Cross-domain federated learning aims to collaboratively train a generalized model across clients with heterogeneous domain distributions without sharing data. Existing methods typically leverage prototypes to align intermediate representations among local models and enhance collaborative knowledge sharing, constructed either by directly aggregating class-center features across clients or by performing clustering to improve diversity. However, their performance is limited by the suboptimal ability to balance the learning of generalized and domain-specific features. To address this issue, this paper presents a novel dual-prototype guided FL framework named FedOrthrus, which decomposes the prototype into two components: i) the generalized prototype to capture cross-client domain-invariant features, and ii) the domain-specific prototype to extract the specific features of each domain. Specifically, the cloud server aggregates generalized prototypes to capture shared semantics across clients, thereby guiding each client to learn domain-invariant representations. Meanwhile, a clustering strategy is employed to adaptively construct domain-specific prototypes, ensuring that the representational capacity allocated to each domain is balanced according to its semantic complexity. Moreover, FedOrthrus employs a distribution-aware prototype construction scheme to dynamically assign the size of each part of prototypes, which enhances adaptability to different levels of domain heterogeneity. The experimental results on three datasets demonstrate that our FedOrthrus can achieve up to 14.56% and 3.96% accuracy improvement compared to traditional and state-of-the-art prototype-based FL methods. Our code is available at https://github.com/AAuZZ/FedOrthrus.

The integration of Energy Harvesting (EH) technology into Mobile Edge Computing (MEC) presents a promising solution to the energy limitations faced by end devices (EDs) when performing computation-intensive tasks, ultimately enhancing the EDs’ sustainability. However, the intermittent and unpredictable nature of harvested energy introduces significant uncertainty in energy availability, complicating dynamic task execution and resource management. In EH-MEC systems, managing task scheduling and offloading dynamically is crucial for optimizing application latency while ensuring long-term battery energy stability and task reliability. Existing approaches inadequately address the time-coupling between task decisions caused by long-term battery energy stability constraints, and often neglect task reliability considerations. To overcome these limitations, we propose decomposing the original problem into 1) a decoupling problem that transforms the optimization with long-term battery energy constraints into a series of deterministic optimizations within individual time slots, 2) a task scheduling problem that determines task-to-ES assignment and computing resource allocation of ESs to offloaded tasks, and 3) a task offloading problem that determines the optimal offloading decision to achieve minimal latency while meeting energy stability and server reliability constraints. To tackle these subproblems, we design a Lyapunov-based optimization method, a reliabilityaware task scheduling algorithm, and an efficient heuristic-based task offloading algorithm. Extensive simulations and experiments based on empirical data from a physical MEC testbed validate the efficacy of our approach.

Federated Learning (FL) has emerged as a potential distributed learning paradigm that enables model training on edge devices (i.e., workers) while preserving data privacy. However, its reliance on a centralized server leads to limited scalability. Decentralized federated learning (DFL) eliminates the dependency on a centralized server by enabling peer-to peer model exchange. Existing DFL mechanisms mainly employ synchronous communication, which may result in training inefficiencies under heterogeneous and dynamic edge environments. Although a few recent asynchronous DFL (ADFL) mechanisms have been proposed to address these issues, they typically yield stale model aggregation and frequent model transmission, leading to degraded training performance on non-IID data and high communication overhead. To overcome these issues, we present DySTop, an innovative mechanism that jointly optimizes dynamic staleness control and topology construction in ADFL. In each round, multiple workers are activated, and a subset of their neighbors is selected to transmit models for aggregation, followed by local training. We provide a rigorous convergence analysis for DySTop, theoretically revealing the quantitative relationships between the convergence bound and key factors such as maximum staleness, activating frequency, and data distribution among workers. From the insights of the analysis, we propose a worker activation algorithm (WAA) for staleness control and a phase-aware topology construction algorithm (PTCA) to reduce communication overhead and handle data non-IID. Extensive evaluations through both large-scale simulations and real-world testbed experiments demonstrate that our DySTop reduces completion time by 46.7% and the communication resource consumption by 48.3% compared to state-of-the-art solutions, while maintaining the same model accuracy.

Task scheduling in mobile edge computing (MEC) is critical for managing latency and energy consumption, especially under user mobility, which poses additional challenges to scheduling decisions. This paper studies the mobility-aware two-stage task scheduling (MTTS) problem in MEC systems to minimize energy consumption at the mobile edge under task deadline constraints. First, the MTTS problem is formulated as a two-stage optimization model consisting of two interrelated sub-problems, associated with task offloading and result downloading, respectively. Then, a dual-population-based evolutionary computation (DORA) framework is proposed that can solve MTTS-type two-stage problems by incorporating a wide range of swarm intelligence algorithms (SIAs). The proposed DORA framework employs two populations that run in parallel, each dedicated to solving one of the two interrelated sub-problems, to enable effective exploration of the solution space and improve computational efficiency. The evolutionary process of each population in DORA consists of three fundamental algorithmic components: mapping, evaluation, and updating. In particular, the mapping component establishes the link between individual space in SIAs and solution space in the MTTS problem, with the critical parameter derived through rigorous theoretical analysis. Furthermore, the inter population collaboration is realized through an asynchronous solution transfer mechanism from the stage-1 population to the stage-2 population, guiding the search toward high-quality final scheduling solutions. Extensive experiments are conducted on a real-world mobile device trajectory dataset, incorporating four well-established SIAs to demonstrate the applicability and effectiveness of DORA.

The rise of pre-trained foundation models (FMs) has popularized the trend of fine-tuning FMs to fit downstream tasks, while Federated Learning (FL) has become the de-facto approach for training distributed data with privacy-preservation. However, fine-tuning FMs in FL faces overwhelming overheads due to its bulky nature. While freezing parameters in FM have the potential to accelerate FL training, existing freezing strategies statically freeze parameters on specified or already converged layers, incur severe accuracy degradation, and resource-inefficiency in heterogeneous environments. In this paper, we propose AFedLF, an adaptive freezing framework for FM in FL, to accelerate its wall-clock time for convergence without losing its final accuracy. However, this poses great challenges, as different freezing strategies lead to different accuracy gains and time overheads, while unfreezing more layers may bring marginal accuracy gains but significant time overheads. To address this challenge, AFedLF mathematically establishes a correlation between the freezing strategy and the accuracy gain and time overhead, and allocates adaptive freezing strategies to clients, based on our insight that unfreezing more layers on devices with strong computation and communication capabilities helps improve resource efficiency. Besides, AFedLF incorporates our well-designed intermediate result caching scheme with constant approximation ratios utilizing the limited storage capacity on mobile devices to cache intermediate results to skip forward propagation, further saving wall-clock time. Finally, we implemented AFedLF using an open-source FL benchmark, and extensive trace-driven experimental results showed that AFedLF accelerates wall-clock time by up to 6.1× compared to state-of-the-art solutions, without sacrificing accuracy.

