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.

Hyperspectral target detection (HTD) can provide detailed information about the objects and materials within a scene and holds significant importance in remote sensing image analysis. Traditional HTD methods often suffer from low detection accuracy when applied to hyperspectral images (HSIs) with low spatial resolution, where the target only occupies a few pixels. This can be addressed by exploiting the higher spatial resolution of multispectral images (MSIs). In addition, many cloud-based HTD methods, which rely on the distributed processing capability of cloud computing to cope with large-scale datasets, may result in long transmission delays that cannot meet real-time requirements. This article suggests a cloud-edge collaborative HTD approach based on the fusion of remotely sensed HSIs and MSIs. We first introduce an HTD algorithm that employs low-rank matrix decomposition and hierarchical constraint energy minimization (hCEM) to fuse a low-resolution HSI (LR-HIS) and a high-resolution MSI (HR-MSI). Aiming at a continuous shooting scenario, we further present a cloud-edge implementation of the HTD algorithm through the collaboration of a cloud cluster and edge servers deployed close to data acquisition devices. The overall processing flow of remotely sensed data fusion in the cloud-edge environment is formulated as a flowshop scheduling-like optimization problem. We develop a co-optimization scheduling algorithm to explore the best resource allocation solutions to the formulated problem. Experimental results on both general-purpose and real-world datasets show that the newly proposed HTD algorithm leads to significant improvements in detection accuracy over traditional methods, and the cloud-edge collaborative approach further enhances computational efficiency.

Low-rank representation is a popularly used method in remote sensing and image processing systems. Existing low-rank representation-based algorithms often neglect the varying sensitivities of different pixels to the dictionary, which may lead to inaccurate detection. Also, the dense iterative computations involved in these algorithms could induce high computation overheads. This article proposes an efficient low-rank representation method for hyperspectral anomaly detection (HAD) on a central processing unit (CPU)-field programmable gate array (FPGA) hybrid computing platform. The proposed method starts with segmenting the hyperspectral image into superpixels based on the selected number of dictionary categories. The image is divided into center and edge parts using a sliding window along the segmentation boundaries. Then, we use a low-rank and sparse representation (LRASR) to process center pixels that are well-adapted to the dictionary, and a weighted low-rank and collaborative representation model to process edge pixels that are less adaptive to the dictionary. The aforementioned two representation models are integrated to obtain the final reconstructed image. Moreover, we propose the Nesterov acceleration method by incorporating an adaptive step size into the linearized alternating direction method with adaptive penalty (LADMAP). At the hardware level, the acceleration method is deployed on the CPU-FPGA platform to speed up the proposed HAD flow. Experimental results demonstrate that the proposed method outperforms most existing methods in detection accuracy and achieves significant improvement in computational efficiency.

Tensor decomposition based fusion methods for hyperspectral and multi-spectral images have recently demonstrated decent fusion performance in the context of remote sensing applications. Considering the huge volume of today’s remote sensing datasets and the involved heavy computation loads, traditional single-machine fusion methods can no longer serve the purpose due to limited storage, communication, and computation capacities. To this end, we propose a distributed and parallel method for Tucker decomposition model based on Apache Spark. Our primary focus is to (i) enhance the generation and update methods for modal matrices to eliminate the bottleneck of insufficient memory; (ii) redesign the initialization and updating procedures of core tensors to adapt to distributed computing and enhance computational efficiency; and (iii) implement group updates to accelerate the overall fusion flow while maintaining fusion accuracy. Experimental results demonstrate that our distributed and parallel fusion method achieves significant speedups over the original model with decent fusion performance.

