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.

Device-to-device (D2D)-enabled mobile edge computing (MEC) is a promising communication network in which computation tasks on mobile devices (MDs) can not only be processed locally but also can be scheduled to the MEC server or nearby MDs via D2D links in order to improve computational and energy efficiency. Due to the explosive growth of information and consequent large-scale data, the conflict between reducing task response time and the limited resources of MEC server and MDs has become an essential bottleneck. In this paper, we consider a D2D-enabled MEC system and study a task scheduling problem for minimizing the flowtime of all computation tasks under the energy constraints of MDs. To solve this problem, we develop a family of metaheuristic algorithms based upon our proposed framework of improved bat algorithm (IBA), which incorporates multiple best bat individuals. Also, we introduce a new linear updating scheme to update the bat positions and facilitate the evolution process of the metaheuristics. More importantly, we provide a rigorous theoretical analysis to prove the convergence of IBA by using this new updating scheme. Furthermore, we employ an improved initial solution generation strategy for enhancing the effectiveness of IBA. Numerical experiments demonstrate that the proposed algorithms can achieve high-quality scheduling solutions, in terms of shorter flowtimes, more effectively and more efficiently over baseline algorithms.

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.

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.

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 target detection (HTD) aims to detect fine targets in hyperspectral images (HSIs). The traditional HTD method in low-resolution hyperspectral image (LR-HSI) is incapable of detecting small targets, clearly and precisely. Accordingly, in this paper, we propose a hyperspectral and multispectral image fusion target detection method based on cloud-edge collaboration. In this method, LR-HSI is first employed for coarse detection with the output of some suspicious target areas. Afterwards, the hyperspectral images and multispectral images (HSI-MSI) fusion is performed on these areas for precise target detection. In order to ensure the efficiency of HTD, we intend to accelerate our method in parallel based on the cloud-edge collaborative architecture. Furthermore, we establish an optimization model and design a greedy strategy to achieve the optimal deployment for minimizing the shortest runtime on the cloud-edge collaborative architecture. The experimental results demonstrate that our proposed method can significantly improve the computational efficiency while ensuring the accuracy.

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.

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.

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.

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.

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.

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.

Anomaly detection in hyperspectral images aims to separate the abnormal pixels from the background, and becomes an important application of hyperspectral data processing. Anomaly detection based on Low-Rank and Sparse Representation (LRASR) can detect abnormal pixels accurately. However, with the growth of the hyperspectral data volumes, this algorithm consumes a huge amount of time and computational resources, and needs to be improved accordingly. Spark is a distributed big data processing platform, and is applicable for complex iterative calculations, because of its powerful in-memory computation and efficient task scheduling. Based on Spark, this paper proposes a distributed and parallel LRASR (called DP-LRASR), which first segments hyperspectral images using narrow dependency of resilient distributed datasets, and afterwards, a parallel clustering algorithm is employed to improve the efficiency, remarkably. Experimental results demonstrate that DP-LRASR achieves a good speedup with high scalability, in the premise of remarkable detection accuracy.

As the volume and value of hyperspectral remote sensing images increasing, a common hyperspectral imagery repository is urgently required. This paper presents a novel distributed content retrieval method for hyperspectral imagery repository based on cloud computing platform. The proposed method uses a software-as-a-service (SaaS) mode, which provides users with services such as hyperspectral image management, storage and retrieval through a web interface. In order to accelerate the acquisition of the spectral feature information needed in the retrieval process, we optimize the procedure of endmember extraction and the abundance estimation in a distributed way on Spark platform. The generated spectral features meta-data are stored in the MySQL database for imagery retrieval. Finally, we introduce the parallel implementation of distributed retrieval of hyperspectral images based on N-FINDR in details, and evaluate the effectiveness and stability of our proposed method through experiments on real datasets.

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.