand situation in the cloud service market. We then design a profit maximization scheme based on the presented dynamic pricing model that optimizes profit of the cloud service provider without violating user service-level agreement. Extensive experiments using data extracted from real-world applications validate the effectiveness of the proposed user perceived value-based pricing model. The proposed profit maximization scheme achieves 24.44% more profit as compared to the state of the art benchmarking methods.

The hybrid architecture analysis and design language (AADL) has been proposed to model the interactions between embedded control systems and continuous physical environment. However, the worst-case performance analysis of hybrid AADL designs often leads to overly pessimistic estimations, and is not suitable for accurate reasoning about overall system performance, in particular when the system closely interacts with an uncertain external environment. To address this challenge, this paper proposes a statistical model checking-based framework that can perform quantitative evaluation of uncertainty-aware hybrid AADL designs against various performance queries. Our approach extends hybrid AADL to support the modeling of environment uncertainties. Furthermore, we propose a set of transformation rules that can automatically translate AADL designs together with designers' requirements into networks of priced timed automata and performance queries, respectively. Comprehensive experimental results on the movement authority scenario of Chinese train control system level 3 demonstrate the effectiveness of our approach.

Branch-and-bound (B&B) approaches are widely investigated in resource constrained scheduling (RCS). However, due to the lack of approaches that can generate a tight schedule at the beginning of the search, B&B approaches usually start with a large initial search space, which makes the following search of an optimal schedule time-consuming. To address this problem, this paper proposes a parallel two-phase B&B approach that can drastically reduce the overall RCS time. This paper makes three major contributions: i) it proposes three partial-search heuristics that can quickly find a tight schedule to compact the initial search space; ii) it presents a two-phase search framework that supports the efficient parallel search of an optimal schedule; iii) it investigates various bound sharing and speculation techniques among collaborative tasks to further improve the parallel search performance at different search phases. The experimental results based on well-established benchmarks demonstrate the efficacy of our proposed approach.

CMOS scaling has greatly increased concerns for lifetime reliability due to permanent faults and soft-error reliability due to transient faults. Most existing works only focus on one of the two reliability concerns, but often times techniques used to increase one type of reliability may adversely impact the other type. A few efforts do consider both types of reliability together and use two different metrics to quantify the two types of reliability. However, for many systems, the concern of the user is to maximize system availability by improving the mean time to failure (MTTF), regardless of whether the failure is caused by permanent faults or transient faults. Addressing this concern requires a uniform metric to measure the effect due to both types of faults. In this paper, we derive a novel analytical expression for calculating the MTTF due to transient faults. Using this new formula and an existing method to evaluate system MTTF, we formulate and solve the problem of maximizing system availability with consideration of permanent faults, transient faults, and throughput constraint. Extensive simulations of synthetic task sets and benchmarks based on real-world applications were performed to validate our algorithm.

In-situ server systems are deployed in very special operating environment to handle in-situ workloads that are normally generated from environmentally sensitive areas or remote places that lack established utility infrastructure. This very special operating environment of in-situ servers urges such systems to be 100 percent powered by renewable energy. However, existing energy management schemes assume a hybrid supply of grid and renewable energy, hence are not well suited for 100 percent renewable powered in-situ server systems. In this paper, we tackle the problem of allocating harvested energy to 100 percent renewable powered server systems for optimizing both the overall system throughput and throughput of individual servers. From a game theoretic perspective, we model the energy allocation problem as a cooperative game among multiple servers and derive a Nash bargaining solution. Based on the Nash bargaining solution, we then propose a heuristic algorithm that determines the energy allocation strategies according to system energy states. Experimental results show that our proposed game theoretic approach achieves a high throughput from perspectives of both the overall system and individual servers.

Since Internet of Things (IoT) applications are deployed within open physical environments, their executions suffer from a wide spectrum of uncertain factors (e.g., network delay, sensor inputs). Although ThingML is a promising IoT modeling and specification language which enables the fast development of resource-constrained IoT applications, it lacks the capability to model such uncertainties and quantify their effects. Consequently, within uncertain environments the quality and performance of IoT applications generated from ThingML designs cannot be guaranteed. To explore the overall runtime performance variations caused by environmental uncertainties, this paper proposes a quantitative uncertainty evaluation framework for ThingML-based IoT designs. By adopting network of priced timed automata as the model of computation and statistical model checking as the evaluation engine, our approach can model uncertainties caused by external environments as well as support various kinds of performance queries on the extended ThingML designs. Experimental results of two comprehensive case studies demonstrate the efficacy of our approach.