By deploying computing resources and services on network edge, mobile edge computing (MEC) can effectively address the energy limitations of mobile devices (MDs) through the computation offloading mechanism. This paper studies the problem of joint optimization of computation offloading, power assignment, and resource allocation, with the objective of reducing the total energy consumption in an MEC environment. We decompose the joint optimization problem into two sub-problems and suggest a composite method that integrates an analytical approach into the decision-making procedure of a deep reinforcement learning (DRL)-based algorithm. We first use the actor network of the twin delayed deep deterministic gradient (TD3), which is an advanced DRL algorithm, to determine the computation offloading rate for each MD as well as resource allocation on the MEC server. To facilitate the training process of DRL, we provide a rigorous analysis to derive the closed-form solution to the optimal assignment of the MD’s operating frequency for local computation as well as transmission power for computation offloading. Distinct from other DRL-based offloading methods that solely rely on DRL techniques to determine the values of all decision variables, our composite method effectively enhances the DRL algorithm’s decision-making capability in producing high-quality solutions. The evaluation results on a multi-user MEC system justify the superiority of the proposed method over baseline algorithms in producing more energy-efficient offloading solutions.

The orchestration of a service function chain (SFC) is responsible for the coordination of multiple virtual network functions (VNFs) in a sequence and the deployment of VNF nodes and links onto physical resources, for the purpose of delivering high-quality network services. This paper studies a multi-domain SFC orchestration problem that aims at minimizing the cost of deploying SFCs across multiple domains while ensuring the overall reliability of SFC requests. We establish a centralized multi-domain framework to formulate the SFC orchestration problem into a combinatorial optimization problem and propose a firefly algorithm (FA)-based metaheuristic approach, named as FA_SFCO, to solve the formulated problem. The proposed FA_SFCO uses a sequence of VNFs in the SFC to represent the orchestration solution, and converts all firefly individuals into valid VNF sequences to facilitate the evolutionary process of FA_SFCO. For each converted VNF sequence, FA_SFCO employs a series of operations, including backup instance allocation, SFC partitioning, and intra-domain mapping, to determine the deployment of all VNFs in the sequence, such that the fitness value of the orchestration in terms of the total deployment cost can be evaluated. Evaluation results on a multi-domain substrate network justify that FA_SFCO outperforms competing algorithms with a significant reduction in deployment cost while meeting the reliability requirement.

Mobile crowdsourcing is a new computing paradigm that enables outsourcing computation tasks to mobile crowd nodes by means of offloading the tasks from the user to a mobile edge computing (MEC) server. This article studies the problem of scheduling security-critical tasks of crowdsourcing applications in a multiserver MEC environment. We formulate this scheduling problem as an integer program and propose a family of convergent grey wolf optimizer (CGWO) metaheuristic algorithms to seek for the best scheduling solutions. Our proposed CGWO uses a task permutation to represent a candidate solution to the formulated scheduling problem, and employs a probability-based mapping scheme to map each search agent in grey wolf optimizer (GWO) onto a valid task permutation. We introduce a new position update strategy for generating the next generation of grey wolf population after each round of search. With this strategy, we prove our proposed CGWO guarantees its convergence to the global best solution. More importantly, we provide a thorough analysis on the movement trajectories of grey wolves during the evolutionary procedure, in order to determine appropriate parameter values such that CGWO would not be trapped in local optima. Experimental results justify the superiority of CGWO metaheuristics over the standard GWO in solving the crowdsourcing task scheduling problem.

When designing task scheduling algorithms in mobile edge computing (MEC), the mobile device (MD)’s mobility becomes an important concern, since the change in MD’s location would affect the data transmission rate, leading to fluctuations in task transmission duration and completion time. In this article, we study a mobility-aware task off-and-downloading scheduling problem in MEC, considering both the communication delay and energy consumption caused by the data offloading and the result downloading. We first formulate a mathematical optimization model of the studied problem and prove its NP-hardness. To explore high-quality task scheduling decisions, we propose a swarm intelligence algorithm-based evolutionary computation (START) framework. The main technical innovations of START include a solution representation of off-and-downloading sequence, an exponential probability model-based mapping operator, and a task dispatching heuristic. Specifically, the solution representation makes START applicable to a wide range of swarm intelligence algorithms. The mapping operator establishes the link between individual space and solution space, in which the critical parameter is determined by a rigorous theoretical analysis. The task dispatching strategy is the only component of the START framework that is relevant to the particular problem, providing the extensibility of applying START to solving other problems. In experiments, we create a real-world MD trajectory dataset MDT-NJUST, and integrate several representative swarm intelligence algorithms to justify the performance of START in solving the scheduling problem. Experimental results also verify the conclusion drawn from the theoretical analysis on critical parameter determination.