Federated learning (FL) enables training AI models across distributed edge devices (i.e., workers) using local data, while facing challenges including communication resource constraints, edge heterogeneity, and non-IID data. Over-the-air computation (AirComp) has emerged as a promising technique to improve communication efficiency by leveraging the superposition property of a wireless multiple access channel (MAC) for model aggregation. However, over-the-air aggregation requires strict synchronization among edge devices, which is essentially incompatible with the asynchronous FL mechanisms often used to handle edge heterogeneity. To overcome this incompatibility, we propose Air-FedGA, a grouping-based asynchronous FL mechanism via AirComp, where workers are organized into groups for synchronized over-the-air aggregation within each group, while groups asynchronously communicate with the parameter server to update the global model. This design retains the communication efficiency of AirComp while addressing training inefficiency caused by edge heterogeneity. We provide a rigorous convergence analysis for Air-FedGA, theoretically quantifying how the convergence bound depends on several key factors, such as the maximum staleness, the degree of non-IID data among groups, and the AirComp aggregation mean squared error (MSE). Guided by these theoretical insights, we propose power control and worker grouping algorithms to minimize the convergence bound by jointly optimizing the AirComp aggregation MSE and the grouping strategy. We conduct experiments on classical models and datasets, and the results demonstrate that our proposed mechanism and algorithms can accelerate the model training by 1.83-$2.22\times $compared with the state-of-the-art solutions.

Deep neural networks (DNNs) have demonstrated exceptional performance, leading to diverse applications across various mobile devices (MDs). Considering factors like portability and environmental sustainability, an increasing number of MDs are adopting energy harvesting (EH) techniques for power supply. However, the computational intensity of DNNs presents significant challenges for their deployment on these resource-constrained devices. Existing approaches often employ DNN partition or offloading to mitigate the time and energy consumption associated with running DNNs on MDs. Nonetheless, existing methods frequently fall short in accurately modeling the execution time of DNNs, and do not consider to use thread allocation for further latency and energy consumption optimization. To solve these problems, we propose a dynamic DNN partition and thread allocation method to optimize the latency and energy consumption of running DNNs on EH-enabled MDs. Specifically, we first investigate the relationship between DNN inference latency and allocated threads and establish an accurate DNN latency prediction model. Based on the prediction model, a DRL-based DNN partition (DDP) algorithm is designed to find the optimal partitions for DNNs. A thread allocation (TA) algorithm is proposed to reduce the inference latency. Experimental results from our test-bed platform demonstrate that compared to four benchmarking methods, our scheme can reduce DNN inference latency and energy consumption by up to 37.3% and 38.5%.

Federated learning (FL) is a new paradigm to train AI models over distributed edge devices (i.e., workers) using their local data, while confronting various challenges including communication resource constraints, edge heterogeneity and data Non-IID. Over-the-air computation (AirComp) is a promising technique to achieve efficient utilization of communication resource for model aggregation by leveraging the superposition property of a wireless multiple access channel (MAC). However, AirComp requires strict synchronization among edge devices, which is hard to achieve in heterogeneous scenarios. In this paper, we propose an AirComp-based grouping asynchronous federated learning mechanism (Air-FedGA), which combines the advantages of AirComp and asynchronous FL to address the communication and heterogeneity challenges. Specifically, AirFedGA organizes workers into groups and performs over-theair aggregation within each group, while groups asynchronously communicate with the parameter server to update the global model. In this way, Air-FedGA accelerates the FL model training by over-the-air aggregation, while relaxing the synchronization requirement of this aggregation technology. We theoretically prove the convergence of Air-FedGA. We formulate a training time minimization problem for Air-FedGA and propose the power control and worker grouping algorithm to solve it, which jointly optimizes the power scaling factors at edge devices, the denoising factors at the parameter server, as well as the worker grouping strategy. We conduct experiments on classical models and datasets, and the results demonstrate that our proposed mechanism and algorithm can speed up FL model training by$\mathbf{29.9\% - 71.6\%}$compared with the state-of-the-art solutions.

The widespread adoption of Deep Neural Networks (DNNs) across diverse applications has intensified the demand for efficient execution strategies on GPU-CPU heterogeneous devices. While existing device-oriented DNN partitioning methods optimize either energy consumption or execution latency in isolation, they often lack the capability to dynamically adapt partitioning strategies under real-world constraints. To address these limitations, we first formulate the DNN partitioning problem as an optimal execution path search on a directed acyclic graph (DAG), then propose an imitation learning (IL)-based DNN partitioning framework that dynamically allocates DNN layers across GPU-CPU processors to minimize energy consumption while satisfying time constraints. Specifically, we first design aProfiler, which analyzes the layer-wise characteristics such as the execution time and energy consumption of layers in DNNs executed on the GPU-CPU heterogeneous device. Then, we present anOracle Generatorthat generates high-quality partitioning solutions based on the Dijkstra algorithm to form a training datasetOracle.Oraclefrom the proposed generator is input to a proposed Trainer, which iteratively learns from theOracledata to obtain a regression model namedPredictor.Predictorcan predict the optimal partitioning strategy for DNNs in real time. Finally, we design aData Aggregator, which enhancesOracledata through continuous runtime feedback, thereby improving the prediction accuracy of the regression model. We evaluate the proposed IL-based DNN partitioning method on 3 NVIDIA Jetson platforms and 1 Huawei NPU platform. Experimental results demonstrate that compared to 3 benchmarking methods, our method reduces energy consumption by up to 73.15%.