With the continuous scaling of CMOS devices, the increase in power density and system integration level have not only resulted in huge energy consumption but also led to elevated chip temperature. Thus, energy efficient task scheduling with thermal consideration has become a pressing research issue in computing systems, especially for real-time embedded systems with limited cooling techniques. In this paper, we design a two-stage energy-efficient temperature-aware task scheduling scheme for heterogeneous real-time multiprocessor system-on-chip (MPSoC) systems. In the first stage, we analyze the energy optimality of assigning real-time tasks to multiple processors of an MPSoC system, and design a task assignment heuristic that minimizes the system dynamic energy consumption under the constraint of task deadlines. In the second stage, the optimality of minimizing the peak temperature of a processor is investigated, and a slack distribution heuristic is proposed to improve the temperature profile of each processor under the thermal constraint, thus the temperature-dependent system leakage energy consumption is reduced. Through the extensive efforts made in two stages, the system overall energy consumption is minimized. Experimental results have demonstrated the effectiveness of our scheme.

Most cellular network communication strategies are focused on data traffic scenarios rather than energy balance and efficient utilization. Thus mobile users(cell phones) in hot cells may suffer from low throughput due to energy loading imbalance problem. In state-of-art cellular network technologies, relay stations extend cell coverage and enhance signal strength for mobile users. However, busy traffic makes the relay stations in hot area run out of energy quickly. In this paper, we propose an energy balancing strategy in which the mobile nodes are able to dynamically select and hand over to the relay station with the highest potential energy capacity to resume communication. Key to the strategy is that each relay station merely maintains two parameters that contains the trend of its previous energy consumption and then predicts its future quantity of energy, which is defined as the relay station's potential energy capacity. Then each mobile node can select the relay station with the highest potential energy capacity. Simulations demonstrate that our approach significantly increase the aggregate throughput and the average life time of relay stations in cellular network environment.

Building automation is the key to sustainable, safe and comfortable buildings as well as to the integration of buildings into smart grids and with other external applications such as cloud computing. In a typical smart community scenario, various household appliances of multiple residential users are connected via a Home Area Network. HANs are further connected to the local power distribution network via smart meters, forming a LAN, where also renewables communicate. Methods are needed to design and integrate networks with hundred thousands of nodes in a cost-efficient way. The key to providing improved services in building automation is to process complex scenarios in an adequate way. Furthermore, building automation systems must be seen as dependable systems covering both safety and security aspects. The main objective of this Special Section is to bring the ideas of the worldwide research community into a common platform, to present the latest advances and developments.

With the increasing deployment of battery-powered embedded systems such as sensor nodes in extreme environments, harvesting renewable energy from ambient environments to achieve near perpetual operation of a system has attracted considerable research efforts in the recent past. In this paper, the authors propose a dynamic frequency selection scheme for energy-harvesting real-time systems. The proposed scheme characterizes the state of a system from the perspectives of system utilization and harvested energy with respect to a certain period of time. A portion of the battery energy is allocated to a group of tasks in the period of time by jointly considering the system utilization and energy state, and the operating frequency is selected based on the allocated energy. The derived operating frequency is fine tuned to further enhance energy efficiency when overflow occurs. Simulation results demonstrate the effectiveness of the proposed scheme. Compared with the state-of-the-art scheme that decouples the energy and timing design constraints, the proposed scheme achieves comparable deadline miss rate when the battery capacity is lower than 5000 J and achieves about 11.5% lower deadline miss rate when the battery capacity is greater than 30 000 J. The proposed scheme also outperforms the benchmarking scheme in energy efficiency. When the battery is near a full charge or overflow occurs, the proposed scheme incurs less energy waste when compared with the benchmarking algorithm, which is favorable for autonomous operation of the system. Furthermore, the time complexity of the proposed scheme is one order of magnitude lower than that of the benchmarking scheme, which makes the proposed scheme well suited for dynamic scheduling.