Constrained by the performance of edge devices and real time (RT) processing technology, the existing hyperspectral target detection algorithms often struggle to rapidly distinguish targets from complex background pixels during real-time detection. To address this issue, this article proposes a new real-time cloud-edge selective background energy constrained (CE-SBEC) hyperspectral target detection algorithm. This algorithm aims to obtain detection results in real-time after capturing new data. Moreover, it conducts in-depth analysis based on existing detection results and updates the algorithm’s internal data to enhance its capabilities in terms of global background annihilation (GBA) and complex background suppression (CBS). Consequently, it improves the accuracy of subsequent real-time detection results. To enhance the resource utilization, this article deploys various task nodes of the algorithm separately on both the cloud and the edge, enabling collaborative execution of the CE-SBEC algorithm. In our context, edge devices are airborne equipment designed for the rapid acquisition and processing of data at the site of data collection, while cloud computing devices refer to high-performance computing clusters situated at a significant distance from the data collection site. Experimental results demonstrate that compared with existing detection algorithms, our newly proposed method achieves more accurate detection results while ensuring real-time performance.

Hyperspectral anomaly detection (AD), as a frontier research topic in the field of remotely sensed data processing, aims to identify targets of interest from complex and vast images. Existing AD methods typically involve complex models and many parameters, posing challenges in meeting the requirements of computational efficiency in hyperspectral AD. To address this issue, this article presents an AD acceleration algorithm based on the multivariate Gaussian model as well as its field programmable gate array (FPGA) implementation. By exploiting the parallel processing capabilities of FPGA, we introduce an innovative spectral dimensionality reduction method in which the data processing flow can be accomplished in a distributed manner. Then, we employ an improved linear rotation strategy based on correlation coefficients to accelerate the convergence rate of the proposed AD algorithm. The rotation of Gaussianization in the improved strategy is independent of eigenvalue decomposition, thereby substantially reducing the computational complexity involved during the rotation procedure. Furthermore, we apply a pipeline parallel mechanism to facilitate the FPGA implementation of the AD algorithm and to significantly enhance the computational efficiency. Experimental results on an embedded FPGA platform demonstrate that the FPGA implementation of the hyperspectral AD algorithm proposed in this article achieves a significant acceleration rate with guaranteed high detection accuracy.

Hyperspectral image (HSI) super-resolution, which aims at improving the spatial quality of HSIs by fusing a low spatial resolution HSI (LR-HSI) with a high spatial resolution multispectral image (HR-MSI), has drawn significant attention. Numerous LR-HSI and HR-MSI (HSI-MSI) fusion algorithms have emerged in recent times, yet they suffer from a lack of generality and integration, which hampers their usability for non-expert users. Moreover, these algorithms encounter significant challenges due to the exponential increase in remote sensing data volume. In this study, we propose a unified cloud-based framework for HSI-MSI fusion based on the general distributed alternating direction method of multipliers that incorporates nonlocal principles and tensor decomposition. The framework not only provides end-users with visualization modeling capabilities equipped with standard and comprehensive components, but also enhances the parallel processing capabilities of cloud computing. We employ a new proposed nonlocal adaptive low-rank coupled tensor canonical polyadic (CP) decomposition algorithm as a case study to evaluate the performance of this framework. Specifically, we establish the LR-HSIs and HR-MSIs relationship using order-4 coupled tensor CP decomposition and suggest an adaptive CP rank estimation method for achieving better super-resolution results. Experimental results on publicly available datasets demonstrate that the proposed parallel distributed optimization algorithm can achieve significant speedup with guaranteed accuracy. The proposed framework enables convenient and efficient processing of large-scale remote sensing data, effectively addressing the challenges associated with handling large data volumes. The source code of our method is released and available online at https://github.com/ZpWaitingForSunshine/DNAC4TCP/.