As smart mobile applications increasingly demand timely situational awareness and energy efficiency, the Age of Information (AoI) metric plays a vital role in maintaining data freshness. This need is further supported by the End-Edge-Cloud Computing (EECC) paradigm, which enhances application performance by facilitating task offloading to the edge or the cloud. However, existing AoI optimization solutions focus solely on task offloading, often neglecting critical aspects such as system resource allocation and energy efficiency, which can lead to resource waste, increased energy consumption, compromised Quality of Service (QoS), and system performance degradation. Therefore, this paper investigates the joint optimization of task offloading, communication and computing resource allocation in EECC systems, aiming to minimize AoI and energy consumption under constraints of deadlines and capacity constraints. To address this problem, we divide the decision space into multiple non-intersecting decision areas based on the characteristics of the studied problem and design a task offloading and resource allocation algorithm based on slow-movement particle swarm optimization (SPSO) to handle each decision area individually. In the algorithm design, we customize the position, velocity, update rules, and fitness function for the optimization problem. Finally, extensive simulation-based and testbed experiment results show that the proposed algorithm can save up to 14.56% of energy consumption, shorten AoI by up to 27.80%, and improve utility (weighted sum of AoI and energy consumption) by up to 15.89% compared with existing algorithms.

Federated learning (FL) enables privacy-preserving distributed machine learning by training models on edge client devices using their local data without revealing their raw data. In edge environments, various applications require different neural network models, making it crucial to perform joint training of multiple models on edge devices, known as multi-task FL. While existing multi-task FL approaches enhance resource utilization on edge devices through adaptive resource configuration or client selection, optimizing either of these aspects alone may lead to suboptimality. Therefore, in this paper, we explore a joint client selection and resource configuration method called JCSRC for multi-task FL, aiming to maximize energy efficiency in environments with limited computation and communication resources and heterogeneous client devices. Firstly, we formalize this problem as a mixed-integer nonlinear programming problem considering all these characteristics and prove its NP-hardness. To address this problem, we first design a multi-agent reinforcement learning (MARL)-based client selection method that selects appropriate clients for each task to train their models. The MARL method makes client selection decisions based on the clients’ data quality, energy efficiency, communication, and computation capacity to ensure fast convergence and energy efficiency. Then, we design a particle swarm optimization (PSO)-based resource configuration scheme that configures appropriate computation and bandwidth resources for each task on each client. The PSO scheme makes resource configuration decisions based on theoretically derived optimal CPU frequency and bandwidth to achieve high energy efficiency. Finally, we carry out extensive simulations and testbed-based experiments to validate our proposed JCSRC. The results demonstrate that, in comparison to state-of-the-art solutions, JCSRC can save energy consumption by up to 59% to achieve the target accuracy.

This letter focuses on integrated CPU-GPU edge systems with renewable energy sources and studies the resource management problem to minimize the energy consumption of real-time tasks while ensuring temperature and reliability constraints. We propose an imitation learning (IL)-based resource management scheme, ILRM, implemented in two phases: 1) offline Oracle generation and 2) online IL. In the offline phase, we design a fast-converging heuristic to generate near-optimal solutions (i.e., Oracles) for training an online prediction model. In the online phase, we realize IL using the trained model that predicts the resource configuration policies for the incoming task sets to be scheduled. A data aggregation method is also developed to enhance the robustness of the prediction model. We validate ILRM through extensive experiments on both simulated and real integrated CPU-GPU edge platforms.

The prevalence of the pre-training & fine-tuning paradigm enables machine learning models to quickly adapt to various downstream tasks by fine-tuning pre-trained foundation models (FMs), greatly facilitating various IoT applications that rely on model inference in dynamic edge serverless environments. Efficiently dispatching inference requests and configuring instances to batch inference requests can significantly enhance resource efficiency. However, existing serverless inference solutions are tailored for traditional models, make coarse-grained request dispatching and instance configuration decisions, fail to exploit the shared model backbone characteristics of the FM and capture delayed rewards in dynamic environments, and ignore communication latency between edge sites, resulting in high costs and constraint violations. In this paper, we leverage our insight that fine-grained batch inference requests can effectively exploit the shared model backbone feature of FM to save monetary costs. We propose an algorithm that incorporates deep reinforcement learning (DRL) and expert intervention for fine-grained request dispatching and instance configuration, where the DRL component outputs fractional solutions as guidance, while the expert intervention module integrates our insights—batching reduces monetary costs at the expense of increased inference latency, whereas higher configurations shorten inference latency. This module rounds fractional solutions and adjusts instance configurations to search for optimal solutions while satisfying constraints, with theoretical guarantees rigorously proved. Finally, we conducted our experiments on an OpenFaas-based platform and simulator, and extensive trace-driven evaluation results show that ExpertDRL can save costs by up to 85.14% and improve request acceptance ratio by up to 26.93%, compared to the state-of-the-art solution.

Mobile-edge computing (MEC) has received wide attention recently due to its efficacy in alleviating the computation stress of mobile devices (MDs), which is realized by offloading workloads from MD users to nearby edge servers (ESs). Prior work has studied related task offloading and scheduling problems and proposed many approaches. However, none of these approaches considers the reliability issue in MEC systems which may suffer soft errors during task execution as well as bit errors during task offloading simultaneously. Targeting optimization on a multi-user MEC system, in this article we investigate the task offloading and scheduling problem of maximizing system quality of experience (QoE) under a certain reliability requirement. With the consideration of the combinatorial nature of this problem, we propose to decompose the original problem into i) a task-to-ES assignment problem with fixed task offloading decision, for satisfying system reliability constraint, ii) a computing resource allocation problem with fixed task offloading and assignment decisions, for maximizing system QoE, and iii) a task offloading optimization problem to find the best offloading decision that achieves the maximum QoE under the reliability constraint using our task assignment and resource allocation methods. In order to solve these sub-problems, we further design a reliability-aware task-to-ES assignment algorithm, a QoE-optimum resource allocation algorithm, and a binary particle swarm optimization based task offloading algorithm. We perform extensive simulations and testbed experiments to validate the efficacy of the proposed scheme. Simulation and testbed results show that the proposed scheme greatly outperforms four benchmark approaches and it achieves up to 63.2% and 43.1% increase in the average QoE (quantified by offloading utility), respectively.