As VLSI technology enters the nanoscale regime, design reliability is becoming increasingly important. A major design reliability concern arises from electromigration which refers to the transport of material caused by ion movement in interconnects. Since the lifetime of an interconnect drastically depends on the current flowing through it, the electromigration problem aggravates with increasingly growing thinner wires. Further, the current-density-induced interconnect thermal issue becomes much more severe with larger current. To mitigate the electromigration and the current-density-induced thermal effects, interconnect current density needs to be reduced. Assigning wires to thick metals increases wire volume, and thus, reduces the current density. However, overstretching thick-metal assignment may hurt routability. Thus, it is highly desirable to minimize the thick-metal usage, or total wire cost, subject to the reliability constraint. In this paper, the minimum cost reliability-driven routing, which consists of Steiner tree construction and layer assignment, is considered. The problem is proven to be NP-hard and a highly effective iterative rounding-based integer linear programming algorithm is proposed. In addition, a unified routing technique is proposed to directly handle multiple current levels, which is critical in analog VLSI design. Further, the new algorithm is extended to handle blockage. Our experiments on 450 nets demonstrate that the new algorithm significantly outperforms the state-of-the-art work [CHECK END OF SENTENCE] with up to 14.7 percent wire reduction. In addition, the new algorithm can save 11.4 percent wires over a heuristic algorithm for handling multiple currents.

This paper proposes a reliability-driven task scheduling scheme for multiprocessor real-time embedded systems that optimizes system energy consumption under stochastic fault occurrences. The task scheduling problem is formulated as an integer linear program where a novel fault adaptation variable is introduced to model the uncertainties of fault occurrences. The proposed scheme, which considers both the dynamic power and the leakage power, is able to handle the scheduling of independent tasks and tasks with precedence constraints, and is capable of scheduling tasks with varying deadlines. Experimental results have demonstrated that the proposed reliability-driven parallel scheduling scheme achieves energy savings of more than 15% when compared to the approach of designing for the corner case of fault occurrences.

Energy-efficient task allocation and scheduling schemes with deterministic fault-tolerance capabilities are proposed for symmetric multiprocessor systems executing tasks with hard real-time constraints. The proposed heuristic is proven to achieve energy savings by optimally balancing application workload among processors in a system. Based on the observation that fault-free operation is expected to remain dominant in the near future and the probability of the worst case faults is low, an optimistic fault-tolerant heuristic is then proposed to achieve maximum energy savings in the absence of faults while degrading gradually to meet application timing requirements in the worst case of faults. Simulation results show that compared to state-of-art allocation and scheduling schemes proposed heuristic achieves average energy savings of up to 70%. It is also shown that optimistic approach is more resilient to variations in application utilizations and fault occurrences beyond system specifications.

In this paper we investigate fault tolerance and Dynamic Voltage Scaling (DVS) in hard real time systems. We present two low-complexity fault-aware scheduling algorithms that combine feasibility analysis of Rate Monotonic Algorithm (RMA) schedules and DVS-based frequency scaling using exact characterization of RMA algorithm. These algorithms lay the foundation for highly efficient online schemes that minimize energy consumption by adapting DVS policies to runtime behavior of tasks and fault occurrences without violating the offline feasibility analysis. Simulation results demonstrate energy savings of up to 60% over low-energy offline scheduling algorithms [22].

Due to their extremely small feature sizes and ultra low power consumption, Quantum-dot Cellular Automata (QCA) technology is projected to be a promising nanotechnology. However, in nanotechnologies, manufacture time defect levels and operational time fault rates are expected to be quite high. Straightforward Triple Modular Redundancy (TMR) based fault tolerance is inappropriate for QCA nanotechnology since wire delays dominate the logic delays and faults in wires dominate the faults in a QCA based design. Furthermore, long wires are necessary in TMR based designs. In this paper we show that fault-tolerance can be obtained by using TMR with Shifted Operands (TMRSO). TMRSO uses shorter wires of QCA cells and exploits the self-latching property of clocked QCA arrays to provide the same level of fault tolerance capability as straightforward TMR while being significantly faster and smaller. This technique can be applied to a variety of operations; we have validated TMRSO on adders. Implementation results obtained using QCADesigner [6] show that an 8-bit adder using TMRSO has more than 50% area reduction and more than 100% throughput improvement when compared to a TMR implementation.