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.

The new intermittent computing paradigm allows for intermittent operation of energy-harvesting devices, posing new challenges to edge intelligence in delivering high-quality computing services. This paper aims at the joint optimization of model partitioning and resource allocation for reducing the latency of deep neural network (DNN) applications on an edge computing system with intermittently operating devices. We establish a rigorous optimization model for the joint optimization problem that takes into account the heterogeneity and intermittent operation of end devices. Tailed for the inference procedure of DNN applications, we develop a family of partitioning rules for decomposing the DNN structure to facilitate computation offloading. We propose a model partitioning and resource allocation algorithm to determine the optimized assignment of computing resources for the DNN tasks offloaded from multiple devices onto the edge server. The proposed algorithm first utilizes the partitioning rules to obtain a preliminary decision on model partitioning, and introduces a greedy-based strategy to determine the final decision on the partitioning points of DNN structures as well as the amount of computing resources assigned for task execution. Simulation results on an edge system with heterogeneous devices, including Raspberry Pi 3B+, Raspberry Pi 4B, and Jetson Xavier NX, demonstrating that the proposed algorithm outperforms baseline methods with shorter latency when executing DNN inference tasks.

In multi-datacenter video forwarding environments, monetary cost and forwarding delay are both important concerns. This paper studies the problem of scheduling end user requests onto the media servers of multiple video datacenters (VDCs), with the objective of joint optimization of cost and delay. The studied scheduling problem is formulated as a multi-objective optimization problem (MOP) with a set of binary decision variables. We introduce a new metric of efficient fuzzy dominance (EFD) to evaluate the quality of MOP solutions with low computational complexity. With this new EFD metric, we propose a EFD-based particle swarm optimization (EFDPSO) algorithm to seek for the optimized scheduling solutions. EFDPSO employs a position-based mapping mechanism to convert each particle into a scheduling solution, represented by a request sequence, and develops a shortest path-based strategy to schedule the requests in the sequence such that cost and delay can be calculated. By using the EFD metric to determine the dominance among all the solutions mapped from the particles, EFDPSO identify a set of non-dominated MOP solutions and relies on PSO’s update rule to iteratively improve the non-dominated solution set. We perform extensive experiments to verify that the EFD metric can substantially reduce the computation time in solution evaluation and the proposed EFDPSO leads to higher quality of MOP solutions compared with baseline metaheuristic algorithms.

Hyperspectral image super-resolution aims to fuse a low spatial resolution hyperspectral image (LR-HSI) and a high spatial resolution multispectral image (HR-MSI) to obtain a high-resolution hyperspectral image (HR-HSI). Tensor-based methods have demonstrated their outstanding ability in constructing the relationship between the LR-HSI and the HR-MSI. This paper introduces a nonlocal hierarchical Tucker decomposition (HTD) model for hyperspectral and multispectral image (HSI-MSI) fusion. First, similar nonlocal patch tensors are clustered according to their similarity in the HR-MSI. Next, the spatial/spectral relationship between the LR-HSI and the HR-MSI is extracted through HTD. The alternating direction method of multipliers (ADMM) is employed to solve the proposed model. Furthermore, to overcome the high computational complexity of the model solver, we propose an efficient distributed and parallel method to accelerate the fusion process. Experimental results demonstrate that the proposed method not only substantially outperforms state-of-the-art HSI-MSI fusion methods, but also achieves a significant acceleration rate.

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.

Combining tensor decomposition and image nonlocal information for remote sensing image fusion method (NCTCP) can effectively preserve the spatial structure of the image, and can obtain good image fusion results, accordingly. However, the NCTCP that is a serial algorithm cannot handle massive remote sensing images due to the computing resources limitation of a single computer. To address this issue, we propose a distributed parallel nonlocal tensor CP decomposition optimization algorithm (DP_NCTCP) based on the Spark platform. The alternating direction method of multipliers(ADMM) in NCTCP is divided into two distributed computing subtasks that can be executed on Spark in parallel to improve the efficiency. Compared with NCTCP, the DP_NCTCP achieves high speedups without the degradation of fusion quality measures accuracy by fusing the real hyperspectral images.