Since Industry 5.0 emphasizes that manufacturing enterprises should raise awareness of social contribution to achieve sustainable development, more and more meta-heuristic algorithms are investigated to save energy in manufacturing systems. Although non-dominated sorting-based meta-heuristics have been recognized as promising multi-objective optimization methods for solving the energy-efficient flexible job shop scheduling problem (EFJSP), it is hard to guarantee the quality of the Pareto front (e.g., total energy consumption, makespan) due to the lack of population diversity. This is mainly because an improper individual comparison inevitably reduces population diversity, thus limiting exploration and exploitation abilities during population updates. To achieve efficient population evolution, this paper introduces a novel space-cooperation multi-objective optimization (SCMO) method that can effectively solve EFJSP to obtain scheduling schemes with better trade-offs. By cooperatively evaluating the similarity among individuals in both the decision space and objective space, we propose a space-cooperation population update method based on a three-vector representation that can accurately eliminate repetitive individuals to derive higher-quality Pareto solutions. To further improve search efficiency, we propose a difference-driven local search, which selectively changes the positions of operations with higher differences to search for neighbors effectively. Based on the Taguchi method, we conduct experiments to obtain a suitable parameter combination of SCMO. Comprehensive experimental results show that, compared to state-of-the-art methods, our SCMO method achieves the highest HV and NR and the lowest IGD, with an average of 0.990, 0.952, and 0.001, respectively. Meanwhile, compared to traditional local search approaches, our difference-driven local search obtains twice the HV on instance Mk12 and reduces the solving time from 1521 s to 475 s.

Federated learning (FL) as a promising distributed machine learning paradigm has been widely adopted in Artificial Intelligence of Things (AIoT) applications. However, the efficiency and inference capability of FL is seriously limited due to the presence of stragglers and data imbalance across massive AIoT devices, respectively. To address the above challenges, we present a novel asynchronous FL approach named CaBaFL, which includes a hierarchical cache-based aggregation mechanism and a feature balance-guided device selection strategy. CaBaFL maintains multiple intermediate models simultaneously for local training. The hierarchical cache-based aggregation mechanism enables each intermediate model to be trained on multiple devices to align the training time and mitigate the straggler issue. In specific, each intermediate model is stored in a low-level cache for local training and when it is trained by sufficient local devices, it will be stored in a high-level cache for aggregation. To address the problem of imbalanced data, the feature balance-guided device selection strategy in CaBaFL adopts the activation distribution as a metric, which enables each intermediate model to be trained across devices with totally balanced data distributions before aggregation. Experimental results show that compared to the state-of-the-art FL methods, CaBaFL achieves up to 9.26X training acceleration and 19.71% accuracy improvements.

The execution of deep neural network (DNN) algorithms suffers from significant bottlenecks due to the separation of the processing and memory units in traditional computer systems. Emerging memristive computing systems introduce an in situ approach that overcomes this bottleneck. The nonvolatility of memristive devices, however, may expose the DNN weights stored in memristive crossbars to potential theft attacks. Therefore, this article proposes a 2-D permutation-based protection (TDPP) method that thwarts such attacks. We first introduce the underlying concept that motivates the TDPP method: permuting both the rows and columns of the DNN weight matrices. This contrasts with previous methods, which focused solely on permuting a single dimension of the weight matrices, either the rows or columns. While it is possible for an adversary to access the matrix values, the original arrangement of rows and columns in the matrices remains concealed. As a result, the extracted DNN model from the accessed matrix values would fail to operate correctly. We consider two different memristive computing systems (designed for layer-by-layer and layer-parallel processing, respectively), and demonstrate the design of the TDPP method that could be embedded into the two systems. Finally, we present a security analysis. Our experiments demonstrate that TDPP can achieve comparable effectiveness to prior approaches, with a high level of security when appropriately parameterized. In addition, TDPP is more scalable than previous methods and results in reduced area and power overheads. The area and power are reduced by, respectively,$1218\times $and$2815\times $for the layer-by-layer system and by$178\times $and$203\times $for the layer-parallel system compared to prior works.

In recent decades, the rapid advances in information technology have promoted a widespread deployment of medical cyber-physical systems (MCPS), especially in the area of digital healthcare. In digital healthcare, medical edge devices empowered by CPU-GPU (Graphics Processing Unit) cooperative multiprocessor system-on-chips (MPSoCs) have a great potential in processing and managing the massive amounts of health-related data. However, most of the existing works on CPU-GPU cooperative MPSoCs cannot maintain a high-precision workload estimation since they simply leverage the worst-case execution cycles to pessimistically predict the workload of digital healthcare applications. Besides, they neglect the personalized requirements of individual healthcare applications and the lifetime reliability demands of heterogeneous CPU-GPU cores. As a result, the normal functions of medical edge devices and the quality-of-services (QoS) of digital healthcare applications are likely to suffer from underlying failures and degradation. In this paper, we explore CPU-GPU cooperative QoS optimization of personalized digital healthcare applications running on reliability guaranteed edge devices with the help of machine learning and swarm intelligence techniques. We first develop two novel predictors: one is a machine learning based predictor for application workload estimation, and the other is a feature-driven predictor for application QoS estimation. We then incorporate the two predictors into a swarm intelligent application scheduling scheme upon the cooperative dual-population evolutionary algorithm (c-DPEA) to find optimal application mapping and partitioning settings. Experimental results show that our solution not only augments the average QoS of whole digital healthcare applications by 15.7%, but also balances the QoS of individual digital healthcare applications by 64.3%.

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.

Cloud computing has attracted wide attention from both academia and industry, since it can provide flexible and on-demand hardware and software resources as services. Energy consumption of cloud servers is the main concern of cloud service providers since reducing energy consumption can bring them a lower operation cost (and hence a higher profit) and alleviate carbon footprints to the environment. Typically, the common power management techniques for enhancing energy efficiency would make cloud servers more vulnerable to soft errors and hence adversely impact the quality of services. Thus, reliability cannot be ignored in the design of methodologies for improving the energy efficiency of cloud servers. In this article, we aim to minimize the energy consumption of cloud servers under the soft-error reliability constraint by configuring the size and speed of servers. Specifically, we first derive the expected reliability based energy consumption of cloud servers to formulate the reliability-constrained energy minimization problem. We then leverage the reinforcement learning technique to obtain an optimal server configuration solution that maximizes system energy efficiency while maintaining the system reliability constraint. Finally, we perform extensive simulation experiments to analyze the relationship between system energy consumption and server configuration under varying arrival rates and execution requirements of service requests. Comparative experiments are also performed to validate the efficacy of the proposed learning-based server configuration scheme. Results show that compared to a benchmark method, the energy saved by the proposed scheme can reach up to 31.5%.