Hyperspectral computational imaging (HCI) is to reconstruct hyperspectral images (HSIs) based on the compressed signals collected by remote sensing and imaging systems. Collaborative Tucker3 tensor decomposition is beneficial for HCI models in reconstructing high-fidelity HSIs. However, the everincreasing amount of compressed data leads to heavy computation burden for tensor decomposition-based HCI models, which may exceed the computing capacity of a single machine. For this reason, this paper proposes a Spark-based distributed and parallel HCI implementation via collaborative Tucker3 tensor decomposition. The proposed implementation decomposes the processing flow of the HCI algorithm into several stages, each of which can be processed in parallel on Spark. In addition, we develop parallel strategies for improving the performance of the redundant computational procedure and data storage procedure, respectively. Experimental results demonstrate that the parallel algorithm not only achieves high accuracy but also improves the computational efficiency when processing large-scale HSI datasets.

Tracking moving objects in remote-sensing videos is becoming increasingly important in remote-sensing analysis. This letter presents a novel object tracking method for remote-sensing videos. We start with using the traditional robust principal component analysis (RPCA) model to extract the moving object from the background. To describe the continuity of moving objects in spatial and temporal directions, we incorporate a 3-D total variation (3DTV) regularization into the RPCA model. Considering that the background is not static and the captured videos will contain noise because of the instability of the sensing camera, our proposed method introduces a certain part of the function to model the noise and capture the changes in background. Experimental results on real videos provided by 2016 IEEE GRSS Data Fusion Contest and 2020 Hyperspectral Object Tracking Challenge demonstrate the advantages of the moving object-tracking method via 3-D TV.

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.

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.

This article gives a survey of state-of-the-art methods for processing remotely sensed big data and thoroughly investigates existing parallel implementations on diverse popular high-performance computing platforms. The pros/cons of these approaches are discussed in terms of capability, scalability, reliability, and ease of use. Among existing distributed computing platforms, cloud computing is currently the most promising solution to efficient and scalable processing of remotely sensed big data due to its advanced capabilities for high-performance and service-oriented computing. We further provide an in-depth analysis of state-of-the-art cloud implementations that seek for exploiting the parallelism of distributed processing of remotely sensed big data. In particular, we study a series of scheduling algorithms (GSs) aimed at distributing the computation load across multiple cloud computing resources in an optimized manner. We conduct a thorough review of different GSs and reveal the significance of employing scheduling strategies to fully exploit parallelism during the remotely sensed big data processing flow. We present a case study on large-scale remote sensing datasets to evaluate the parallel and distributed approaches and algorithms. Evaluation results demonstrate the advanced capabilities of cloud computing in processing remotely sensed big data and the improvements in computational efficiency obtained by employing scheduling strategies.

The large data volume and high algorithm complexity of hyperspectral image (HSI) problems have posed big challenges for efficient classification of massive HSI data repositories. Recently, cloud computing architectures have become more relevant to address the big computational challenges introduced in the HSI field. This article proposes an acceleration method for HSI classification that relies on scheduling metaheuristics to automatically and optimally distribute the workload of HSI applications across multiple computing resources on a cloud platform. By analyzing the procedure of a representative classification method, we first develop its distributed and parallel implementation based on the MapReduce mechanism on Apache Spark. The subtasks of the processing flow that can be processed in a distributed way are identified as divisible tasks. The optimal execution of this application on Spark is further formulated as a divisible scheduling framework that takes into account both task execution precedences and task divisibility when allocating the divisible and indivisible subtasks onto computing nodes. The formulated scheduling framework is an optimization procedure that searches for optimized task assignments and partition counts for divisible tasks. Two metaheuristic algorithms are developed to solve this divisible scheduling problem. The scheduling results provide an optimized solution to the automatic processing of HSI big data on clouds, improving the computational efficiency of HSI classification by exploring the parallelism during the parallel processing flow. Experimental results demonstrate that our scheduling-guided approach achieves remarkable speedups by facilitating the automatic processing of HSI classification on Spark, and is scalable to the increasing HSI data volume.