The network system deployed in hazardous environments is a key component of hazard-aware cyber-physical systems (CPSs) and its performance highly depends on surrounding environments. Due to the mobility of network nodes (e.g., portable IoT devices), frequently changeable network topology and links, as well as other external interferences such as electromagnetic interference, ensuring adaptivity and reliability of hazard-aware CPSs is of utmost importance. Meanwhile, the timeliness of message transmission is stringent in hazardous environments because the violation of timing requirements may lead to serious consequences. Last but not least, portable IoT devices are typically energy limited, thus ensuring a sustainable message transmission is highly necessary. In this paper, we aim at optimizing the reliability of hazard-aware CPSs while meeting the timing and energy constraints. To this end, we develop the first hazard-aware CPS model and study the impacts of surrounding environments (i.e., physical side) to the network infrastructure of a hazard-aware CPS (i.e., cyber side) with respect to reliability. We also propose a new scheme that adaptively tunes the fault tolerance strategies and admission strategies for real-time messages, to increase the reliability of hazard-aware CPSs under the energy constraint. Extensive simulation results demonstrate that our proposed scheme is capable of increasing system reliability by up to 4.21× with a lower deadline miss rate and runtime overhead compared with the state-of-the-art approaches.

The maturation of energy harvesting technologies enables the integration of batteryless devices in advanced computing paradigms. For example, in mobile edge computing (MEC), batteryless mobile devices can charge when they have insufficient energy to perform task offloading, thereby relaxing the energy constraints in developing offloading strategies. This paper studies the problem of minimizing the latency of task execution in an MEC system with multiple resource-limited servers and multiple batteryless devices under intermittent operation conditions. We formulate this problem as an integer program-based optimization model and propose a discrete grey wolf optimizer (DGWO) algorithm to solve the formulated problem. DGWO uses a task sequence, which is a permutation of all tasks to be offloaded, to represent an offloading solution and introduces a discrete representation of grey wolves to link each grey wolf with a solution. For each discrete grey wolf, we design an effective task allocation strategy to designate the computing resources of MEC servers for each offloaded task. We further define a set of discrete operations upon the discrete representation to update the positions of grey wolves, for the purpose of enhancing DGWO’s global search capability. Experimental results demonstrate that DGWO outperforms other baseline metaheuristics with reduced task execution latency and improved computational efficiency.

To meet the need for efficient execution of hyperspectral remote sensing image classification algorithms, this paper proposes a fine-grained parallel optimization method for a CPU-GPU heterogeneous hyperspectral image superpixel spectral fusion classification algorithm based on cloud computing. Ray is used as the distributed computing engine to fully utilize the logical control ability and large-scale parallel computing ability of the CPU-GPU heterogeneous platform. We first decouple the superpixel spectral fusion classification algorithm, analyze the data dependence and computing characteristics of sub-tasks, use the GPU to accelerate the algorithm, and then further extend the algorithm to the CPU-GPU heterogeneous platform. At the same time, we establish a scheduling model for algorithm task scheduling problems, specifying the value of the parallelism degree for the algorithm in a fine-grained manner. It is verified by experiments that the parallelization method proposed in this paper can effectively improve the execution efficiency with the premise of the accuracy unreduced.

As a standardized software framework and open E/E system architecture, the AUTomotive Open System ARchitecture (AUTOSAR) has been widely applied to autonomous driving systems to enable real-time control. However, due to the increasing design complexity and the lack of efficient algorithms and design automation tools, it is difficult to quickly figure out an optimal task scheduling scheme for an AUTOSAR-based system. To address this problem, we introduce a novel task scheduling method that can parallelly search for an optimal solution with the help of our proposed pruning strategy. Experimental results on a real-world AUTOSAR-based autonomous driving system demonstrate that our approach can achieve much better task scheduling solutions than the ones obtained manually and significantly reduce the overall task scheduling time.

Internet of Vehicles (IoV) has become an indispensable data sensing and processing platform in Internet of Things (IoT) for intelligent transportation. The mounted cameras on the vehicles along with the fixed roadside cameras are utilized to provide pictorial services for IoV users and law enforcement agencies. For such forensic services, ensuring the security and privacy of vehicles while guaranteeing the efficiency of data transmission among vehicles is important. In this paper, we propose a lightweight incentive authentication scheme (LIAS) for forensic services in IoV. LIAS is developed on a three-tier architecture containing cloud layer, fog layer, and user layer. LIAS uses pairing-free certificateless signcryption, pseudonym update mechanism, and incentive mechanism to realize a secure anonymous authentication efficiently. We conduct correctness and security analysis, as well as performance analysis and evaluation to validate the high security and efficiency of LIAS. Experimental results reveal that, the communication and computation overheads as well as the message delay and packet loss of LIAS are much lower than those of state-of-the-art techniques. Note to Practitioners—This paper is motivated by the security and privacy issues of forensic services in IoV for intelligent transportation. Our goal is to improve the security and privacy of vehicles while guaranteeing the lightweight and incentive of data transmission among the vehicles. Fog-assisted IoV is introduced to fully utilize the capacities of near-user edge devices as well as the connections between fog nodes and devices. However, it still faces the difficulties in ensuring vehicles’ security and privacy. Moreover, vehicles’ information dissemination could be easily monitored because of the unavoidable defect of wireless communication. Thereby, it is essential to guarantee the security and privacy of vehicles while enhancing the efficiency of vehicles’ data transmission during the forensic service. To this end, this paper proposes a lightweight conditional anonymous authentication scheme for forensic services in IoV, which is developed based on the pairing-free technique to achieve secure anonymous authentication with high efficiency. This paper also designs a user tracing mechanism, incentive mechanism, and pseudonym update mechanism to realize safe and effective forensic service in IoV.