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.

Classification is one of the major research fields in hyperspectral imagery. Due to the fact that neighboring pixels are more likely to share the same label, it is practical to use spatial information in hyperspectral image to achieve higher accuracy. On the other hand, however, spatial information also leads to higher computational complexity. This paper proposes an efficient implementation of a spatial-spectral kernel sparse representation for hyperspectral image classification base on the multi-GPU platform. The proposed implementation takes advantage of the capability of compute-unified device architecture (CUDA), such as shared memory, streams and peer-to-peer (P2P) transfer of data. In addition, an improvement of performance can be achieved by calculation reorganization and bandwidth usage optimization. Experimental results demonstrate that the proposed method achieves an up to 56.81X speedup in computation time while guaranteeing the classification accuracy.

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.

Change Detection in Remote sensing image is, in essence, to detect the changes of ground features with regard to time from remote sensing perspective. It is usually realized by analyzing and processing multi-temporal high resolution images. Change Detection based on fully connected conditional random field not only improves the detection accuracy of remote sensing image, but also achieves better robustness. However, with the growth of high-resolution data volumes, this algorithm consumes a huge amount of time and computational resources, and therefore needs to be improved accordingly. Spark is an open-source distributed general- purpose cluster-computing framework. It has powerful memory computing and efficient task scheduling capabilities for complex iterative calculations. Based on Spark, this paper proposes a distributed and parallel method of change detection in remote sensing image based on Fully Connected Conditional Random Field that analyzes the data input form, and proposes a multi-temporal image reading strategy on cloud platforms. This method decomposes the algorithm flow, and performs distributed parallel processing on each stage and makes full use of the processing advantages of data locality to implement a reasonable intermediate data storage. Experimental results demonstrate that this parallel method achieves a promising speedup with high scalability, while guaranteeing remarkable detection accuracy.

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.

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.

The large amount of data produced by satellites and airborne remote sensing instruments has posed important challenges to efficient and scalable processing of remotely sensed data in the context of various applications. In this paper, we propose a new big data framework for processing massive amounts of remote sensing images on cloud computing platforms. In addition to taking advantage of the parallel processing abilities of cloud computing to cope with large-scale remote sensing data, this framework incorporates task scheduling strategy to further exploit the parallelism during the distributed processing stage. Using a computation- and data-intensive pan-sharpening method as a study case, the proposed approach starts by profiling a remote sensing application and characterizing it into a directed acyclic graph (DAG). With the obtained DAG representing the application, we further develop an optimization framework that incorporates the distributed computing mechanism and task scheduling strategy to minimize the total execution time. By determining an optimized solution of task partitioning and task assignments, high utilization of cloud computing resources and accordingly a significant speedup can be achieved for remote sensing data processing. Experimental results demonstrate that the proposed framework achieves promising results in terms of execution time as compared with the traditional (serial) processing approach. Our results also show that the proposed approach is scalable with regard to the increasing scale of remote sensing data.

Recently, Parallel Intrusion Detection (PID) becomes very popular and its procedure of the parallel processing is called a PID application (PIDA). This PIDA can be regarded as a Bag-of-Tasks (BoT) application, consisting of multiple tasks that can be processed in parallel. Given multiple PIDAs (i.e., BoT applications) to be handled, when the private cloud has insufficiently available resources to afford all tasks, some tasks have to be outsourced to public clouds with resource-used costs. The key challenge here is how to schedule tasks on hybrid clouds to minimize makespan given a limited budget. This problem can be formulated as an Integer Programming model, which is generally NP-Hard. Accordingly, in this paper, we construct an Iterated Local Search (ILS) algorithm, which employs an effective heuristic to obtain the initial task sequence and utilizes an insertion-neighbourhood-based local search method to explore better task sequences with lower makespans. A swap-based perturbation operator is adopted to avoid local optimum. With the objective of improving the proposal’s efficiency without loss of any effectiveness, to calculate task sequences’ objectives, we construct a Fast Task Assignment (FTA) method by integrating an existing Task Assignment (TA) method with an acceleration mechanism designed through theoretical analysis. Accordingly, the proposed ILS is named FILS. Experimental results show that FILS outperforms the existing best algorithm for the considered problem, considerably and significantly. More importantly, compared with TA, FTA achieves a 2.42x speedup, which verifies that the acceleration mechanism employed by FTA is able to remarkably improve the efficiency. Finally, impacts of key factors are also evaluated and analyzed, exhaustively.