As the backbone of Industry 4.0, industrial cyber-physical systems (ICPSs) that are geographically dispersed, federated, cooperative, and security-critical systems become the center of interest from both industry and academia. In ICPS, there are huge amounts of devices, such as sensors and actuators, which are embedded and networked together to improve the performance of real-time monitoring and control. Reliability and temperature are two important concerns of these embedded and networked devices in ICPS due to their stringent requirement of reliable execution and long lifespan. In this article, we study the problem of maximizing soft-error reliability of CPU- and GPU-integrated embedded platforms deployed in ICPS under the temperature constraint. To speed up the estimation of soft-error rate (SER) and temperature, we train an artificial neural network (ANN) that is able to quickly and accurately derive the system’s SER and temperature. To solve the temperature-constrained reliability optimization problem, we propose a feedback control-based task scheduling scheme that adaptively determines the number of tasks admitted in the system and the number of replicas for the admitted tasks. We perform a series of simulation experiments to verify the efficacy of our scheme. The experimental results demonstrate that: 1) the estimated SER and temperature derived by our ANN-based method are very close to the ground-truth data and 2) our proposed feedback control-based task scheduling method can improve system reliability by up to 184.2% with a lower peak temperature when compared with one baseline and two state-of-the-art methods. Note to Practitioners—This article is motivated by the safety-critical industrial cyber-physical system (ICPS) applications necessitating reliable execution and long lifespan, which could be realized by increasing reliability and controlling operating temperature. Our goal is to improve the system reliability of CPU- and GPU-integrated multiprocessor systems-on-chip (MPSoCs) deployed in ICPS under the temperature constraint. Most of the existing papers target either reliability or temperature. A few recent papers have focused on reliability and temperature optimization simultaneously. However, they are not designed for ICPS and do not consider the widely accepted CPU- and GPU-integrated MPSoC platforms. This article proposes a machine learning-based approach that trains an artificial neural network (ANN) to facilitate the online estimation of system SER and temperature. Compared to the offline estimation using simulation tools, the online approach is more applicable to real-time ICPS applications. This article also designs a feedback control-based approach for improving system reliability and reducing peak temperature of the CPU- and GPU-integrated MPSoCs by determining the number of tasks to be admitted and the number of replicas for tasks.

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.

Although reinforcement learning (RL) approaches are promising in autonomous traffic signal control (TSC), they often suffer from the unfairness problem that causes extremely long waiting time at intersections for partial vehicles. This is mainly because the traditional RL methods focus on optimizing the overall traffic performance, while the fairness of individual vehicles is neglected. To address this problem, we propose a novel RL-based method named FairLight for the fair and efficient control of traffic with variable phase duration. Inspired by the concept of user satisfaction index (USI) proposed in the transportation field, we introduce a fairness index in the design of key RL elements, which specially considers the travel quality (e.g., fairness). Based on our proposed hierarchical action space method, FairLight can accurately allocate the duration of traffic lights for selected phases. Experimental results obtained from various well-known traffic benchmarks show that, compared with the state-of-the-art RL-based TSC methods, FairLight can not only achieve better fairness performance but also improve the control quality from the perspectives of the average travel time of vehicles and RL convergence speed.

Due to the great advancement in computation, communication, and control technologies, the Internet of Things (IoT) can provide ubiquitous connectivity for anyone and anything at any time and any place, leading to a revolution in an information society. Protecting devices against various security threats is one of the most important challenges in IoT since IoT applications are generally security-critical systems while IoT devices are often poorly secured. For IoT devices, employing security services provided by smart gateways or edge/cloud servers to defend against various threats is an effective way to enhance their security. However, the finite battery energy of devices and the limited fund of device users hinder the wide application of security services in IoT. This necessitates the demand for designing new methodologies to tackle the tradeoff among security, energy, and fund of IoT devices. Therefore, this article attempts to optimize system security of IoT devices under energy and fund constraints. Specifically, to formulate the energy and fund constrained security optimization problem, we first propose a pricing model for the security services provided by the smart gateway. We then formulate the problem as a mixed-integer linear programming (MILP) problem. Since using a solver to address the MILP problem may be time consuming, we leverage the swarm intelligence technique to design a new task scheduling scheme that can efficiently solve the optimization problem. Extensive experiments are conducted to validate our proposed MILP and swarm intelligence-based task scheduling algorithms. Simulation results show that our scheme outperforms two state-of-the-art methods in improving system quality of security and guaranteeing schedule feasibility.

In cloud-edge computing paradigms, the integration of edge servers and task offloading mechanisms has posed new challenges to developing task scheduling strategies. This paper proposes an efficient convergent firefly algorithm (ECFA) for scheduling security-critical tasks onto edge servers and the cloud datacenter. The proposed ECFA uses a probability-based mapping operator to convert an individual firefly into a scheduling solution, in order to associate the firefly space with the solution space. Distinct from the standard FA, ECFA employs a low-complexity position update strategy to enhance computational efficiency in solution exploration. In addition, we provide a rigorous theoretical analysis to justify that ECFA owns the capability of converging to the global best individual in the firefly space. Furthermore, we introduce the concept of boundary traps for analyzing firefly movement trajectories, and investigate whether ECFA would fall into boundary traps during the evolutionary procedure under different parameter settings. We create various testing instances to evaluate the performance of ECFA in solving the cloud-edge scheduling problem, demonstrating its superiority over FA-based and other competing metaheuristics. Evaluation results also validate that the parameter range derived from the theoretical analysis can prevent our algorithm from falling into boundary traps.

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.