Summary Users are willing to execute bag‐of‐task applications consisting of multiple tasks on clouds, since cloud resources are delivered in a pay‐as‐you‐go manner. Given multiple bag‐of‐task applications to be executed with user‐specified quality‐of‐service demands, a cloud provider has to outsource some tasks to public clouds when its private cloud has insufficient resources to afford all applications' tasks. The key issue is how to schedule tasks on hybrid clouds (environments consisting of a private cloud and multiple public clouds) for maximizing the cloud provider's profit while meeting the quality‐of‐service demands. To solve this problem, we propose an efficient particle swarm optimization algorithm (EPSO) and three hybrid ones (HEPSO1‐HEPSO3), in which task sequences are considered as solutions. A mapping operator (BBMO) is developed to map particles to solutions and a quick task dispatching method containing an acceleration method is designed to calculate solutions' objectives. Experimental results show that EPSO not only outperforms an existing PSO (the best algorithm for solving a problem that is a special case of ours) significantly but also achieves a 11.48x speedup. The HEPSO1 to HEPSO3 outperform EPSO. The BBMO outperforms the well‐known ranked‐order value rule and achieves a 5.47x speedup. The acceleration method in quick task dispatching brings a 2.69x speedup.

Due to continuous process scaling, process, voltage, and temperature (PVT) parameter variations have become one of the most problematic issues in circuit design. The resulting correlations among performance metrics lead to a significant parametric yield loss. Previous algorithms on parametric yield prediction are limited to predicting a single-parametric yield or performing balanced optimization for several single-parametric yields. Consequently, these methods fail to predict the multi-parametric yield that optimizes multiple performance metrics simultaneously, which may result in significant accuracy loss. In this paper we suggest an efficient multi-parametric yield prediction framework, in which multiple performance metrics are considered as simultaneous constraint conditions for parametric yield prediction, to maintain the correlations among metrics. First, the framework models the performance metrics in terms of PVT parameter variations by using the adaptive elastic net (AEN) method. Then the parametric yield for a single performance metric can be predicted through the computation of the cumulative distribution function (CDF) based on the multiplication theorem and the Markov chain Monte Carlo (MCMC) method. Finally, a copula-based parametric yield prediction procedure has been developed to solve the multi-parametric yield prediction problem, and to generate an accurate yield estimate. Experimental results demonstrate that the proposed multi-parametric yield prediction framework is able to provide the designer with either an accurate value for parametric yield under specific performance limits, or a multi-parametric yield surface under all ranges of performance limits.

With shrinking technology, the increase in variability of process, voltage, and temperature (PVT) parameters significantly impacts the yield analysis and optimization for chip designs. Previous yield estimation algorithms have been limited to predicting either timing or power yield. However, neglecting the correlation between power and delay will result in significant yield loss. Most of these approaches also suffer from high computational complexity and long runtime. We suggest a novel bi-objective optimization framework based on Chebyshev affine arithmetic (CAA) and the adaptive weighted sum (AWS) method. Both power and timing yield are set as objective functions in this framework. The two objectives are optimized simultaneously to maintain the correlation between them. The proposed method first predicts the guaranteed probability bounds for leakage and delay distributions under the assumption of arbitrary correlations. Then a power-delay bi-objective optimization model is formulated by computation of cumulative distribution function (CDF) bounds. Finally, the AWS method is applied for power-delay optimization to generate a well-distributed set of Pareto-optimal solutions. Experimental results on ISCAS benchmark circuits show that the proposed bi-objective framework is capable of providing sufficient trade-off information between power and timing yield.