With the recent advances in mobile sensing, computing, and communication technologies, mobile cyber–physical systems (MCPSs) become a promising networking paradigm that provides mobile users with various applications and services from cyber space to the physical world. One of the main challenges in the MCPSs is to defend security threats launched by adversaries. Exploiting security services to defend threats is energy consuming, whereas the energy of mobile devices is generally limited since most of mobile devices are battery powered. This necessitates the need to design new methodologies to tackle the tradeoff between security and energy of MCPSs. To this end, this article aims to maximize MCPSs’ security under the constraints of energy and deadline. In this article, we first formulate the MCPS security maximization problem as a mixed-integer nonlinear programming (MINLP) problem and then transform it into a mixed-integer linear programming (MILP) problem without performance degradation. To solve the transformed MILP problem efficiently, we propose a decomposed algorithm to derive the optimum task scheduling solution instead of using MILP solvers that may be very time consuming for MCPSs of large granularity. The derived task scheduling solution decides the assignment, operating frequency, execution order, as well as security service selection for all tasks. We implement a series of simulation-based experiments to validate the proposed decomposed task scheduling scheme. Simulation results demonstrate that the proposed scheme increases system security level by 20.38% and 65.11% on average as compared to a state-of-art approach and a baseline method.

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.

Maximizing profit is a key goal for cloud service providers in the modern cloud business market. Service revenue and business cost are two major factors in determining profit and highly depend on multiserver configuration. Understanding the relationship between multiserver configuration and profit is important to service providers. Although existing articles have explored this issue, few of them consider deadline miss rate and soft error reliability of cloud services in multiserver configuration for profit maximization. Since deadline misses violate cloud services’ real-time requirements and soft error prevents successful processing of cloud services, it is necessary to consider the impact of deadline miss rate and soft error reliability on service providers’ profits when configuring the multiserver. This article introduces a deadline miss rate and soft error reliability aware multiserver configuration scheme for maximizing cloud service providers’ profit. Specifically, we derive the deadline miss rate considering the heterogeneity of cloud service requests, and propose an analytical method to compute the soft error reliability of multiserver systems. Based on the new deadline miss rate and soft error reliability models, we formulate the multiserver configuration optimization problem and introduce an augmented Lagrange multiplier-based iterative method to find the optimal multiserver configuration. Extensive experiments evaluate the efficacy of the proposed multiserver configuration approach. Compared with the two state-of-the-art methods, the profit gained by our scheme can be up to 11.92% higher.

Heterogeneous multiprocessor system-on-chips (MPSoCs) are suitable platforms for real-time embedded applications that require powerful parallel processing capability as well as low power consumption. For such applications, soft-error reliability (SER) due to transient faults and lifetime reliability (LTR) due to permanent faults are both key concerns. There have been several efforts in the literature oriented toward related reliability problems. However, most existing techniques only concentrate on improving one of the two reliability metrics, which are not suitable for embedded systems deployed in critical applications in need of a long lifetime as well as a reliable execution. This article develops a novel heterogeneous earliest-finish-time (HEFT)-based algorithm to maximize SER and LTR simultaneously under the real-time constraint for dependent tasks executing on heterogeneous MPSoC systems. More specifically, a new deadline-constrained reliability-aware HEFT algorithm, namely DRHEFT, is proposed, which seeks for the best SER–LTR tradeoff solutions through using fuzzy dominance to evaluate the relative fitness values of candidate solutions. The extensive experiments on real-life benchmarks as well as synthetic applications demonstrate that DRHEFT is capable of achieving better SER–LTR tradeoff solutions with higher hypervolume and less computation cost when compared with the state-of-the-art approaches.

Heterogeneous MPSoCs consisting of integrated CPUs and GPUs are suitable platforms for embedded applications running on handheld devices such as smart phones. As the handheld devices are mostly powered by battery, the integrated CPU and GPU MPSoC is usually designed with an emphasis on low-power rather than performance. In this paper, we are interested in exploring a power-efficient layer mapping of convolution neural networks (CNNs) deployed on integrated CPU and GPU platforms. Specifically, we investigate the impact of layer mapping of YoloV3-Tiny (i.e., a widely-used CNN in both industry and academia) on system power consumption through numerous experiments on NVIDIA board Jetson TX2. The experimental results indicate that 1) almost all of the convolution layers are not suitable for mapping to CPU, 2) the pooling layer can be mapped to CPU for reducing power consumption, but the mapping may lead to a decrease in inference speed when the layer's output tensor size is large, 3) the detection layer can be mapped to CPU as long as its floating-point operation scale is not too large, and 4) the channel and upsampling layers are both suitable for mapping to CPU. These observations obtained in this study can be further utilized to guide the design of power-efficient layer mapping strategies for integrated CPU and GPU platforms.

This special issue aims to provide a platform for the researchers, academia, and industry to present their novel solutions, applications, tools, software, hardware, and algorithms designed for addressing various sustainability challenges in CPS.The response from the CPS community was enthusiastic: the special issue received 34 manuscripts submitted by the authors from China, United States, India, Korea, Lebanon, and so on.According to the Journal of Software: Practice and Experience (SPE) review standards, this special issue accepted 14 high-quality research articles that cover a wide range of topics.These articles provide the software and hardware co-design solutions to improve dependability, energy efficiency, quality of service (QoS) of CPS, and also to introduce the methodologies for specific CPS applications.

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.

Cyber-physical cloud systems (CPCS) are integrations of cyber-physical systems (CPS) and cloud computing infrastructures. Integrating CPS into cloud computing infrastructures could improve the performance in many aspects. However, new reliability and security challenges are also introduced. This fact highlights the need to develop novel methodologies to tackle these challenges in CPCS. To this end, this article is oriented toward enhancing the soft-error reliability of real-time workflows on CPCS while satisfying the lifetime reliability, security, and real-time constraints. In this article, we propose a dependable algorithm for scheduling workflow applications on CPCS. The proposed algorithm uses slack to recover failed tasks and allows all tasks to share the available slack in the system. To improve soft-error reliability, the algorithm first determines the priority of tasks, then assigns the maximum frequency to each task, and finally assigns the recoveries to tasks dynamically. Slack also can be used to utilize security services for satisfying system security requirements. The lifetime reliability constraint is met by dynamically scaling down the operating frequency of low-priority tasks. Extensive experiments on real-world workflow benchmarks demonstrate that the proposed scheme reduces the probability of failure by up to $52.1\%$ and improves the scheduling feasibility by up to $83.5\%$ compared to a number of representative approaches.

End-to-end latency analysis is one of the key problems in the automotive embedded system design. In this paper, we propose an efficient worst-case end-to-end latency analysis method for data chains of periodic real-time tasks executed on multiprocessors under a partitioned fixed-priority preemptive scheduling policy. The key idea of this research is to improve the analysis efficiency by transforming the problem of bounding the worst-case latency of the data chain to a problem of bounding the releasing interval of data propagation instances for each pair of consecutive tasks in the chain. In particular, we derive an upper bound on the releasing interval of successive data propagation instances to yield the desired data chain latency bound by a simple accumulation. Based on the above idea, we present an efficient latency upper bound analysis algorithm with polynomial time complexity. Experiments with randomly generated task sets based on a generic automotive benchmark show that our proposed approach can obtain a relatively tighter data chain latency upper bound with lower computational cost.

Neural networks (NN) have gained great success in visual object recognition and natural language processing, but this kind of data-intensive applications requires huge data movements between computing units and memory. Emerging resistive random-access memory (RRAM) computing systems have demonstrated great potential in avoiding the huge data movements by performing matrix-vector-multiplications in memory. However, the nonvolatility of the RRAM devices may lead to potential stealing of the NN weights stored in crossbars and the adversary could extract the NN models from the stolen weights. This paper proposes an effective security enhancing method for RRAM computing systems to thwart this sort of piracy attack. We first analyze the theft methods of the NN weights. Then we propose an efficient security enhancing technique based on obfuscating the row connections between positive crossbars and their pairing negative crossbars. Two heuristic techniques are also presented to optimize the hardware overhead of the obfuscation module. Compared with existing NN security work, our method eliminates the additional RRAM writing operations used for encryption/decryption, without shortening the lifetime of RRAM computing systems. The experiment results show that the proposed methods ensure the trial times of brute-force attack are more than (16!)17and the classification accuracy of the incorrectly extracted NN models is less than 20%, with minimal area overhead.

To ensure data security and privacy during big data publishing, it is challenging to design a security and privacy protection scheme for the big data environment with a large scale of users. At the same time, due to the users' dynamically joining and exiting, it is also very important to design a user's dynamic update mechanism. To address such challenges, we design a novel scalable and updatable attribute-based privacy protection scheme (SUAPP) for big data publishing. The proposed scheme can realize users' hierarchical management, which can reduce the overhead on key generation and management caused by the large scale of data users in the big data center (BDC). We set a user group for each attribute, then adapt the Chinese remaining theorem to dynamically assist the big data center to generate and update group keys for the attribute users group. Analyses and experiments show that while ensuring the privacy protection of big data publishing, our scheme also has low communication and computation overhead and higher efficiency compared with two state peer schemes.

Nowadays, computers, as well as smart devices, are connected through communication networks making them more vulnerable to attacks. Honeypots are proposed as deception tools but usually used as part of a proactive defense strategy. Hence, this article demonstrates how honeypots data can be analyzed in an active defense strategy. Furthermore, anomaly detection based on unsupervised machine learning techniques allows to build autonomous systems and to detect unknown anomalies without the need for prior knowledge. However, the unsupervised techniques applied for honeypots data analysis do not value the advantages of these tools’ data, particularly the high probability that they include a large number of previously unseen anomalies with unexpected and diverse patterns. Therefore, in the present work, the aim is to improve the unsupervised anomaly detection in honeypots data by varying the data feature subset and the parameterization of the anomaly detection algorithm. To this purpose, an outlier ensemble with LOF (Local Outlier Factor) as a base algorithm is proposed. The ensemble outperforms existing solutions as depicted in the experiments where a detection rate higher than 92% is achieved.

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.

Heterogeneous multiprocessor systems on a chips (MPSoCs) consisting of cores with different performance/power characteristics are widely used in many real-time embedded systems, where both soft-error reliability and lifetime reliability are key concerns. Although existing efforts have investigated related problems, they either focus on one of the two reliability concerns or propose time-consuming scheduling algorithms that cannot adequately address runtime workload and environmental variations. This paper introduces an online framework which is adaptive to runtime variations and maximizes soft-error reliability while satisfying the lifetime reliability constraint for soft real-time systems executing on MPSoCs that are composed of high-performance cores and low-power (LP) cores. Based on each core's executing frequency and utilization, the framework performs workload migration between high-performance cores and LP cores to reduce power consumption and improve soft-error reliability. Experimental results based on different hardware platforms show that the proposed approach reduces the probability of failures due to soft errors by at least 17% and 50% on average compared to a number of representative existing approaches that satisfy the same lifetime reliability constraints.

Multiprocessor systems on a chip consisting of integrated CPUs and GPUs are suitable platforms for real-time embedded applications requiring massively parallel processing. For such applications, lifetime reliability due to permanent faults and soft-error reliability due to transient faults are major concerns. Detailed execution profiling has revealed that a CUDA task's CPU execution time significantly increases if the task executes on a different core than the operating system (OS). Based on this observation, an extended task model is introduced to consider the execution time dependencies among tasks and the OS. A hybrid framework is proposed to improve soft-error reliability while satisfying a lifetime reliability constraint for soft real-time systems executing on integrated CPU and GPU platforms. This framework: 1) reduces the total utilization of cores and improves soft-error reliability via off-line task mapping; 2) achieves a higher lifetime reliability through task migration at run time; and 3) improves soft-error reliability by dynamically scaling frequencies of CPU and GPU cores. The experimental results show that the proposed framework leads to a system that can execute without soft errors for at least 4 days (4 times) and 6 days (6 times) longer, on average, than existing approaches.

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.

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.

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.

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.

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.

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.

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.

Online portfolio selection has attracted increasing attention from data mining and machine learning communities in recent years. An important theory in financial markets is mean reversion, which plays a critical role in some state-of-the-art portfolio selection strategies. Although existing mean reversion strategies have been shown to achieve good empirical performance on certain datasets, they seldom carefully deal with noise and outliers in the data, leading to suboptimal portfolios, and consequently yielding poor performance in practice. In this paper, we propose to exploit the reversion phenomenon by using robust$L_1$-median estimators, and design a novel online portfolio selection strategy named “Robust Median Reversion” (RMR), which constructs optimal portfolios based on the improved reversion estimator. We examine the performance of the proposed algorithms on various real markets with extensive experiments. Empirical results show that RMR can overcome the drawbacks of existing mean reversion algorithms and achieve significantly better results. Finally, RMR runs in linear time, and thus is suitable for large-scale real-time algorithmic trading applications.